1
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Yuan J, Siakallis L, Li HB, Brandner S, Zhang J, Li C, Mancini L, Bisdas S. Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach. BMC Med Imaging 2024; 24:104. [PMID: 38702613 PMCID: PMC11067215 DOI: 10.1186/s12880-024-01274-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 04/15/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.
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Affiliation(s)
- Jialin Yuan
- Department of Radiology, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
- Queen Square Institute of Neurology, University College London, London, UK
| | - Loizos Siakallis
- Queen Square Institute of Neurology, University College London, London, UK
| | - Hongwei Bran Li
- Department of Informatics, Technical University of Munich, Munich, Germany
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, USA
| | - Sebastian Brandner
- Division of Neuropathology, Queen Square Institute of Neurology, University College London, London, UK
| | - Jianguo Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Chenming Li
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Laura Mancini
- Queen Square Institute of Neurology, University College London, London, UK
- Lysholm Department of Neuroradiology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Sotirios Bisdas
- Queen Square Institute of Neurology, University College London, London, UK.
- Lysholm Department of Neuroradiology, University College London Hospitals NHS Foundation Trust, London, UK.
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2
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Taghvaei M, Cook P, Sadaghiani S, Shakibajahromi B, Tackett W, Dolui S, De D, Brown C, Khandelwal P, Yushkevich P, Das S, Wolk DA, Detre JA. Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography. Hum Brain Mapp 2023; 44:3943-3953. [PMID: 37148501 PMCID: PMC10258527 DOI: 10.1002/hbm.26326] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/08/2023] Open
Abstract
White matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and changes in adjacent normal-appearing white matter can disrupt computerized tract reconstruction and result in inaccurate measures of structural brain connectivity. The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH. To assess the impact of using young versus older subject diffusion MRI data for virtual lesion tractography, we leveraged recently available diffusion MRI data from the Human Connectome Project (HCP) Lifespan database. Neuroimaging data from 50 healthy young (39.2 ± 1.6 years) and 46 healthy older (74.2 ± 2.5 years) subjects were obtained from the publicly available HCP-Aging database. Three WMH masks with low, moderate, and high lesion burdens were extracted from the WMH lesion frequency map of locally acquired FLAIR MRI data. Deterministic tractography was conducted to extract streamlines in 21 WM bundles with and without the WMH masks as regions of avoidance in both young and older cohorts. For intact tractography without virtual lesion masks, 7 out of 21 WM pathways showed a significantly lower number of streamlines in older subjects compared to young subjects. A decrease in streamline count with higher native lesion burden was found in corpus callosum, corticostriatal tract, and fornix pathways. Comparable percentages of affected streamlines were obtained in young and older groups with virtual lesion tractography using the three WMH lesion masks of increasing severity. We conclude that using normative diffusion MRI data from young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age-matched normative data.
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Affiliation(s)
- Mohammad Taghvaei
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Philip Cook
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Shokufeh Sadaghiani
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - William Tackett
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sudipto Dolui
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Debarun De
- Department of Computer EngineeringUniversity of IllinoisUrbanaIllinoisUSA
| | - Christopher Brown
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Pulkit Khandelwal
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Paul Yushkevich
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandhitsu Das
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John A. Detre
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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3
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Yao J, Tendler BC, Zhou Z, Lei H, Zhang L, Bao A, Zhong J, Miller KL, He H. Both noise-floor and tissue compartment difference in diffusivity contribute to FA dependence on b-value in diffusion MRI. Hum Brain Mapp 2023; 44:1371-1388. [PMID: 36264194 PMCID: PMC9921221 DOI: 10.1002/hbm.26121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 08/27/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022] Open
Abstract
Noninvasive diffusion magnetic resonance imaging (dMRI) has been widely employed in both clinical and research settings to investigate brain tissue microstructure. Despite the evidence that dMRI-derived fractional anisotropy (FA) correlates with white matter properties, the metric is not specific. Recent studies have reported that FA is dependent on the b-value, and its origin has primarily been attributed to either the influence of microstructure or the noise-floor effect. A systematic investigation into the inter-relationship of these two effects is however still lacking. This study aims to quantify contributions of the reported differences in intra- and extra-neurite diffusivity to the observed changes in FA, in addition to the noise in measurements. We used in-vivo and post-mortem human brain imaging, as well as numerical simulations and histological validation, for this purpose. Our investigations reveal that the percentage difference of FA between b-values (pdFA) has significant positive associations with neurite density index (NDI), which is derived from in-vivo neurite orientation dispersion and density imaging (NODDI), or Bielschowsky's silver impregnation (BIEL) staining sections of fixed post-mortem human brain samples. Furthermore, such an association is found to be varied with Signal-to-Noise Ratio (SNR) level, indicating a nonlinear interaction effect between tissue microstructure and noise. Finally, a multicompartment model simulation revealed that these findings can be driven by differing diffusivities of intra- and extra-neurite compartments in tissue, with the noise-floor further amplifying the effect. In conclusion, both the differences in intra- and extra-neurite diffusivity and noise-floor effects significantly contribute to the FA difference associated with the b-value.
