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Yan Y, He X, Xu Y, Peng J, Zhao F, Shao Y. Comparison between morphometry and radiomics: detecting normal brain aging based on grey matter. Front Aging Neurosci 2024; 16:1366780. [PMID: 38685908 PMCID: PMC11056505 DOI: 10.3389/fnagi.2024.1366780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 04/04/2024] [Indexed: 05/02/2024] Open
Abstract
Objective Voxel-based morphometry (VBM), surface-based morphometry (SBM), and radiomics are widely used in the field of neuroimage analysis, while it is still unclear that the performance comparison between traditional morphometry and emerging radiomics methods in diagnosing brain aging. In this study, we aimed to develop a VBM-SBM model and a radiomics model for brain aging based on cognitively normal (CN) individuals and compare their performance to explore both methods' strengths, weaknesses, and relationships. Methods 967 CN participants were included in this study. Subjects were classified into the middle-aged group (n = 302) and the old-aged group (n = 665) according to the age of 66. The data of 360 subjects from the Alzheimer's Disease Neuroimaging Initiative were used for training and internal test of the VBM-SBM and radiomics models, and the data of 607 subjects from the Australian Imaging, Biomarker and Lifestyle, the National Alzheimer's Coordinating Center, and the Parkinson's Progression Markers Initiative databases were used for the external tests. Logistics regression participated in the construction of both models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the two model performances. The DeLong test was used to compare the differences in AUCs between models. The Spearman correlation analysis was used to observe the correlations between age, VBM-SBM parameters, and radiomics features. Results The AUCs of the VBM-SBM model and radiomics model were 0.697 and 0.778 in the training set (p = 0.018), 0.640 and 0.789 in the internal test set (p = 0.007), 0.736 and 0.737 in the AIBL test set (p = 0.972), 0.746 and 0.838 in the NACC test set (p < 0.001), and 0.701 and 0.830 in the PPMI test set (p = 0.036). Weak correlations were observed between VBM-SBM parameters and radiomics features (p < 0.05). Conclusion The radiomics model achieved better performance than the VBM-SBM model. Radiomics provides a good option for researchers who prioritize performance and generalization, whereas VBM-SBM is more suitable for those who emphasize interpretability and clinical practice.
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Affiliation(s)
| | | | | | | | | | - Yuan Shao
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
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Negrón-Oyarzo I, Dib T, Chacana-Véliz L, López-Quilodrán N, Urrutia-Piñones J. Large-scale coupling of prefrontal activity patterns as a mechanism for cognitive control in health and disease: evidence from rodent models. Front Neural Circuits 2024; 18:1286111. [PMID: 38638163 PMCID: PMC11024307 DOI: 10.3389/fncir.2024.1286111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 03/11/2024] [Indexed: 04/20/2024] Open
Abstract
Cognitive control of behavior is crucial for well-being, as allows subject to adapt to changing environments in a goal-directed way. Changes in cognitive control of behavior is observed during cognitive decline in elderly and in pathological mental conditions. Therefore, the recovery of cognitive control may provide a reliable preventive and therapeutic strategy. However, its neural basis is not completely understood. Cognitive control is supported by the prefrontal cortex, structure that integrates relevant information for the appropriate organization of behavior. At neurophysiological level, it is suggested that cognitive control is supported by local and large-scale synchronization of oscillatory activity patterns and neural spiking activity between the prefrontal cortex and distributed neural networks. In this review, we focus mainly on rodent models approaching the neuronal origin of these prefrontal patterns, and the cognitive and behavioral relevance of its coordination with distributed brain systems. We also examine the relationship between cognitive control and neural activity patterns in the prefrontal cortex, and its role in normal cognitive decline and pathological mental conditions. Finally, based on these body of evidence, we propose a common mechanism that may underlie the impaired cognitive control of behavior.
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Affiliation(s)
- Ignacio Negrón-Oyarzo
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Tatiana Dib
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Lorena Chacana-Véliz
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Nélida López-Quilodrán
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Jocelyn Urrutia-Piñones
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
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3
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Marzi C, Giannelli M, Barucci A, Tessa C, Mascalchi M, Diciotti S. Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets. Sci Data 2024; 11:115. [PMID: 38263181 PMCID: PMC10805868 DOI: 10.1038/s41597-023-02421-7] [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: 12/06/2022] [Accepted: 07/27/2023] [Indexed: 01/25/2024] Open
Abstract
Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage by design.
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Affiliation(s)
- Chiara Marzi
- Department of Statistics, Computer Science and Applications "Giuseppe Parenti", University of Florence, 50134, Florence, Italy
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council (CNR), 50019, Sesto Fiorentino, Florence, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126, Pisa, Italy
| | - Andrea Barucci
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council (CNR), 50019, Sesto Fiorentino, Florence, Italy
| | - Carlo Tessa
- Radiology Unit Apuane e Lunigiana, Azienda USL Toscana Nord Ovest, 54100, Massa, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50139, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and netwoRk in Oncology (ISPRO), 50139, Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, University of Bologna, 47522, Cesena, Italy.
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, 40121, Bologna, Italy.
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Padulo C, Sestieri C, Punzi M, Picerni E, Chiacchiaretta P, Tullo MG, Granzotto A, Baldassarre A, Onofrj M, Ferretti A, Delli Pizzi S, Sensi SL. Atrophy of specific amygdala subfields in subjects converting to mild cognitive impairment. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2023; 9:e12436. [PMID: 38053753 PMCID: PMC10694338 DOI: 10.1002/trc2.12436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 10/09/2023] [Accepted: 10/23/2023] [Indexed: 12/07/2023]
Abstract
Introduction Accumulating evidence indicates that the amygdala exhibits early signs of Alzheimer's disease (AD) pathology. However, it is still unknown whether the atrophy of distinct subfields of the amygdala also participates in the transition from healthy cognition to mild cognitive impairment (MCI). Methods Our sample was derived from the AD Neuroimaging Initiative 3 and consisted of 97 cognitively healthy (HC) individuals, sorted into two groups based on their clinical follow-up: 75 who remained stable (s-HC) and 22 who converted to MCI within 48 months (c-HC). Anatomical magnetic resonance (MR) images were analyzed using a semi-automatic approach that combines probabilistic methods and a priori information from ex vivo MR images and histology to segment and obtain quantitative structural metrics for different amygdala subfields in each participant. Spearman's correlations were performed between MR measures and baseline and longitudinal neuropsychological measures. We also included anatomical measurements of the whole amygdala, the hippocampus, a key target of AD-related pathology, and the whole cortical thickness as a test of spatial specificity. Results Compared with s-HC individuals, c-HC subjects showed a reduced right amygdala volume, whereas no significant difference was observed for hippocampal volumes or changes in cortical thickness. In the amygdala subfields, we observed selected atrophy patterns in the basolateral nuclear complex, anterior amygdala area, and transitional area. Macro-structural alterations in these subfields correlated with variations of global indices of cognitive performance (measured at baseline and the 48-month follow-up), suggesting that amygdala changes shape the cognitive progression to MCI. Discussion Our results provide anatomical evidence for the early involvement of the amygdala in the preclinical stages of AD. Highlights Amygdala's atrophy marks elderly progression to mild cognitive impairment (MCI).Amygdala's was observed within the basolateral and amygdaloid complexes.Macro-structural alterations were associated with cognitive decline.No atrophy was found in the hippocampus and cortex.
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Affiliation(s)
- Caterina Padulo
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Department of HumanitiesUniversity of Naples Federico IINaplesItaly
| | - Carlo Sestieri
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)“G. d'Annunzio” University, Chieti‐PescaraChietiItaly
| | - Miriam Punzi
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Eleonora Picerni
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Piero Chiacchiaretta
- Department of Innovative Technologies in Medicine and Dentistry“G. d'Annunzio” University of Chieti‐Pescara, ChietiChietiItaly
- Advanced Computing CoreCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Maria Giulia Tullo
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Alberto Granzotto
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Marco Onofrj
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Antonio Ferretti
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Stefano Delli Pizzi
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Stefano L. Sensi
- Department of Neuroscience, Imaging, and Clinical SciencesUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)“G. d'Annunzio” University, Chieti‐PescaraChietiItaly
- Molecular Neurology UnitCenter for Advanced Studies and Technology (CAST)University “G. d'Annunzio” of Chieti‐PescaraChietiItaly
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Clifford KP, Miles AE, Prevot TD, Misquitta KA, Ellegood J, Lerch JP, Sibille E, Nikolova YS, Banasr M. Brain structure and working memory adaptations associated with maturation and aging in mice. Front Aging Neurosci 2023; 15:1195748. [PMID: 37484693 PMCID: PMC10359104 DOI: 10.3389/fnagi.2023.1195748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/13/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction As the population skews toward older age, elucidating mechanisms underlying human brain aging becomes imperative. Structural MRI has facilitated non-invasive investigation of lifespan brain morphology changes, yet this domain remains uncharacterized in rodents despite increasing use as models of disordered human brain aging. Methods Young (2m, n = 10), middle-age (10m, n = 10) and old (22m, n = 9) mice were utilized for maturational (young vs. middle-age) and aging-related (middle-age vs. old mice) comparisons. Regional brain volume was averaged across hemispheres and reduced to 32 brain regions. Pairwise group differences in regional volume were tested using general linear models, with total brain volume as a covariate. Sample-wide associations between regional brain volume and Y-maze performance were assessed using logistic regression, residualized for total brain volume. Both analyses corrected for multiple comparisons. Structural covariance networks were generated using the R package "igraph." Group differences in network centrality (degree), integration (mean distance), and segregation (transitivity, modularity) were tested across network densities (5-40%), using 5,000 (1,000 for degree) permutations with significance criteria of p < 0.05 at ≥5 consecutive density thresholds. Results Widespread significant maturational changes in volume occurred in 18 brain regions, including considerable loss in isocortex regions and increases in brainstem regions and white matter tracts. The aging-related comparison yielded 6 significant changes in brain volume, including further loss in isocortex regions and increases in white matter tracts. No significant volume changes were observed across either comparison for subcortical regions. Additionally, smaller volume of the anterior cingulate area (χ2 = 2.325, pBH = 0.044) and larger volume of the hippocampal formation (χ2 = -2.180, pBH = 0.044) were associated with poorer cognitive performance. Maturational network comparisons yielded significant degree changes in 9 regions, but no aging-related changes, aligning with network stabilization trends in humans. Maturational decline in modularity occurred (24-29% density), mirroring human trends of decreased segregation in young adulthood, while mean distance and transitivity remained stable. Conclusion/Implications These findings offer a foundational account of age effects on brain volume, structural brain networks, and working memory in mice, informing future work in facilitating translation between rodent models and human brain aging.