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Affiliation(s)
- Junye Yao
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Zihan Zhou
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hao Lei
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China
| | - Lei Zhang
- Department of Neurology in Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of Zhejiang Province, and Department of Neurobiology, Zhejiang University, Hangzhou, China.,National Human Brain Bank for Health and Disease, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China
| | - Aimin Bao
- Department of Neurology in Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of Zhejiang Province, and Department of Neurobiology, Zhejiang University, Hangzhou, China.,National Human Brain Bank for Health and Disease, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
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4
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Lam K, Nguyen PT, Anh LV, Lien T. Blended Motor-Sensory Nerve Bundles on Diffused Tensor Imaging: Evidence of Brain Plasticity in a Patient with 36-year Sequelae from Encephalitis. Open Access Maced J Med Sci 2022. [DOI: 10.3889/oamjms.2022.8643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND: Brain plasticity refers to the extraordinary ability of the brain to modify its structure and function following changes within the body or in the external environment. However, it is not easy to find it on non-invasive imaging modality.
CASE REPORT: In this article, we report the case of a 36-year-old male patient with sequelae of encephalitis. The patient had general epilepsy with multiple hospital admissions. MRI 3.0 Tesla showed his cerebral hemispheres were asymmetrical both morphologically and tractographically; there was a scar at the right temporo-occipital region, and an atrophy of the right temporal lobe, hippocampus and pontine. DTI reconstruction showed asymmetrical cortico-spinal and thalamo-cortical tracts with posterior thalamo-cortical tract was partly damaged by the scar. Blended motor-sensory nerve bundles were observed only on the left side of the patient’s brain but not on the right or healthy subjects. DTI quantification showed the lower line number, lower FA and higher ADC in the patient compared to healthy subjects and within the patient with decreased functionality on the side of the scar.
CONCLUSION: Non-invasive DTI with 3D image reconstruction on the patient showed evidence of brain plasticity appeared on cortico-spinal and thalamo-cortical tracts and can inform diagnosis and treatment strategies.
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5
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MacDonald ME, Pike GB. MRI of healthy brain aging: A review. NMR IN BIOMEDICINE 2021; 34:e4564. [PMID: 34096114 DOI: 10.1002/nbm.4564] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 05/08/2021] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
We present a review of the characterization of healthy brain aging using MRI with an emphasis on morphology, lesions, and quantitative MR parameters. A scope review found 6612 articles encompassing the keywords "Brain Aging" and "Magnetic Resonance"; papers involving functional MRI or not involving imaging of healthy human brain aging were discarded, leaving 2246 articles. We first consider some of the biogerontological mechanisms of aging, and the consequences of aging in terms of cognition and onset of disease. Morphological changes with aging are reviewed for the whole brain, cerebral cortex, white matter, subcortical gray matter, and other individual structures. In general, volume and cortical thickness decline with age, beginning in mid-life. Prevalent silent lesions such as white matter hyperintensities, microbleeds, and lacunar infarcts are also observed with increasing frequency. The literature regarding quantitative MR parameter changes includes T1 , T2 , T2 *, magnetic susceptibility, spectroscopy, magnetization transfer, diffusion, and blood flow. We summarize the findings on how each of these parameters varies with aging. Finally, we examine how the aforementioned techniques have been used for age prediction. While relatively large in scope, we present a comprehensive review that should provide the reader with sound understanding of what MRI has been able to tell us about how the healthy brain ages.
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Affiliation(s)
- M Ethan MacDonald
- Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
- Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
- Healthy Brain Aging Laboratory, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - G Bruce Pike
- Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
- Healthy Brain Aging Laboratory, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
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6
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Sargurupremraj M, Suzuki H, Jian X, Sarnowski C, Evans TE, Bis JC, Eiriksdottir G, Sakaue S, Terzikhan N, Habes M, Zhao W, Armstrong NJ, Hofer E, Yanek LR, Hagenaars SP, Kumar RB, van den Akker EB, McWhirter RE, Trompet S, Mishra A, Saba Y, Satizabal CL, Beaudet G, Petit L, Tsuchida A, Zago L, Schilling S, Sigurdsson S, Gottesman RF, Lewis CE, Aggarwal NT, Lopez OL, Smith JA, Valdés Hernández MC, van der Grond J, Wright MJ, Knol MJ, Dörr M, Thomson RJ, Bordes C, Le Grand Q, Duperron MG, Smith AV, Knopman DS, Schreiner PJ, Evans DA, Rotter JI, Beiser AS, Maniega SM, Beekman M, Trollor J, Stott DJ, Vernooij MW, Wittfeld K, Niessen WJ, Soumaré A, Boerwinkle E, Sidney S, Turner ST, Davies G, Thalamuthu A, Völker U, van Buchem MA, Bryan RN, Dupuis J, Bastin ME, Ames D, Teumer A, Amouyel P, Kwok JB, Bülow R, Deary IJ, Schofield PR, Brodaty H, Jiang J, Tabara Y, Setoh K, Miyamoto S, Yoshida K, Nagata M, Kamatani Y, Matsuda F, Psaty BM, Bennett DA, De Jager PL, Mosley TH, Sachdev PS, Schmidt R, Warren HR, Evangelou E, Trégouët DA, Ikram MA, Wen W, DeCarli C, Srikanth VK, Jukema JW, Slagboom EP, Kardia SLR, Okada Y, Mazoyer B, Wardlaw JM, Nyquist PA, Mather KA, Grabe HJ, Schmidt H, Van Duijn CM, Gudnason V, Longstreth WT, Launer LJ, Lathrop M, Seshadri S, Tzourio C, Adams HH, Matthews PM, Fornage M, Debette S. Cerebral small vessel disease genomics and its implications across the lifespan. Nat Commun 2020; 11:6285. [PMID: 33293549 PMCID: PMC7722866 DOI: 10.1038/s41467-020-19111-2] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 09/10/2020] [Indexed: 12/14/2022] Open
Abstract
White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.