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Affiliation(s)
- Kevan P. Clifford
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Amy E. Miles
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Thomas D. Prevot
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Keith A. Misquitta
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Departments of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Jacob Ellegood
- Mouse Imaging Centre (MICe), Hospital for Sick Children, Toronto, ON, Canada
| | - Jason P. Lerch
- Mouse Imaging Centre (MICe), Hospital for Sick Children, Toronto, ON, Canada
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Etienne Sibille
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Departments of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Yuliya S. Nikolova
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Mounira Banasr
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Departments of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
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Chen W, Gou M, Wang L, Li N, Li W, Tong J, Zhou Y, Xie T, Yu T, Feng W, Li Y, Chen S, Tian B, Tan S, Wang Z, Pan S, Luo X, Zhang P, Huang J, Tian L, Li CSR, Tan Y. Inflammatory disequilibrium and lateral ventricular enlargement in treatment-resistant schizophrenia. Eur Neuropsychopharmacol 2023; 72:18-29. [PMID: 37058967 DOI: 10.1016/j.euroneuro.2023.03.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 04/16/2023]
Abstract
Treatment-resistant schizophrenia (TRS) patient respond poorly to antipsychotics. Inflammatory imbalance involving pro- and anti-inflammatory cytokines may play an important role in the mechanism of antipsychotic-medication response. This study aimed to investigate immune imbalance and how the latter relates to clinical manifestations in patients with TRS. The level of net inflammation was estimated by evaluating the immune-inflammatory response system and compensatory immune-regulatory reflex system (IRS/CIRS) in 52 patients with TRS, 47 with non-TRS, and 56 sex and age matched healthy controls. The immune biomarkers mainly included macrophagic M1, T helper, Th-1, Th-2, Th-17, and T regulatory cytokines and receptors. Plasma cytokine levels were measured using enzyme-linked immunosorbent assay. Psychopathology was assessed using the Positive and Negative Syndrome Scale (PANSS). Subcortical volumes were quantified using a 3-T Prisma Magnetic Resonance Imaging scanner. The results showed that (1) patients with TRS were characterized by activated pro-inflammatory cytokines and relatively insufficient anti-inflammatory cytokines, with an elevated IRS/CIRS ratio indicating a new homeostatic immune setpoint; (2) IRS/CIRS ratio was positively correlated with larger lateral ventricle volume and higher PANSS score in patients with TRS. Our findings highlighted the inflammatory disequilibrium as a potential pathophysiological process of TRS.
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Affiliation(s)
- Wenjin Chen
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Mengzhuang Gou
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Leilei Wang
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Na Li
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Wei Li
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Jinghui Tong
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Yanfang Zhou
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Ting Xie
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Ting Yu
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Wei Feng
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Yanli Li
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Song Chen
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Baopeng Tian
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Shuping Tan
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Zhiren Wang
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Shujuan Pan
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Xingguang Luo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Ping Zhang
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Junchao Huang
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China
| | - Li Tian
- Institute of Biomedicine and Translational Medicine, Department of Physiology, Faculty of Medicine, University of Tartu, Tartu, Estonia
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Yunlong Tan
- Peking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China.
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Zhou Y, Wei L, Gao S, Wang J, Hu Z. Characterization of diffusion magnetic resonance imaging revealing relationships between white matter disconnection and behavioral disturbances in mild cognitive impairment: a systematic review. Front Neurosci 2023; 17:1209378. [PMID: 37360170 PMCID: PMC10285107 DOI: 10.3389/fnins.2023.1209378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023] Open
Abstract
White matter disconnection is the primary cause of cognition and affection abnormality in mild cognitive impairment (MCI). Adequate understanding of behavioral disturbances, such as cognition and affection abnormality in MCI, can help to intervene and slow down the progression of Alzheimer's disease (AD) promptly. Diffusion MRI is a non-invasive and effective technique for studying white matter microstructure. This review searched the relevant papers published from 2010 to 2022. Sixty-nine studies using diffusion MRI for white matter disconnections associated with behavioral disturbances in MCI were screened. Fibers connected to the hippocampus and temporal lobe were associated with cognition decline in MCI. Fibers connected to the thalamus were associated with both cognition and affection abnormality. This review summarized the correspondence between white matter disconnections and behavioral disturbances such as cognition and affection, which provides a theoretical basis for the future diagnosis and treatment of AD.
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Affiliation(s)
- Yu Zhou
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Lan Wei
- Business School, The University of Sydney, Sydney, NSW, Australia
| | - Song Gao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
| | - Jun Wang
- School of Information Engineering, Henan University of Science and Technology, Luoyang, China
| | - Zhigang Hu
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
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Zhao J, Liu S, Ahmad S, Pew-Thian Y. MeshDeform: Surface Reconstruction of Subcortical Structures in Human Brain MRI. INFORMATION PROCESSING IN MEDICAL IMAGING : PROCEEDINGS OF THE ... CONFERENCE 2023; 13939:536-547. [PMID: 37915753 PMCID: PMC10617631 DOI: 10.1007/978-3-031-34048-2_41] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
Surface reconstruction of cortical and subcortical structures is crucial for brain morphological studies. Existing deep learning surface reconstruction methods, such as DeepCSR and Vox2Surf, learn an implicit field function for computing the isosurface, but do not consider mesh topology. In this paper, we propose a novel and efficient deep learning mesh deformation network, called MeshDeform, to reconstruct topologically correct surfaces of subcortical structures using brain MR images. MeshDeform combines features extracted from a U-Net encoder with mesh deformation blocks to predict surfaces of subcortical structures by deforming spherical mesh templates. MeshDeform is able to reconstruct in less than 10 seconds the surfaces of a left-right pair of subcortical structures with subvoxel accuracy. Reconstruction of all 17 subcortical structures takes less than one and a half minutes. By contrast, Vox2Surf takes about 20-30 minutes for all subcortical structures. Visual and quantitative evaluation on the Human Connectome Project (HCP) dataset demonstrate that MeshDeform generates accurate subcortical surfaces in limited time while preserving mesh topology.
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Affiliation(s)
- Junjie Zhao
- Department of Computer Science, University of North Carolina, Chapel Hill, USA
| | - Siyuan Liu
- College of Marine Engineering, Dalian Maritime University, Dalian, China
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, USA
| | - Yap Pew-Thian
- Department of Radiology, University of North Carolina, Chapel Hill, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, USA
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9
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Li G, Tong R, Zhang M, Gillen KM, Jiang W, Du Y, Wang Y, Li J. Age-dependent changes in brain iron deposition and volume in deep gray matter nuclei using quantitative susceptibility mapping. Neuroimage 2023; 269:119923. [PMID: 36739101 DOI: 10.1016/j.neuroimage.2023.119923] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/10/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Microstructural changes in deep gray matter (DGM) nuclei are related to physiological behavior, cognition, and memory. Therefore, it is critical to study age-dependent trajectories of biomarkers in DGM nuclei for understanding brain development and aging, as well as predicting cognitive or neurodegenerative diseases. OBJECTIVES We aimed to (1) characterize age-dependent trajectories of mean susceptibility, adjusted volume, and total iron content simultaneously in DGM nuclei using quantitative susceptibility mapping (QSM); (2) examine potential contributions of sex related effects to the different age-dependence trajectories of volume and iron deposition; and (3) evaluate the ability of brain age prediction by combining mean magnetic susceptibility and volume of DGM nuclei. METHODS Magnetic susceptibilities and volumetric values of DGM nuclei were obtained from 220 healthy participants (aged 10-70 years) scanned on a 3T MRI system. Regions of interest (ROIs) were drawn manually on the QSM images. Univariate regression analysis between age and each of the MRI measurements in a single ROI was performed. Pearson correlation coefficients were calculated between magnetic susceptibility and adjusted volume in a single ROI. The statistical significance of sex differences in age-dependent trajectories of magnetic susceptibilities and adjusted volumes were determined using one-way ANCOVA. Multiple regression analysis was used to evaluate the ability to estimate brain age using a combination of the mean susceptibilities and adjusted volumes in multiple DGM nuclei. RESULTS Mean susceptibility and total iron content increased linearly, quadratically, or exponentially with age in all six DGM nuclei. Negative linear correlation was observed between adjusted volume and age in the head of the caudate nucleus (CN; R2 = 0.196, p < 0.001). Quadratic relationships were found between adjusted volume and age in the putamen (PUT; R2 = 0.335, p < 0.001), globus pallidus (GP; R2 = 0.062, p = 0.001), and dentate nucleus (DN; R2 = 0.077, p < 0.001). Males had higher mean magnetic susceptibility than females in the PUT (p = 0.001), red nucleus (RN; p = 0.002), and substantia nigra (SN; p < 0.001). Adjusted volumes of the CN (p < 0.001), PUT (p = 0.030), GP (p = 0.007), SN (p = 0.021), and DN (p < 0.001) were higher in females than those in males throughout the entire age range (10-70 years old). The total iron content of females was higher than that of males in the CN (p < 0.001), but lower than that of males in the PUT (p = 0.014) and RN (p = 0.043) throughout the entire age range (10-70 years old). Multiple regression analyses revealed that the combination of the mean susceptibility value of the PUT, and the volumes of the CN and PUT had the strongest associations with brain age (R2 = 0.586). CONCLUSIONS QSM can be used to simultaneously investigate age- and sex- dependent changes in magnetic susceptibility and volume of DGM nuclei, thus enabling a comprehensive understanding of the developmental trajectories of iron accumulation and volume in DGM nuclei during brain development and aging.