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Affiliation(s)
- Muralidharan Sargurupremraj
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Hideaki Suzuki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo, Aoba, Sendai, 980-8573, Japan
- Department of Cardiovascular Medicine, Tohoku University Hospital, 1-1, Seiryo, Aoba, Sendai, 980-8574, Japan
- Department of Brain Sciences, Imperial College London, London, W12 0NN, UK
| | - Xueqiu Jian
- University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, 77030, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
| | - Chloé Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Tavia E Evans
- Department of Clinical Genetics, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | | | - Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo, 113-0033, Japan
| | - Natalie Terzikhan
- Department of Epidemiology, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Community Medicine, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Nicola J Armstrong
- Mathematics and Statistics, Murdoch University, Murdoch, WA, 6150, Australia
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, 8036, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036, Graz, Austria
| | - Lisa R Yanek
- GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Saskia P Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Social Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Rajan B Kumar
- Department of Public Health Sciences, University of California at Davis, Davis, CA, 95616, USA
| | - Erik B van den Akker
- Section of Molecular Epidemiology, Biomedical Sciences, Leiden university Medical Center, 2333 ZA, Leiden, The Netherlands
- Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, NL, 2629 HS, USA
- Leiden Computational Biology Centre, Leiden University Medical Centre, 2333 ZA, Leiden, The Netherlands
| | - Rebekah E McWhirter
- Centre for Law and Genetics, Faculty of Law, University of Tasmania, Hobart, TAS, 7005, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia
| | - Stella Trompet
- Department of Internal Medicine, section of gerontology and geriatrics, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
- Department of Cardiology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Aniket Mishra
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Yasaman Saba
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
- Gottfried Schatz Research Center, Department of Molecular Biology and Biochemistry, Medical University of Graz, 8010, Graz, Austria
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
- Boston University and the NHLBI's Framingham Heart Study, Boston, MA, 02215, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Gregory Beaudet
- University of Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
| | - Laurent Petit
- University of Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
| | - Ami Tsuchida
- University of Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
| | - Laure Zago
- University of Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
| | - Sabrina Schilling
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | | | | | - Cora E Lewis
- University of Alabama at Birmingham School of Medicine, Birmingham, AL, 35233, USA
| | - Neelum T Aggarwal
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Oscar L Lopez
- Departments of Neurology and Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109-2029, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Maria C Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Row Fogo Centre for Ageing and The Brain, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Jeroen van der Grond
- Department of Radiology, Leiden University medical Center, 2333 ZA, Leiden, The Netherlands
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, 4072, Australia
- Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, 17475, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, 17475, Greifswald, Germany
| | - Russell J Thomson
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia
- Centre for Research in Mathematics and Data Science, Western Sydney University, Penrith, NSW, 2751, Australia
| | - Constance Bordes
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Quentin Le Grand
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Marie-Gabrielle Duperron
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | | | | | - Pamela J Schreiner
- University of Minnesota School of Public Health, Minneapolis, MN, 55455, USA
| | - Denis A Evans
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Alexa S Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
- Boston University and the NHLBI's Framingham Heart Study, Boston, MA, 02215, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Marian Beekman
- Section of Molecular Epidemiology, Biomedical Sciences, Leiden university Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Julian Trollor
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Meike W Vernooij
- Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Katharina Wittfeld
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, 17489, Greifswald, Germany
| | - Wiro J Niessen
- Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
- Faculty of Applied Sciences, Delft University of Technology, Delft, NL, 2629 HS, USA
| | - Aicha Soumaré
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Eric Boerwinkle
- University of Texas Health Science Center at Houston School of Public Health, Houston, TX, 77030, USA
| | - Stephen Sidney
- Kaiser Permanente Division of Research, Oakland, CA, 94612, USA
| | - Stephen T Turner
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, 55905, USA
| | - Gail Davies
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036, Graz, Austria
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Anbupalam Thalamuthu
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Mark A van Buchem
- Row Fogo Centre for Ageing and The Brain, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - R Nick Bryan
- The University of Texas at Austin Dell Medical School, Austin, TX, 78712, USA
| | - Josée Dupuis
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
- Department of Cardiology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Mark E Bastin
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036, Graz, Austria
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - David Ames
- National Ageing Research Institute Royal Melbourne Hospital, Parkville, VIC, 3052, Australia
- Academic Unit for Psychiatry of Old Age, University of Melbourne, St George's Hospital, Kew, VIC, 3101, Australia
| | - Alexander Teumer
- Department of Epidemiology, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
- Department of Internal Medicine B, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Philippe Amouyel
- Inserm U1167, 59000, Lille, France
- Department of Epidemiology and Public Health, Pasteur Institute of Lille, 59000, Lille, France
| | - John B Kwok