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Affiliation(s)
- Gaiying Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062
| | - Rui Tong
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062
| | - Miao Zhang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062
| | - Kelly M Gillen
- Department of Radiology, Weill Medical College of Cornell University, 407 East 61st St., New York, New York, United States 10065
| | - Wenqing Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China 200030
| | - Yasong Du
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China 200030
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, 407 East 61st St., New York, New York, United States 10065
| | - Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062; Institute of Brain and Education Innovation, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062.
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10
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Gurlek Celik N, Tiryaki S. Changes in the volumes and asymmetry of subcortical structures in healthy individuals according to gender. Anat Sci Int 2023:10.1007/s12565-023-00714-w. [PMID: 36947348 DOI: 10.1007/s12565-023-00714-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/08/2023] [Indexed: 03/23/2023]
Abstract
In recent years, with the development of technology, three-dimensional software has entered our lives. Volumetric measurements made with Magnetic Resonance Imaging (MRI) are essential in the morphometry of the brain and subcortical structures. In this study, we aim to share the volume and asymmetry of the hippocampus, its sub-branches, and other subcortical structures and their interaction with age/sex using volBrain, a web-based automated software.1.5 T T1-weighted volumetric MRI, of 90 healthy individuals (51 females, 39 males) of both genders were included in our study. Pallidum, hippocampus, Cornu Ammonis1 (CA1), Cornu Ammonis2-3 (CA2-CA3), and Cornu Ammonis4-Dentate Gyrus (CA4-DG) measurements in females and males had a statistically higher mean in the right region (p < 0.05). In addition, females' hippocampus, CA1, CA2-CA3, and CA4-DG averages decreased more rapidly in the right region than in the left region. Subiculum measurement had a higher mean in the left region in both males and females (p < 0.05).The mean subiculum of males decreased more rapidly in the right region than in the left region. When the total values of the subcortical region in males and females were compared according to age categories, amygdala, pallidum, putamen, hippocampus, CA2-CA3, and subiculum values did not differ to gender in individuals aged 50 and over (p > 0.05). In individuals under 50 years old, the mean of females was statistically lower than the mean of males (p < 0.05).The Stratum radiatum (SR), Stratum lacunosum (SL), and Stratum molecuare (SM) asymmetry values of males in the examined subcortical regions had a higher mean than females (p = 0.039). In other regions, there was no statistically asymmetrical difference (p > 0.05). Studies evaluating the volumetric analysis and asymmetry of hippocampus subbranches and other subcortical structures in adults are very limited. As a result, the morphometry of the hippocampus subbranches and other subcortical structures was examined in detail. It was determined that the structures differed according to age, gender and body side.
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Affiliation(s)
- Nihal Gurlek Celik
- Department of Anatomy, Faculty of Medicine, Amasya University, 05100, Amasya, Turkey.
| | - Saban Tiryaki
- Department of Radiology, Faculty of Medicine, Kirsehir Ahi Evran University, 40100, Kirsehir, Turkey
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11
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Lazard DS, Doelling KB, Arnal LH. Plasticity After Hearing Rehabilitation in the Aging Brain. Trends Hear 2023; 27:23312165231156412. [PMID: 36794429 PMCID: PMC9936397 DOI: 10.1177/23312165231156412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
Abstract
Age-related hearing loss, presbycusis, is an unavoidable sensory degradation, often associated with the progressive decline of cognitive and social functions, and dementia. It is generally considered a natural consequence of the inner-ear deterioration. However, presbycusis arguably conflates a wide array of peripheral and central impairments. Although hearing rehabilitation maintains the integrity and activity of auditory networks and can prevent or revert maladaptive plasticity, the extent of such neural plastic changes in the aging brain is poorly appreciated. By reanalyzing a large-scale dataset of more than 2200 cochlear implant users (CI) and assessing the improvement in speech perception from 6 to 24 months of use, we show that, although rehabilitation improves speech understanding on average, age at implantation only minimally affects speech scores at 6 months but has a pejorative effect at 24 months post implantation. Furthermore, older subjects (>67 years old) were significantly more likely to degrade their performances after 2 years of CI use than the younger patients for each year increase in age. Secondary analysis reveals three possible plasticity trajectories after auditory rehabilitation to account for these disparities: Awakening, reversal of deafness-specific changes; Counteracting, stabilization of additional cognitive impairments; or Decline, independent pejorative processes that hearing rehabilitation cannot prevent. The role of complementary behavioral interventions needs to be considered to potentiate the (re)activation of auditory brain networks.
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Affiliation(s)
- Diane S. Lazard
- Institut Pasteur, Université Paris Cité, INSERM AU06, Institut de l’Audition, Paris, France,ENT department, Institut Arthur Vernes, Paris, France,Diane Lazard, Institut de l’Audition, Institut Pasteur, 63 rue de Charenton, 75012 Paris, France.
| | - Keith B. Doelling
- Institut Pasteur, Université Paris Cité, INSERM AU06, Institut de l’Audition, Paris, France
| | - Luc H. Arnal
- Institut Pasteur, Université Paris Cité, INSERM AU06, Institut de l’Audition, Paris, France
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12
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Choi EY, Tian L, Su JH, Radovan MT, Tourdias T, Tran TT, Trelle AN, Mormino E, Wagner AD, Rutt BK. Thalamic nuclei atrophy at high and heterogenous rates during cognitively unimpaired human aging. Neuroimage 2022; 262:119584. [PMID: 36007822 PMCID: PMC9787236 DOI: 10.1016/j.neuroimage.2022.119584] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 08/09/2022] [Accepted: 08/21/2022] [Indexed: 02/02/2023] Open
Abstract
The thalamus is a central integration structure in the brain, receiving and distributing information among the cerebral cortex, subcortical structures, and the peripheral nervous system. Prior studies clearly show that the thalamus atrophies in cognitively unimpaired aging. However, the thalamus is comprised of multiple nuclei involved in a wide range of functions, and the age-related atrophy of individual thalamic nuclei remains unknown. Using a recently developed automated method of identifying thalamic nuclei (3T or 7T MRI with white-matter-nulled MPRAGE contrast and THOMAS segmentation) and a cross-sectional design, we evaluated the age-related atrophy rate for 10 thalamic nuclei (AV, CM, VA, VLA, VLP, VPL, pulvinar, LGN, MGN, MD) and an epithalamic nucleus (habenula). We also used T1-weighted images with the FreeSurfer SAMSEG segmentation method to identify and measure age-related atrophy for 11 extra-thalamic structures (cerebral cortex, cerebral white matter, cerebellar cortex, cerebellar white matter, amygdala, hippocampus, caudate, putamen, nucleus accumbens, pallidum, and lateral ventricle). In 198 cognitively unimpaired participants with ages spanning 20-88 years, we found that the whole thalamus atrophied at a rate of 0.45% per year, and that thalamic nuclei had widely varying age-related atrophy rates, ranging from 0.06% to 1.18% per year. A functional grouping analysis revealed that the thalamic nuclei involved in cognitive (AV, MD; 0.53% atrophy per year), visual (LGN, pulvinar; 0.62% atrophy per year), and auditory/vestibular (MGN; 0.64% atrophy per year) functions atrophied at significantly higher rates than those involved in motor (VA, VLA, VLP, and CM; 0.37% atrophy per year) and somatosensory (VPL; 0.32% atrophy per year) functions. A proximity-to-CSF analysis showed that the group of thalamic nuclei situated immediately adjacent to CSF atrophied at a significantly greater atrophy rate (0.59% atrophy per year) than that of the group of nuclei located farther from CSF (0.36% atrophy per year), supporting a growing hypothesis that CSF-mediated factors contribute to neurodegeneration. We did not find any significant hemispheric differences in these rates of change for thalamic nuclei. Only the CM thalamic nucleus showed a sex-specific difference in atrophy rates, atrophying at a greater rate in male versus female participants. Roughly half of the thalamic nuclei showed greater atrophy than all extra-thalamic structures examined (0% to 0.54% per year). These results show the value of white-matter-nulled MPRAGE imaging and THOMAS segmentation for measuring distinct thalamic nuclei and for characterizing the high and heterogeneous atrophy rates of the thalamus and its nuclei across the adult lifespan. Collectively, these methods and results advance our understanding of the role of thalamic substructures in neurocognitive and disease-related changes that occur with aging.
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Affiliation(s)
- Eun Young Choi
- Department of Neurosurgery, Stanford University, 300 Pasteur Drive, MC5327, Stanford, CA 94305, USA
| | - Lu Tian
- Department of Biomedical Data Science, 1265 Welch Road, MC5464, Stanford, CA 94305, USA
| | - Jason H. Su
- Department of Radiology, Stanford University, 300 Pasteur Drive, MC5488, Stanford, CA 94305, USA,Department of Electrical Engineering, Stanford University, 350 Jane Stanford Way, MC9505, Stanford, CA 94305, USA
| | - Matthew T. Radovan
- Department of Computer Science, Stanford University, 353 Jane Stanford Way, MC9025, Stanford, CA 94305, USA
| | - Thomas Tourdias
- Department of Neuroradiology, Bordeaux University Hospital, Bordeaux, France,INSERM U1215, Neurocentre Magendie, University of Bordeaux, France
| | - Tammy T. Tran
- Department of Psychology, Stanford University, Building 420, MC2130, Stanford, CA 94305, USA
| | - Alexandra N. Trelle
- Department of Psychology, Stanford University, Building 420, MC2130, Stanford, CA 94305, USA
| | - Elizabeth Mormino
- Department of Neurology and Neurological Sciences, Stanford, University, 300 Pasteur Drive, MC5235, Stanford, CA 94305, USA,Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, USA
| | - Anthony D. Wagner
- Department of Psychology, Stanford University, Building 420, MC2130, Stanford, CA 94305, USA,Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, USA
| | - Brian K. Rutt
- Department of Radiology, Stanford University, 300 Pasteur Drive, MC5488, Stanford, CA 94305, USA,Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, USA,Corresponding author. (B.K. Rutt)
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13
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Kannappan B, Gunasekaran TI, te Nijenhuis J, Gopal M, Velusami D, Kothandan G, Lee KH. Polygenic score for Alzheimer’s disease identifies differential atrophy in hippocampal subfield volumes. PLoS One 2022; 17:e0270795. [PMID: 35830443 PMCID: PMC9278752 DOI: 10.1371/journal.pone.0270795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/20/2022] [Indexed: 01/18/2023] Open
Abstract
Hippocampal subfield atrophy is a prime structural change in the brain, associated with cognitive aging and neurodegenerative diseases such as Alzheimer’s disease. Recent developments in genome-wide association studies (GWAS) have identified genetic loci that characterize the risk of hippocampal volume loss based on the processes of normal and abnormal aging. Polygenic risk scores are the genetic proxies mimicking the genetic role of the pre-existing vulnerabilities of the underlying mechanisms influencing these changes. Discriminating the genetic predispositions of hippocampal subfield atrophy between cognitive aging and neurodegenerative diseases will be helpful in understanding the disease etiology. In this study, we evaluated the polygenic risk of Alzheimer’s disease (AD PGRS) for hippocampal subfield atrophy in 1,086 individuals (319 cognitively normal (CN), 591 mild cognitively impaired (MCI), and 176 Alzheimer’s disease dementia (ADD)). Our results showed a stronger association of AD PGRS effect on the left hemisphere than on the right hemisphere for all the hippocampal subfield volumes in a mixed clinical population (CN+MCI+ADD). The subfields CA1, CA4, hippocampal tail, subiculum, presubiculum, molecular layer, GC-ML-DG, and HATA showed stronger AD PGRS associations with the MCI+ADD group than with the CN group. The subfields CA3, parasubiculum, and fimbria showed moderately higher AD PGRS associations with the MCI+ADD group than with the CN group. Our findings suggest that the eight subfield regions, which were strongly associated with AD PGRS are likely involved in the early stage ADD and a specific focus on the left hemisphere could enhance the early prediction of ADD.
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Affiliation(s)
- Balaji Kannappan
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
| | - Tamil Iniyan Gunasekaran
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
| | - Jan te Nijenhuis
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
- * E-mail: (JN); (KHL)
| | - Muthu Gopal
- Health Systems Research & MRHRU, ICMR-National Institute of Epidemiology, Tirunelveli, Tamil Nadu, India
| | - Deepika Velusami
- Department of Physiology, Sri Manakula Vinayagar Medical College and Hospital, Puducherry, Tamil Nadu, India
| | - Gugan Kothandan
- Biopolymer Modeling and Protein Chemistry Laboratory, Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Chennai, Tamil Nadu, India
| | - Kun Ho Lee
- Gwangju Alzheimer’s & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
- Korea Brain Research Institute, Daegu, Republic of Korea
- * E-mail: (JN); (KHL)
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14
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Differential grey matter structure in women with premenstrual dysphoric disorder: evidence from brain morphometry and data-driven classification. Transl Psychiatry 2022; 12:250. [PMID: 35705554 PMCID: PMC9200862 DOI: 10.1038/s41398-022-02017-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 05/25/2022] [Accepted: 05/31/2022] [Indexed: 11/12/2022] Open
Abstract
Premenstrual dysphoric disorder (PMDD) is a female-specific condition classified in the Diagnostic and Statical Manual-5th edition under depressive disorders. Alterations in grey matter volume, cortical thickness and folding metrics have been associated with a number of mood disorders, though little is known regarding brain morphological alterations in PMDD. Here, women with PMDD and healthy controls underwent magnetic resonance imaging (MRI) during the luteal phase of the menstrual cycle. Differences in grey matter structure between the groups were investigated by use of voxel- and surface-based morphometry. Machine learning and multivariate pattern analysis were performed to test whether MRI data could distinguish women with PMDD from healthy controls. Compared to controls, women with PMDD had smaller grey matter volume in ventral posterior cortices and the cerebellum (Cohen's d = 0.45-0.76). Region-of-interest analyses further indicated smaller volume in the right amygdala and putamen of women with PMDD (Cohen's d = 0.34-0.55). Likewise, thinner cortex was observed in women with PMDD compared to controls, particularly in the left hemisphere (Cohen's d = 0.20-0.74). Classification analyses showed that women with PMDD can be distinguished from controls based on grey matter morphology, with an accuracy up to 74%. In line with the hypothesis of an impaired top-down inhibitory circuit involving limbic structures in PMDD, the present findings point to PMDD-specific grey matter anatomy in regions of corticolimbic networks. Furthermore, the results include widespread cortical and cerebellar regions, suggesting the involvement of distinct networks in PMDD pathophysiology.
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15
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Dubol M, Wikström J, Lanzenberger R, Epperson CN, Sundström-Poromaa I, Comasco E. Grey matter correlates of affective and somatic symptoms of premenstrual dysphoric disorder. Sci Rep 2022; 12:5996. [PMID: 35397641 PMCID: PMC8994757 DOI: 10.1038/s41598-022-07109-3] [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: 08/02/2021] [Accepted: 02/08/2022] [Indexed: 12/21/2022] Open
Abstract
Ovarian hormones fluctuations across the menstrual cycle are experienced by about 58% of women in their fertile age. Maladaptive brain sensitivity to these changes likely leads to the severe psychological, cognitive, and physical symptoms repeatedly experienced by women with Premenstrual Dysphoric Disorder (PMDD) during the late luteal phase of the menstrual cycle. However, the neuroanatomical correlates of these symptoms are unknown. The relationship between grey matter structure and PMDD symptom severity was delineated using structural magnetic resonance imaging during the late luteal phase of fifty-one women diagnosed with PMDD, combined with Voxel- and Surface-Based Morphometry, as well as subcortical volumetric analyses. A negative correlation was found between depression-related symptoms and grey matter volume of the bilateral amygdala. Moreover, the severity of affective and somatic PMDD symptoms correlated with cortical thickness, gyrification, sulcal depth, and complexity metrics, particularly in the prefrontal, cingulate, and parahippocampal gyri. The present findings provide the first evidence of grey matter morphological characteristics associated with PMDD symptomatology in brain regions expressing ovarian hormone receptors and of relevance to cognitive-affective functions, thus potentially having important implications for understanding how structural brain characteristics relate to PMDD symptomatology.
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Affiliation(s)
- Manon Dubol
- Department of Neuroscience, Science for Life Laboratory, Uppsala University, POB 593, 75124, Uppsala, Sweden
| | - Johan Wikström
- Department of Surgical Sciences, Neuroradiology, Uppsala University, Uppsala, Sweden
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - C Neill Epperson
- Department of Psychiatry, Department of Family Medicine, University of Colorado School of Medicine-Anschutz Medical Campus, Aurora, USA
| | | | - Erika Comasco
- Department of Neuroscience, Science for Life Laboratory, Uppsala University, POB 593, 75124, Uppsala, Sweden.
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16
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Rus-Oswald OG, Benner J, Reinhardt J, Bürki C, Christiner M, Hofmann E, Schneider P, Stippich C, Kressig RW, Blatow M. Musicianship-Related Structural and Functional Cortical Features Are Preserved in Elderly Musicians. Front Aging Neurosci 2022; 14:807971. [PMID: 35401149 PMCID: PMC8990841 DOI: 10.3389/fnagi.2022.807971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
Background Professional musicians are a model population for exploring basic auditory function, sensorimotor and multisensory integration, and training-induced neuroplasticity. The brain of musicians exhibits distinct structural and functional cortical features; however, little is known about how these features evolve during aging. This multiparametric study aimed to examine the functional and structural neural correlates of lifelong musical practice in elderly professional musicians. Methods Sixteen young musicians, 16 elderly musicians (age >70), and 15 elderly non-musicians participated in the study. We assessed gray matter metrics at the whole-brain and region of interest (ROI) levels using high-resolution magnetic resonance imaging (MRI) with the Freesurfer automatic segmentation and reconstruction pipeline. We used BrainVoyager semiautomated segmentation to explore individual auditory cortex morphotypes. Furthermore, we evaluated functional blood oxygenation level-dependent (BOLD) activations in auditory and non-auditory regions by functional MRI (fMRI) with an attentive tone-listening task. Finally, we performed discriminant function analyses based on structural and functional ROIs. Results A general reduction of gray matter metrics distinguished the elderly from the young subjects at the whole-brain level, corresponding to widespread natural brain atrophy. Age- and musicianship-dependent structural correlations revealed group-specific differences in several clusters including superior, middle, and inferior frontal as well as perirolandic areas. In addition, the elderly musicians exhibited increased gyrification of auditory cortex like the young musicians. During fMRI, the elderly non-musicians activated predominantly auditory regions, whereas the elderly musicians co-activated a much broader network of auditory association areas, primary and secondary motor areas, and prefrontal and parietal regions like, albeit weaker, the young musicians. Also, group-specific age- and musicianship-dependent functional correlations were observed in the frontal and parietal regions. Moreover, discriminant function analysis could separate groups with high accuracy based on a set of specific structural and functional, mainly temporal and occipital, ROIs. Conclusion In conclusion, despite naturally occurring senescence, the elderly musicians maintained musicianship-specific structural and functional cortical features. The identified structural and functional brain regions, discriminating elderly musicians from non-musicians, might be of relevance for the aging musicians’ brain. To what extent lifelong musical activity may have a neuroprotective impact needs to be addressed further in larger longitudinal studies.