- Brain and Mind Centre - The University of Sydney, Camperdown, NSW, 2050, Australia
- School of Medical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Robin Bülow
- Department of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, 17489, Greifswald, Germany
| | - Ian J Deary
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036, Graz, Austria
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Peter R Schofield
- School of Medical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
- Neuroscience Research Australia, Randwick, NSW, 2031, Australia
| | - Henry Brodaty
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
- Dementia Centre for Research Collaboration, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Jiyang Jiang
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Kazuya Setoh
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Susumu Miyamoto
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Kazumichi Yoshida
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Manabu Nagata
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Bruce M Psaty
- Departments of Epidemiology, Medicine and Health Services, University of Washington, Seattle, WA, 98195, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, 10032, USA
- Program in Population and Medical Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Thomas H Mosley
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Perminder S Sachdev
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, 2031, Australia
| | - Reinhold Schmidt
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Helen R Warren
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, E1 4NS, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, SW7 2AZ, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Mpizani, 455 00, Greece
| | - David-Alexandre Trégouët
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Mohammad A Ikram
- Department of Epidemiology, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Charles DeCarli
- Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, 95817, USA
| | - Velandai K Srikanth
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia
- Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Eline P Slagboom
- Section of Molecular Epidemiology, Biomedical Sciences, Leiden university Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Osaka, Japan
| | - Bernard Mazoyer
- University of Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Row Fogo Centre for Ageing and The Brain, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- MRC UK Dementia Research Institute at the University of Edinburgh, Edinburgh, EH8 9YL, UK
| | - Paul A Nyquist
- Department of Neurology, Johns Hopkins School of Medicine, Baltimone, MD, 21205, USA
- General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia
- Neuroscience Research Australia, Randwick, NSW, 2031, Australia
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, 17475, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, 17475, Greifswald, Germany
| | - Helena Schmidt
- Gottfried Schatz Research Center, Department of Molecular Biology and Biochemistry, Medical University of Graz, 8010, Graz, Austria
| | - Cornelia M Van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Vilmundur Gudnason
- Icelandic Heart Association, IS-201, Kópavogur, Iceland
- University of Iceland, Faculty of Medicine, 101, Reykjavík, Iceland
| | - William T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, WA, 98104-2420, USA
| | - Lenore J Launer
- Laboratory of Epidemiology, Demography, and Biometry, National Institute of Aging, The National Institutes of Health, Bethesda, MD, 20892, USA
- Intramural Research Program/National Institute on Aging/National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mark Lathrop
- University of McGill Genome Center, Montreal, QC, H3A 0G1, Canada
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
- Boston University and the NHLBI's Framingham Heart Study, Boston, MA, 02215, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Christophe Tzourio
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
- CHU de Bordeaux, Pole de santé publique, Service d'information médicale, 33000, Bordeaux, France
| | - Hieab H Adams
- Department of Clinical Genetics, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Paul M Matthews
- Department of Brain Sciences, Imperial College London, London, W12 0NN, UK
- UK Dementia Research Institute, London, WC1E 6BT, UK
- Data Science Institute, Imperial College London, London, SW7 2AZ, UK
| | - Myriam Fornage
- University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, 77030, USA.
| | - Stéphanie Debette
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France.
- Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA.
- Department of Neurology, CHU de Bordeaux, 33000, Bordeaux, France.
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7
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Relationship between the disrupted topological efficiency of the structural brain connectome and glucose hypometabolism in normal aging. Neuroimage 2020; 226:117591. [PMID: 33248254 DOI: 10.1016/j.neuroimage.2020.117591] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/02/2020] [Accepted: 11/19/2020] [Indexed: 12/18/2022] Open
Abstract
Normal aging is accompanied by structural degeneration and glucose hypometabolism in the human brain. However, the relationship between structural network disconnections and hypometabolism in normal aging remains largely unknown. In the present study, by combining MRI and PET techniques, we investigated the metabolic mechanism of the structural brain connectome and its relationship with normal aging in a cross-sectional, community-based cohort of 42 cognitively normal elderly individuals aged 57-84 years. The structural connectome was constructed based on diffusion MRI tractography, and the network efficiency metrics were quantified using graph theory analyses. FDG-PET scanning was performed to evaluate the glucose metabolic level in the cortical regions of the individuals. The results of this study demonstrated that both network efficiency and cortical metabolism decrease with age (both p < 0.05). In the subregions of the bilateral thalamus, significant correlations between nodal efficiency and cortical metabolism could be observed across subjects. Individual-level analyses indicated that brain regions with higher nodal efficiency tend to exhibit higher metabolic levels, implying a tight coupling between nodal efficiency and glucose metabolism (r = 0.56, p = 1.15 × 10-21). Moreover, efficiency-metabolism coupling coefficient significantly increased with age (r = 0.44, p = 0.0046). Finally, the main findings were also reproducible in the ADNI dataset. Together, our results demonstrate a close coupling between structural brain connectivity and cortical metabolism in normal elderly individuals and provide new insight that improve the present understanding of the metabolic mechanisms of structural brain disconnections in normal aging.
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8
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Edde M, Theaud G, Rheault F, Dilharreguy B, Helmer C, Dartigues JF, Amieva H, Allard M, Descoteaux M, Catheline G. Free water: A marker of age-related modifications of the cingulum white matter and its association with cognitive decline. PLoS One 2020; 15:e0242696. [PMID: 33216815 PMCID: PMC7678997 DOI: 10.1371/journal.pone.0242696] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 11/08/2020] [Indexed: 11/19/2022] Open
Abstract
Diffusion MRI is extensively used to investigate changes in white matter microstructure. However, diffusion measures within white matter tissue can be affected by partial volume effects due to cerebrospinal fluid and white matter hyperintensities, especially in the aging brain. In previous aging studies, the cingulum bundle that plays a central role in the architecture of the brain networks supporting cognitive functions has been associated with cognitive deficits. However, most of these studies did not consider the partial volume effects on diffusion measures. The aim of this study was to evaluate the effect of free water elimination on diffusion measures of the cingulum in a group of 68 healthy elderly individuals. We first determined the effect of free water elimination on conventional DTI measures and then examined the effect of free water elimination on verbal fluency performance over 12 years. The cingulum bundle was reconstructed with a tractography pipeline including a white matter hyperintensities mask to limit the negative impact of hyperintensities on fiber tracking algorithms. We observed that free water elimination increased the ability of conventional DTI measures to detect associations between tissue diffusion measures of the cingulum and changes in verbal fluency in older individuals. Moreover, free water content and mean diffusivity measured along the cingulum were independently associated with changes in verbal fluency. This suggests that both tissue modifications and an increase in interstitial isotropic water would contribute to cognitive decline. These observations reinforce the importance of using free water elimination when studying brain aging and indicate that free water itself could be a relevant marker for age-related cingulum white matter modifications and cognitive decline.
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Affiliation(s)
- Manon Edde
- EPHE, PSL, Bordeaux, France
- CNRS, INCIA, UMR 5287, Bordeaux, France
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - François Rheault
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Catherine Helmer
- Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, France
| | - Jean-François Dartigues
- Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, France
- CHU de Bordeaux, Bordeaux, France
| | - Hélène Amieva
- Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, France
| | - Michèle Allard
- EPHE, PSL, Bordeaux, France
- CNRS, INCIA, UMR 5287, Bordeaux, France
- CHU de Bordeaux, Bordeaux, France
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Gwénaëlle Catheline
- EPHE, PSL, Bordeaux, France
- CNRS, INCIA, UMR 5287, Bordeaux, France
- Université de Bordeaux, INCIA, UMR 5287, Bordeaux, France
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9
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Kimpton JA, Batalle D, Barnett ML, Hughes EJ, Chew ATM, Falconer S, Tournier JD, Alexander D, Zhang H, Edwards AD, Counsell SJ. Diffusion magnetic resonance imaging assessment of regional white matter maturation in preterm neonates. Neuroradiology 2020; 63:573-583. [PMID: 33123752 PMCID: PMC7966229 DOI: 10.1007/s00234-020-02584-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/13/2020] [Indexed: 02/03/2023]
Abstract