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Affiliation(s)
- Oana G. Rus-Oswald
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zürich, Switzerland
- University Department of Geriatric Medicine FELIX PLATTER, Basel, Switzerland
- *Correspondence: Oana G. Rus-Oswald,
| | - Jan Benner
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Jan Benner,
| | - Julia Reinhardt
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zürich, Switzerland
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
- Department of Cardiology and Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland
- Department of Orthopedic Surgery and Traumatology, University Hospital of Basel, University of Basel, Basel, Switzerland
| | - Céline Bürki
- University Department of Geriatric Medicine FELIX PLATTER, Basel, Switzerland
| | - Markus Christiner
- Centre for Systematic Musicology, University of Graz, Graz, Austria
- Vitols Jazeps Latvian Academy of Music, Riga, Latvia
| | - Elke Hofmann
- Academy of Music, University of Applied Sciences and Arts Northwestern Switzerland (FHNW), Basel, Switzerland
| | - Peter Schneider
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Centre for Systematic Musicology, University of Graz, Graz, Austria
- Vitols Jazeps Latvian Academy of Music, Riga, Latvia
| | - Christoph Stippich
- Department of Neuroradiology and Radiology, Kliniken Schmieder, Allensbach, Germany
| | - Reto W. Kressig
- University Department of Geriatric Medicine FELIX PLATTER, Basel, Switzerland
| | - Maria Blatow
- Section of Neuroradiology, Department of Radiology and Nuclear Medicine, Neurocenter, Cantonal Hospital Lucerne, University of Lucerne, Lucerne, Switzerland
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Goto M, Abe O, Hagiwara A, Fujita S, Kamagata K, Hori M, Aoki S, Osada T, Konishi S, Masutani Y, Sakamoto H, Sakano Y, Kyogoku S, Daida H. Advantages of Using Both Voxel- and Surface-based Morphometry in Cortical Morphology Analysis: A Review of Various Applications. Magn Reson Med Sci 2022; 21:41-57. [PMID: 35185061 PMCID: PMC9199978 DOI: 10.2463/mrms.rev.2021-0096] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Surface-based morphometry (SBM) is extremely useful for estimating the indices of cortical morphology, such as volume, thickness, area, and gyrification, whereas voxel-based morphometry (VBM) is a typical method of gray matter (GM) volumetry that includes cortex measurement. In cases where SBM is used to estimate cortical morphology, it remains controversial as to whether VBM should be used in addition to estimate GM volume. Therefore, this review has two main goals. First, we summarize the differences between the two methods regarding preprocessing, statistical analysis, and reliability. Second, we review studies that estimate cortical morphological changes using VBM and/or SBM and discuss whether using VBM in conjunction with SBM produces additional values. We found cases in which detection of morphological change in either VBM or SBM was superior, and others that showed equivalent performance between the two methods. Therefore, we concluded that using VBM and SBM together can help researchers and clinicians obtain a better understanding of normal neurobiological processes of the brain. Moreover, the use of both methods may improve the accuracy of the detection of morphological changes when comparing the data of patients and controls. In addition, we introduce two other recent methods as future directions for estimating cortical morphological changes: a multi-modal parcellation method using structural and functional images, and a synthetic segmentation method using multi-contrast images (such as T1- and proton density-weighted images).
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Affiliation(s)
- Masami Goto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
| | | | - Shohei Fujita
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine
| | - Masaaki Hori
- Department of Radiology, Juntendo University School of Medicine
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine
| | - Takahiro Osada
- Department of Neurophysiology, Juntendo University School of Medicine
| | - Seiki Konishi
- Department of Neurophysiology, Juntendo University School of Medicine
| | | | - Hajime Sakamoto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | - Yasuaki Sakano
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | - Shinsuke Kyogoku
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | - Hiroyuki Daida
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
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18
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Zhou Y, Si X, Chen Y, Chao Y, Lin CP, Li S, Zhang X, Ming D, Li Q. Hippocampus- and Thalamus-Related Fiber-Specific White Matter Reductions in Mild Cognitive Impairment. Cereb Cortex 2021; 32:3159-3174. [PMID: 34891164 DOI: 10.1093/cercor/bhab407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/04/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis of mild cognitive impairment (MCI) fascinates screening high-risk Alzheimer's disease (AD). White matter is found to degenerate earlier than gray matter and functional connectivity during MCI. Although studies reveal white matter degenerates in the limbic system for MCI, how other white matter degenerates during MCI remains unclear. In our method, regions of interest with a high level of resting-state functional connectivity with hippocampus were selected as seeds to track fibers based on diffusion tensor imaging (DTI). In this way, hippocampus-temporal and thalamus-related fibers were selected, and each fiber's DTI parameters were extracted. Then, statistical analysis, machine learning classification, and Pearson's correlations with behavior scores were performed between MCI and normal control (NC) groups. Results show that: 1) the mean diffusivity of hippocampus-temporal and thalamus-related fibers are significantly higher in MCI and could be used to classify 2 groups effectively. 2) Compared with normal fibers, the degenerated fibers detected by the DTI indexes, especially for hippocampus-temporal fibers, have shown significantly higher correlations with cognitive scores. 3) Compared with the hippocampus-temporal fibers, thalamus-related fibers have shown significantly higher correlations with depression scores within MCI. Our results provide novel biomarkers for the early diagnoses of AD.
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Affiliation(s)
- Yu Zhou
- School of Microelectronics, Tianjin University, Tianjin 300072, China
| | - Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China.,Institute of Applied Psychology, Tianjin University, Tianjin 300350, China
| | - Yuanyuan Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Yiping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan.,Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience Hsinchu City, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin 300072, China
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19
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Shi L, Liu X, Wu K, Sun K, Lin C, Li Z, Zhao S, Fan X. Surface values, volumetric measurements and radiomics of structural MRI for the diagnosis and subtyping of attention-deficit/hyperactivity disorder. Eur J Neurosci 2021; 54:7654-7667. [PMID: 34614247 DOI: 10.1111/ejn.15485] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 08/22/2021] [Accepted: 10/03/2021] [Indexed: 11/28/2022]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is diagnosed subjectively based on an individual's behaviour and performance. The clinical community has no objective biomarker to inform the diagnosis and subtyping of ADHD. This study aimed to explore the potential diagnostic biomarkers of ADHD among surface values, volumetric metrics and radiomic features that were extracted from structural MRI images. Public data of New York University and Peking University were downloaded from the ADHD-200 Consortium. MRI T1-weighted images were pre-processed using CAT12. We calculated surface values based on the Desikan-Killiany atlas. The volumetric metrics (mean grey matter volume and mean white matter volume) and radiomic features within each automated anatomical labelling (AAL) brain area were calculated using DPABI and IBEX, respectively. The differences among three groups of participants were tested using ANOVA or Kruskal-Wallis test depending on the normality of the data. We selected discriminative features and classified typically developing controls (TDCs) and ADHD patients as well as two ADHD subtypes using least absolute shrinkage and selection operator and support vector machine algorithms. Our results showed that the radiomics-based model outperformed the others in discriminating ADHD from TDC and classifying ADHD subtypes (area under the curve [AUC]: 0.78 and 0.94 in training test; 0.79 and 0.85 in testing set). Combining grey matter volumes, surface values and clinical factors with radiomic features can improve the performance for classifying ADHD patients and TDCs with training and testing AUCs of 0.82 and 0.83, respectively. This study demonstrates that MRI T1-weighted features, especially radiomic features, are potential diagnostic biomarkers of ADHD.