Purpose Diffusion magnetic resonance imaging (dMRI) studies report altered white matter (WM) development in preterm infants. Neurite orientation dispersion and density imaging (NODDI) metrics provide more realistic estimations of neurite architecture in vivo compared with standard diffusion tensor imaging (DTI) metrics. This study investigated microstructural maturation of WM in preterm neonates scanned between 25 and 45 weeks postmenstrual age (PMA) with normal neurodevelopmental outcomes at 2 years using DTI and NODDI metrics. Methods Thirty-one neonates (n = 17 male) with median (range) gestational age (GA) 32+1 weeks (24+2–36+4) underwent 3 T brain MRI at median (range) post menstrual age (PMA) 35+2 weeks (25+3–43+1). WM tracts (cingulum, fornix, corticospinal tract (CST), inferior longitudinal fasciculus (ILF), optic radiations) were delineated using constrained spherical deconvolution and probabilistic tractography in MRtrix3. DTI and NODDI metrics were extracted for the whole tract and cross-sections along each tract to assess regional development. Results PMA at scan positively correlated with fractional anisotropy (FA) in the CST, fornix and optic radiations and neurite density index (NDI) in the cingulum, CST and fornix and negatively correlated with mean diffusivity (MD) in all tracts. A multilinear regression model demonstrated PMA at scan influenced all diffusion measures, GA and GAxPMA at scan influenced FA, MD and NDI and gender affected NDI. Cross-sectional analyses revealed asynchronous WM maturation within and between WM tracts.). Conclusion We describe normal WM maturation in preterm neonates with normal neurodevelopmental outcomes. NODDI can enhance our understanding of WM maturation compared with standard DTI metrics alone. Supplementary Information The online version of this article (10.1007/s00234-020-02584-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- J A Kimpton
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK
| | - D Batalle
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M L Barnett
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK
| | - E J Hughes
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK
| | - A T M Chew
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK
| | - S Falconer
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK
| | - J D Tournier
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK
| | - D Alexander
- Department of Computer Science and Centre for Medical Imaging Computing, University College London, London, UK
| | - H Zhang
- Department of Computer Science and Centre for Medical Imaging Computing, University College London, London, UK
| | - A D Edwards
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK
| | - S J Counsell
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK.
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10
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Lynn JD, Anand C, Arshad M, Homayouni R, Rosenberg DR, Ofen N, Raz N, Stanley JA. Microstructure of Human Corpus Callosum across the Lifespan: Regional Variations in Axon Caliber, Density, and Myelin Content. Cereb Cortex 2020; 31:1032-1045. [PMID: 32995843 DOI: 10.1093/cercor/bhaa272] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 08/21/2020] [Accepted: 08/21/2020] [Indexed: 12/13/2022] Open
Abstract
The myeloarchitecture of the corpus callosum (CC) is characterized as a mosaic of distinct differences in fiber density of small- and large-diameter axons along the anterior-posterior axis; however, regional and age differences across the lifespan are not fully understood. Using multiecho T2 magnetic resonance imaging combined with multi-T2 fitting, the myelin water fraction (MWF) and geometric-mean of the intra-/extracellular water T2 (geomT2IEW) in 395 individuals (7-85 years; 41% males) were examined. The approach was validated where regional patterns along the CC closely resembled the histology; MWF matched mean axon diameter and geomT2IEW mirrored the density of large-caliber axons. Across the lifespan, MWF exhibited a quadratic association with age in all 10 CC regions with evidence of a positive linear MWF-age relationship among younger participants and minimal age differences in the remainder of the lifespan. Regarding geomT2IEW, a significant linear age × region interaction reflected positive linear age dependence mostly prominent in the regions with the highest density of small-caliber fibers-genu and splenium. In all, these two indicators characterize distinct attributes that are consistent with histology, which is a first. In addition, these results conform to rapid developmental progression of CC myelination leveling in middle age as well as age-related degradation of axon sheaths in older adults.