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Affiliation(s)
- Liting Shi
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China
| | - Xuechun Liu
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Keqing Wu
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China.,School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Kui Sun
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Chunsen Lin
- Department of Radiology, Taian Disabled Soldiers' Hospital of Shandong Province, Tai'an, China
| | - Zhengmei Li
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Shuying Zhao
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China.,The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xiuqin Fan
- Laboratory of Nutrition and Development, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
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20
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Sandry J, Dobryakova E. Global hippocampal and selective thalamic nuclei atrophy differentiate chronic TBI from Non-TBI. Cortex 2021; 145:37-56. [PMID: 34689031 DOI: 10.1016/j.cortex.2021.08.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 05/04/2021] [Accepted: 08/12/2021] [Indexed: 12/27/2022]
Abstract
Traumatic brain injury (TBI) may increase susceptibility to neurodegenerative diseases later in life. One neurobiological parallel between chronic TBI and neurodegeneration may be accelerated aging and the nature of atrophy across subcortical gray matter structures. The main aim of the present investigation is to evaluate and rank the degree that subcortical gray matter atrophy differentiates chronic moderate-severe TBI from non-TBI participants by evaluating morphometric differences between groups. Forty individuals with moderate-severe chronic TBI (9.23 yrs from injury) and 33 healthy controls (HC) underwent high resolution 3D T1-weighted structural magnetic resonance imaging. Whole brain volume was classified into white matter, cortical and subcortical gray matter structures with hippocampi and thalami further segmented into subfields and nuclei, respectively. Extensive atrophy was observed across nearly all brain regions for chronic TBI participants. A series of multivariate logistic regression models identified subcortical gray matter structures of the hippocampus and thalamus as the most sensitive to differentiating chronic TBI from non-TBI participants (McFadden R2 = .36, p < .001). Further analyses revealed the pattern of hippocampal atrophy to be global, occurring across nearly all subfields. The pattern of thalamic atrophy appeared to be much more selective and non-uniform, with largest between-group differences evident for nuclei bordering the ventricles. Subcortical gray matter was negatively correlated with time since injury (r = -.31, p = .054), while white matter and cortical gray matter were not. Cognitive ability was lower in the chronic TBI group (Cohen's d = .97, p = .003) and correlated with subcortical structures including the pallidum (r2 = .23, p = .038), thalamus (r2 = .36, p = .007) and ventral diencephalon (r2 = .23, p = .036). These data may support an accelerated aging hypothesis in chronic moderate-severe TBI that coincides with a similar neuropathological profile found in neurodegenerative diseases.
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Affiliation(s)
- Joshua Sandry
- Psychology Department, Montclair State University, Montclair, NJ, USA.
| | - Ekaterina Dobryakova
- Center for Traumatic Brain Injury Research, Kessler Foundation, East Hanover, NJ, USA; Department of Physical Medicine and Rehabilitation, Rutgers-New Jersey Medical School Newark, NJ, USA
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21
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Mayer C, Frey BM, Schlemm E, Petersen M, Engelke K, Hanning U, Jagodzinski A, Borof K, Fiehler J, Gerloff C, Thomalla G, Cheng B. Linking cortical atrophy to white matter hyperintensities of presumed vascular origin. J Cereb Blood Flow Metab 2021; 41:1682-1691. [PMID: 33259747 PMCID: PMC8221767 DOI: 10.1177/0271678x20974170] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We examined the relationship between white matter hyperintensities (WMH) and cortical neurodegeneration in cerebral small vessel disease (CSVD) by investigating whether cortical thickness is a remote effect of WMH through structural fiber tract connectivity in a population at increased risk of CSVD. We measured cortical thickness on T1-weighted images and segmented WMH on FLAIR images in 930 participants of a population-based cohort study at baseline. DWI-derived whole-brain probabilistic tractography was used to define WMH connectivity to cortical regions. Linear mixed-effects models were applied to analyze the relationship between cortical thickness and connectivity to WMH. Factors associated with cortical thickness (age, sex, hemisphere, region, individual differences in cortical thickness) were added as covariates. Median age was 64 [IQR 46-76] years. Visual inspection of surface maps revealed distinct connectivity patterns of cortical regions to WMH. WMH connectivity to the cortex was associated with reduced cortical thickness (p = 0.009) after controlling for covariates. This association was found for periventricular WMH (p = 0.001) only. Our results indicate an association between WMH and cortical thickness via connecting fiber tracts. The results imply a mechanism of secondary neurodegeneration in cortical regions distant, yet connected to subcortical vascular lesions, which appears to be driven by periventricular WMH.
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Affiliation(s)
- Carola Mayer
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Benedikt M Frey
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Eckhard Schlemm
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kristin Engelke
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Uta Hanning
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Annika Jagodzinski
- Epidemiological Study Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Department of General and Interventional Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany
| | - Katrin Borof
- Epidemiological Study Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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22
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Podgórski P, Bladowska J, Sasiadek M, Zimny A. Novel Volumetric and Surface-Based Magnetic Resonance Indices of the Aging Brain - Does Male and Female Brain Age in the Same Way? Front Neurol 2021; 12:645729. [PMID: 34163419 PMCID: PMC8216769 DOI: 10.3389/fneur.2021.645729] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 04/20/2021] [Indexed: 12/21/2022] Open
Abstract
Introduction: Novel post-processing methods allow not only for assessment of brain volumetry or cortical thickness based on magnetic resonance imaging (MRI) but also for more detailed analysis of cortical shape and complexity using parameters such as sulcal depth, gyrification index, or fractal dimension. The aim of this study was to analyze changes in brain volumetry and other cortical indices during aging in men and women. Material and Methods: Material consisted of 697 healthy volunteers (aged 38–80 years; M/F, 264/443) who underwent brain MRI using a 1.5-T scanner. Voxel-based volumetry of total gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) was performed followed by assessment of cortical parameters [cortical thickness (CT), sulcal depth (SD), gyrification index (GI), and fractal dimension (FD)] in 150 atlas locations using surface-based morphometry with a region-based approach. All parameters were compared among seven age groups (grouped every 5 years) separately for men and women. Additionally, percentile curves for men and women were provided for total volumes of GM, WM, and CSF. Results: In men and women, a decrease in GM and WM volumes and an increase in CSF volume seem to progress slowly since the age of 45. In men, significant GM and WM loss as well as CSF increase start above 55 years of age, while in women, significant GM loss starts above 50 and significant WM loss as well as CSF increase above 60. CT was found to significantly decrease with aging in 39% of locations in women and in 36% of locations in men, SD was found to increase in 13.5% of locations in women and in 1.3% of locations in men, GI was decreased in 3.4% of locations in women and in 2.0% of locations in men, and FD was changed in 2.7% of locations in women compared to 2.0% in men. Conclusions: Male and female brains start aging at the similar age of 45. Compared to men, in women, the cortex is affected earlier and in the more complex pattern regarding not only cortical loss but also other alterations within the cortical shape, with relatively longer sparing of WM volume.
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Affiliation(s)
- Przemysław Podgórski
- Department of General and Interventional Radiology and Neuroradiology, Wroclaw Medical University, Wrocław, Poland
| | - Joanna Bladowska
- Department of General and Interventional Radiology and Neuroradiology, Wroclaw Medical University, Wrocław, Poland
| | - Marek Sasiadek
- Department of General and Interventional Radiology and Neuroradiology, Wroclaw Medical University, Wrocław, Poland
| | - Anna Zimny
- Department of General and Interventional Radiology and Neuroradiology, Wroclaw Medical University, Wrocław, Poland
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23
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Clinically Applicable Quantitative Magnetic Resonance Morphologic Measurements of Grey Matter Changes in the Human Brain. Brain Sci 2021; 11:brainsci11010055. [PMID: 33466559 PMCID: PMC7824828 DOI: 10.3390/brainsci11010055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/18/2020] [Accepted: 12/21/2020] [Indexed: 11/17/2022] Open
Abstract
(1) Purpose: Quantitative magnetic resonance imaging (qMRI) measurements can be used to sensitively estimate brain morphological alterations and may support clinical diagnosis of neurodegenerative diseases (ND). We aimed to establish a normative reference database for a clinical applicable quantitative MR morphologic measurement on neurodegenerative changes in patients; (2) Methods: Healthy subjects (HCs, n = 120) with an evenly distribution between 21 to 70 years and amyotrophic lateral sclerosis (ALS) patients (n = 11, mean age = 52.45 ± 6.80 years), as an example of ND patients, underwent magnetic resonance imaging (MRI) examinations under routine diagnostic conditions. Regional cortical thickness (rCTh) in 68 regions of interest (ROIs) and subcortical grey matter volume (SGMV) in 14 ROIs were determined from all subjects by using Computational Anatomy Toolbox. Those derived from HCs were analyzed to determine age-related differences and subsequently used as reference to estimate ALS-related alterations; (3) Results: In HCs, the rCTh (in 49/68 regions) and the SGMV (in 9/14 regions) in elderly subjects were less than those in younger subjects and exhibited negative linear correlations to age (p < 0.0007 for rCTh and p < 0.004 for SGMV). In comparison to age- and sex-matched HCs, the ALS patients revealed significant decreases of rCTh in eight ROIs, majorly located in frontal and temporal lobes; (4) Conclusion: The present study proves an overall grey matter decline with normal ageing as reported previously. The provided reference may be used for detection of grey matter alterations in neurodegenerative diseases that are not apparent in standard MR scans, indicating the potential of using qMRI as an add-on diagnostic tool in a clinical setting.
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24
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Sämann PG, Iglesias JE, Gutman B, Grotegerd D, Leenings R, Flint C, Dannlowski U, Clarke‐Rubright EK, Morey RA, Erp TG, Whelan CD, Han LKM, Velzen LS, Cao B, Augustinack JC, Thompson PM, Jahanshad N, Schmaal L. FreeSurfer
‐based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for
ENIGMA
studies and other collaborative efforts. Hum Brain Mapp 2020; 43:207-233. [PMID: 33368865 PMCID: PMC8805696 DOI: 10.1002/hbm.25326] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 11/26/2020] [Accepted: 12/13/2020] [Indexed: 12/11/2022] Open
Abstract
Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013–12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi‐)genetics. Finally, we highlight points where FreeSurfer‐based hippocampal subfield studies may be optimized.
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Affiliation(s)
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing University College London London UK
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology Massachusetts General Hospital/Harvard Medical School Boston Massachusetts US
- Computer Science and AI Laboratory (CSAIL), Massachusetts Institute of Technology (MIT) Cambridge Massachusetts US
| | - Boris Gutman
- Department of Biomedical Engineering Illinois Institute of Technology Chicago USA
| | | | - Ramona Leenings
- Department of Psychiatry University of Münster Münster Germany
| | - Claas Flint
- Department of Psychiatry University of Münster Münster Germany
- Department of Mathematics and Computer Science University of Münster Germany
| | - Udo Dannlowski
- Department of Psychiatry University of Münster Münster Germany
| | - Emily K. Clarke‐Rubright
- Brain Imaging and Analysis Center, Duke University Durham North Carolina USA
- VISN 6 MIRECC, Durham VA Durham North Carolina USA
| | - Rajendra A. Morey
- Brain Imaging and Analysis Center, Duke University Durham North Carolina USA
- VISN 6 MIRECC, Durham VA Durham North Carolina USA
| | - Theo G.M. Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior University of California Irvine California USA
- Center for the Neurobiology of Learning and Memory University of California Irvine Irvine California USA
| | - Christopher D. Whelan
- Imaging Genetics Center Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Los Angeles California USA
| | - Laura K. M. Han
- Department of Psychiatry Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience Amsterdam The Netherlands
| | - Laura S. Velzen
- Orygen Parkville Australia
- Centre for Youth Mental Health The University of Melbourne Melbourne Australia
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine & Dentistry University of Alberta Edmonton Canada
| | - Jean C. Augustinack
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology Massachusetts General Hospital/Harvard Medical School Boston Massachusetts US
| | - Paul M. Thompson
- Imaging Genetics Center Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Los Angeles California USA
| | - Neda Jahanshad
- Imaging Genetics Center Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Los Angeles California USA
| | - Lianne Schmaal
- Orygen Parkville Australia
- Centre for Youth Mental Health The University of Melbourne Melbourne Australia
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25
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Kong L, Lv B, Wu T, Zhang J, Fan Y, Ouyang M, Huang H, Peng Y, Liu Y. Altered structural cerebral cortex in children with Tourette syndrome. Eur J Radiol 2020; 129:109119. [PMID: 32593075 DOI: 10.1016/j.ejrad.2020.109119] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 05/24/2020] [Accepted: 06/04/2020] [Indexed: 11/28/2022]
Abstract
PURPOSE In this study, we used magnetic resonance imaging (MRI) to investigate the anatomical alterations of cerebral cortex in children with Tourette syndrome (TS) and explore whether such deficits were related with their clinical symptoms. METHODS All subjects were scanned in a 3.0T MRI scanner with three-dimensional T1-weighted images (3DT1WI). Then, some surface-based features were extracted by using the FreeSurfer software. After that, the between-group differences of those features were assessed. RESULTS Sixty TS patients and 52 age- and gender-matched healthy control were included in this study. Surface-based analyses revealed altered cortical thickness, cortical sulcus, cortical curvature and local gyrification index (LGI) in TS group compared with healthy controls. The brain regions with significant-group differences in cortical thickness included postcentral gyrus, superiorparietal gyrus, rostral anterior cingulate cortex in the left hemisphere and frontal pole, lateral occipital gyrus, inferior temporal gyrus in the right hemisphere. In addition, the superior temporal gyrus, medial orbitofrontal gyrus, supramarginal gyrus, medial orbitofrontal gyrus, superiorparietal gyrus and lateral occipital gyrus showed significant between-group differences for cortical sulcus. Moreover, the brain regions with significant between-group differences in cortical curvature were located in caudal anterior cingulate cortex, supramarginal gyrus, inferior parietal gyrus and lateral occipital gyrus. The alteration of LGI were most prominent in the inferior temporal gyrus and insula. Additionally, there was no statistical difference in brain surface area for TS children compared with controls. CONCLUSION The results of this study revealed that cortical thickness, sulcus, cortical curvature and LGI were changed in multiple brain regions for children with TS.
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Affiliation(s)
- Lei Kong
- The Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China; The Department of Radiology, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Bin Lv
- China Academy of Information and Communications Technology, Beijing, China; Ping An Technology (Shenzhen) Company Limited, Shenzhen, China
| | - Tongning Wu
- China Academy of Information and Communications Technology, Beijing, China
| | - Jishui Zhang
- The Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yang Fan
- Beijing Intelligent Brain Cloud Incorporated, Beijing, China
| | - Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States; The Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | - Yun Peng
- The Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yue Liu
- The Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
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26
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Henneghan A, Rao V, Harrison RA, Karuturi M, Blayney DW, Palesh O, Kesler SR. Cortical Brain Age from Pre-treatment to Post-chemotherapy in Patients with Breast Cancer. Neurotox Res 2020; 37:788-799. [PMID: 31900898 DOI: 10.1007/s12640-019-00158-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 12/18/2019] [Accepted: 12/22/2019] [Indexed: 12/12/2022]
Abstract
Chemotherapy-related cognitive impairment and associated brain changes may reflect accelerated brain aging; however, empirical evidence for this theory is limited. The purpose of this study was to measure brain aging in newly diagnosed patients with breast cancer treated with chemotherapy (n = 43) and compare its longitudinal change to that of controls (n = 50). Brain age indices, derived from cortical measures, were compared between women with breast cancer and matched healthy controls across 3 timepoints (time 1: pre-surgery, time 2: 1 month following chemotherapy completion, and time 3: 1-year post-chemotherapy). The breast cancer group showed a significant decrease in cortical thickness across the 3 timepoints (p < .001) and a trend towards significant increase in predicted brain age especially from pre-treatment (time 1) to post-chemotherapy (time 2) compared to controls (p = 0.08). Greater increase in predicted brain age was related to several clinical factors (HER-2 status, surgery type, and history of neoadjuvant chemotherapy) and greater decrease in cortical thickness was associated with greater decrease in performance on a verbal learning task from time 1 to time 3 (r = - 0.48, p < .01). This study demonstrated evidence of increased cortical brain aging in middle-aged patients with breast cancer following chemotherapy treatment that was associated with decreased verbal memory performance.
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Affiliation(s)
- Ashley Henneghan
- University of Texas at Austin School of Nursing, 1710 Red River St, Austin, TX, 78701, USA. .,Department of Oncology, Dell Medical School, University of Texas at Austin, 1601 Trinity St, Austin, TX, USA.
| | - Vikram Rao
- University of Texas at Austin School of Nursing, 1710 Red River St, Austin, TX, 78701, USA
| | - Rebecca A Harrison
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 431, Houston, TX, 77030, USA
| | - Meghan Karuturi
- Department of Breast Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Unit 1354, 1155 Pressler St., Houston, TX, 77030, USA
| | - Douglas W Blayney
- Division of Medical Oncology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Oxana Palesh
- Department of Psychiatry and Behavioral Sciences, Stanford University Cancer Institute, Stanford University, 401 Quarry Road, Office 2318, Stanford, CA, 94305, USA
| | - Shelli R Kesler
- University of Texas at Austin School of Nursing, 1710 Red River St, Austin, TX, 78701, USA.,Department of Oncology, Dell Medical School, University of Texas at Austin, 1601 Trinity St, Austin, TX, USA
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den Hollander J, Jonkers R, Mariën P, Bastiaanse R. Identifying the Speech Production Stages in Early and Late Adulthood by Using Electroencephalography. Front Hum Neurosci 2019; 13:298. [PMID: 31551734 PMCID: PMC6746946 DOI: 10.3389/fnhum.2019.00298] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 08/12/2019] [Indexed: 12/22/2022] Open
Abstract
Structural changes in the brain take place throughout one’s life. Changes related to cognitive decline may delay the stages of the speech production process in the aging brain. For example, semantic memory decline and poor inhibition may delay the retrieval of a concept from the mental lexicon. Electroencephalography (EEG) is a valuable method for identifying the timing of speech production stages. So far, studies using EEG mainly focused on a particular speech production stage in a particular group of subjects. Differences between subject groups and between methodologies have complicated identifying time windows of the speech production stages. For the current study, the speech production stages lemma retrieval, lexeme retrieval, phonological encoding, and phonetic encoding were tracked using a 64-channel EEG in 20 younger adults and 20 older adults. Picture-naming tasks were used to identify lemma retrieval, using semantic interference through previously named pictures from the same semantic category, and lexeme retrieval, using words with varying age of acquisition. Non-word reading was used to target phonological encoding (using non-words with a variable number of phonemes) and phonetic encoding (using non-words that differed in spoken syllable frequency). Stimulus-locked and response-locked cluster-based permutation analyses were used to identify the timing of these stages in the full time course of speech production from stimulus presentation until 100 ms before response onset in both subject groups. It was found that the timing of each speech production stage could be identified. Even though older adults showed longer response times for every task, only the timing of the lexeme retrieval stage was later for the older adults compared to the younger adults, while no such delay was found for the timing of the other stages. The results of a second cluster-based permutation analysis indicated that clusters that were observed in the timing of the stages for one group were absent in the other subject group, which was mainly the case in stimulus-locked time windows. A z-score mapping analysis was used to compare the scalp distributions related to the stages between the older and younger adults. No differences between both groups were observed with respect to scalp distributions, suggesting that the same groups of neurons are involved in the four stages, regardless of the adults’ age, even though the timing of the individual stages is different in both groups.
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Affiliation(s)
- Jakolien den Hollander
- International Doctorate in Experimental Approaches to Language and Brain (IDEALAB, Universities of Groningen, Potsdam, Newcastle, Trento and Macquarie University), Sydney, NSW, Australia
| | - Roel Jonkers
- Center for Language and Cognition Groningen (CLCG), University of Groningen, Groningen, Netherlands
| | - Peter Mariën
- Clinical and Experimental Neurolinguistics (CLIEN), Vrije Universiteit Brussel, Brussels, Belgium.,Department of Neurology and Memory Clinic, ZNA Middelheim General Hospital, Antwerp, Belgium
| | - Roelien Bastiaanse
- Center for Language and Cognition Groningen (CLCG), University of Groningen, Groningen, Netherlands.,Center for Language and Brain, National Research University Higher School of Economics, Moscow, Russia
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Tian Q, Resnick SM, Davatzikos C, Erus G, Simonsick EM, Studenski SA, Ferrucci L. A prospective study of focal brain atrophy, mobility and fitness. J Intern Med 2019; 286:88-100. [PMID: 30861232 PMCID: PMC6586507 DOI: 10.1111/joim.12894] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND The parallel decline of mobility and cognition with ageing is explained in part by shared brain structural changes that are related to fitness. However, the temporal sequence between fitness, brain structural changes and mobility loss has not been fully evaluated. METHODS Participants were from the Baltimore Longitudinal Study of Aging, aged 60 or older, initially free of cognitive and mobility impairments, with repeated measures of fitness (400-m time), mobility (6-m gait speed) and neuroimaging markers over 4 years (n = 332). Neuroimaging markers included volumes of total brain, ventricles, frontal, parietal, temporal and subcortical motor areas, and corpus callosum. Autoregressive models were used to examine the temporal sequence of each brain volume with mobility and fitness, adjusted for age, sex, race, body mass index, height, education, intracranial volume and APOE ɛ4 status. RESULTS After adjustment, greater volumes of total brain and selected frontal, parietal and temporal areas, and corpus callosum were unidirectionally associated with future faster gait speed over and beyond cross-sectional and autoregressive associations. There were trends towards faster gait speed being associated with future greater hippocampus and precuneus. Higher fitness was unidirectionally associated with future greater parahippocampal gyrus and not with volumes in other areas. Smaller ventricle predicted future higher fitness. CONCLUSION Specific regional brain volumes predict future mobility impairment. Impaired mobility is a risk factor for future atrophy of hippocampus and precuneus. Maintaining fitness preserves parahippocampal gyrus volume. Findings provide new insight into the complex and bidirectional relationship between the parallel decline of mobility and cognition often observed in older persons.
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Affiliation(s)
- Q Tian
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - S M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - C Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - G Erus
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - E M Simonsick
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - S A Studenski
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - L Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
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Zahr NM, Pohl KM, Saranathan M, Sullivan EV, Pfefferbaum A. Hippocampal subfield CA2+3 exhibits accelerated aging in Alcohol Use Disorder: A preliminary study. NEUROIMAGE-CLINICAL 2019; 22:101764. [PMID: 30904825 PMCID: PMC6434095 DOI: 10.1016/j.nicl.2019.101764] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 12/21/2018] [Accepted: 03/10/2019] [Indexed: 12/31/2022]
Abstract
The profile of brain structural dysmorphology of individuals with Alcohol Use Disorders (AUD) involves disruption of the limbic system. In vivo imaging studies report hippocampal volume loss in AUD relative to controls, but only recently has it been possible to articulate different regions of this complex structure. Volumetric analysis of hippocampal regions rather than total hippocampal volume may augment differentiation of disease processes. For example, damage to hippocampal subfield cornu ammonis 1 (CA1) is often reported in Alzheimer's disease (AD), whereas deficits in CA4/dentate gyrus are described in response to stress and trauma. Two previous studies explored the effects of chronic alcohol use on hippocampal subfields: one reported smaller volume of the CA2+3 in alcohol-dependent subjects relative to controls, associated with years of alcohol consumption; the other, smaller volumes of presubiculum, subiculum, and fimbria in alcohol-dependent relative to control men. The current study, conducted in 24 adults with DSM5-diagnosed AUD (7 women, 53.7 ± 8.8) and 20 controls (7 women, 54.1 ± 9.3), is the first to use FreeSurfer 6.0, which provides state-of-the art hippocampal parcellation, to explore the sensitivity of hippocampal sufields to alcoholism. T1- and T2- images were collected on a GE MR750 system with a 32-channel Nova head coil. FreeSurfer 6.0 hippocampal subfield analysis produced 12 subfields: parasubiculum; presubiculum; subiculum; CA1; CA2+3; CA4; GC-ML-DG (Granule Cell (GC) and Molecular Layer (ML) of the Dentate Gyrus (DG)); molecular layer; hippocampus-amygdala-transition-area (HATA); fimbria; hippocampal tail; hippocampal fissure; and whole volume for left and right hippocampi. A comprehensive battery of neuropsychological tests comprising attention, memory and learning, visuospatial abilities, and executive functions was administered. Multiple regression analyses of raw volumetric data for each subfields by group, age, sex, hemisphere, and supratentorial volume (svol) showed significant effects of svol (p < .04) on nearly all structures (excluding tail and fissure). Volumes corrected for svol showed effects of age (fimbria, fissure) and group (subiculum, CA1, CA4, GC-ML-DG, HATA, fimbria); CA2+3 showed a diagnosis-by-age interaction indicating older AUD individuals had a smaller volume than would be expected for their age. There were no selective relations between hippocampal subfields and performance on neuropsychological tests, likely due to lack of statistical power. The current results concur with the previous study identifying CA2+3 as sensitive to alcoholism, extend them by identifying an alcoholism-age interaction, and suggest an imaging phenotype distinguishing AUD from AD and stress/trauma. Whether alcohol use disorders (AUD) compromise hippocampal volume is disputed. A 32-channel head coil acquired high-resolution images. The hippocampus was segmented using FreeSurfer 6.0. Several subregions showed volume deficits in AUD relative to healthy controls. Cornu Ammonis 2+3 showed a alcoholism-by-age interaction.
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Affiliation(s)
- Natalie M Zahr
- Neuroscience Program, SRI International, 333 Ravenswood Ave., Menlo Park, CA 94025, USA; Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., Stanford, CA 94305, USA.
| | - Kilian M Pohl
- Neuroscience Program, SRI International, 333 Ravenswood Ave., Menlo Park, CA 94025, USA
| | - Manojkumar Saranathan
- Department of Medical Imaging, University of Arizona College of Medicine, 1501 N. Campbell Ave., Tucson, AZ 85724, USA
| | - Edith V Sullivan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., Stanford, CA 94305, USA
| | - Adolf Pfefferbaum
- Neuroscience Program, SRI International, 333 Ravenswood Ave., Menlo Park, CA 94025, USA; Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., Stanford, CA 94305, USA
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30
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Zheng F, Cui D, Zhang L, Zhang S, Zhao Y, Liu X, Liu C, Li Z, Zhang D, Shi L, Liu Z, Hou K, Lu W, Yin T, Qiu J. The Volume of Hippocampal Subfields in Relation to Decline of Memory Recall Across the Adult Lifespan. Front Aging Neurosci 2018; 10:320. [PMID: 30364081 PMCID: PMC6191512 DOI: 10.3389/fnagi.2018.00320] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 09/24/2018] [Indexed: 12/27/2022] Open
Abstract
Background: The hippocampus is an important limbic structure closely related to memory function. However, few studies have focused on the association between hippocampal subfields and age-related memory decline. We investigated the volume alterations of hippocampal subfields at different ages and assessed the correlations with Immediate and Delayed recall abilities. Materials and Methods: A total of 275 participants aged 20-89 years were classified into 4 groups: Young, 20-35 years; Middle-early, 36-50 years; Middle-late, 51-65 years; Old, 66-89 years. All data were acquired from the Dallas Lifespan Brain Study (DLBS). The volumes of hippocampal subfields were obtained using Freesurfer software. Analysis of covariance (ANCOVA) was performed to analyze alterations of subfield volumes among the 4 groups, and multiple comparisons between groups were performed using the Bonferroni method. Spearman correlation with false discovery rate correction was used to investigate the relationship between memory recall scores and hippocampal subfield volumes. Results: Apart from no significant difference in the left parasubiculum (P = 0.269) and a slight difference in the right parasubiculum (P = 0.022), the volumes of other hippocampal subfields were significantly different across the adult lifespan (P < 0.001). The hippocampal fissure volume was increased in the Old group, while volumes for other subfields decreased. In addition, Immediate recall scores were associated with volumes of the bilateral molecular layer, granule cell layer of the dentate gyrus (GC-DG), cornus ammonis (CA) 1, CA2/3, CA4, left fimbria and hippocampal amygdala transition area (HATA), and right fissure (P < 0.05). Delayed recall scores were associated with the bilateral molecular layer, GC-DG, CA2/3 and CA4; left tail, presubiculum, CA1, subiculum, fimbria and HATA (P < 0.05). Conclusion: The parasubiculum volume was not significantly different across the adult lifespan, while atrophy in dementia patients in some studies. Based on these findings, we speculate that volume changes in this region might be considered as a biomarker for dementia disorders. Additionally, several hippocampal subfield volumes were significantly associated with memory scores, further highlighting the key role of the hippocampus in age-related memory decline. These regions could be used to assess the risk of memory decline across the adult lifespan.
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Affiliation(s)
- Fenglian Zheng
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
| | - Dong Cui
- College of Radiology, Taishan Medical University, Taian, China
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Li Zhang
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
| | - Shitong Zhang
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
- College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Yue Zhao
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
- College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Xiaojing Liu
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
| | - Chunhua Liu
- School of Basic Medical Sciences, Taishan Medical University, Taian, China
| | - Zhengmei Li
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
| | - Dongsheng Zhang
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
| | - Liting Shi
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
| | - Zhipeng Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Kun Hou
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
| | - Wen Lu
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
| | - Tao Yin
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Jianfeng Qiu
- Medical Engineering and Technology Research Center, Taishan Medical University, Taian, China
- Imaging-X Joint Laboratory, Taian, China
- College of Radiology, Taishan Medical University, Taian, China
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