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Affiliation(s)
- Jonathan D Lynn
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
| | - Chaitali Anand
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
| | - Muzamil Arshad
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
| | - Roya Homayouni
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
- Department of Psychology, Wayne State University, Detroit MI 48201, USA
| | - David R Rosenberg
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
| | - Noa Ofen
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
- Department of Psychology, Wayne State University, Detroit MI 48201, USA
- Lifespan Cognitive Neuroscience, Merrill Palmer Skillman Institute, Wayne State University, Detroit MI 14195, USA
| | - Naftali Raz
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
- Department of Psychology, Wayne State University, Detroit MI 48201, USA
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin 14195, Germany
| | - Jeffrey A Stanley
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
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11
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Castro-Chavira SA, Vangberg TR, Gorecka MM, Vasylenko O, Waterloo K, Rodríguez-Aranda C. White matter correlates of gait perturbations resulting from spontaneous and lateralized attention in healthy older adults: A dual-task study. Exp Gerontol 2019; 128:110744. [PMID: 31634543 DOI: 10.1016/j.exger.2019.110744] [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: 07/22/2019] [Revised: 09/18/2019] [Accepted: 09/27/2019] [Indexed: 10/25/2022]
Abstract
To date the neural mechanisms behind gait perturbations caused by dual-task paradigms are still unknown. Therefore, the present study examined white matter correlates of gait perturbations caused by a dichotic listening task where spontaneous (free focus of attention) and lateralized attentional control (voluntary attention directed to right or left-ear) were tested. Fifty-nine right-handed, healthy older adults (59-88 years) were evaluated during single-task walking and three dual-task conditions. Dual-task costs were calculated for mean (DTCM) and coefficients of variation (DTCCoV) in gait speed, step length, stride length and step width. Volume, fractional anisotropy and mean diffusivity were estimated using global probabilistic tractography for the 18 major brain tracts and correlated with the DTCs. Data demonstrated that DTCs on gait speed and step length significantly correlated with white matter integrity and volume in various tracts. Perturbations on gait speed caused by spontaneous attention were related to frontal circuitry integrity including corpus callosum, while perturbations on gait speed and step length produced by voluntary lateralized attention were associated to tracts subserving visuomotor integration and frontal function.
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Affiliation(s)
- Susana A Castro-Chavira
- Faculty of Health Sciences, Department of Psychology, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Torgil R Vangberg
- Department of Radiology and Nuclear Medicine, University Hospital of North Norway, Tromsø, Norway
| | - Marta M Gorecka
- Faculty of Health Sciences, Department of Psychology, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Olena Vasylenko
- Faculty of Health Sciences, Department of Psychology, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Knut Waterloo
- Faculty of Health Sciences, Department of Psychology, University of Tromsø, The Arctic University of Norway, Tromsø, Norway; Department of Neurology, University Hospital of North Norway, Tromsø, Norway
| | - Claudia Rodríguez-Aranda
- Faculty of Health Sciences, Department of Psychology, University of Tromsø, The Arctic University of Norway, Tromsø, Norway.
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12
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Versace A, Graur S, Greenberg T, Lima Santos JP, Chase HW, Bonar L, Stiffler RS, Hudak R, Kim T, Yendiki A, Greenberg B, Rasmussen S, Liu H, Haber S, Phillips ML. Reduced focal fiber collinearity in the cingulum bundle in adults with obsessive-compulsive disorder. Neuropsychopharmacology 2019; 44:1182-1188. [PMID: 30802896 PMCID: PMC6784994 DOI: 10.1038/s41386-019-0353-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 01/17/2019] [Accepted: 02/04/2019] [Indexed: 12/31/2022]
Abstract
Obsessive-compulsive disorder (OCD) is a disabling condition, often associated with a chronic course. Given its role in attentional control, decision-making, and emotional regulation, the anterior cingulate cortex is considered to have a key role in the pathophysiology of the disorder. Notably, the cingulum bundle, being the major white matter tract connecting to this region, has been historically a target for the surgical treatment of intractable OCD. In this study, we aimed to identify the extent to which focal-more than diffuse-abnormalities in fiber collinearity of the cingulum bundle could distinguish 48 adults with OCD (mean age [SD] = 23.3 [4.5] years; F/M = 30/18) from 45 age- and sex-matched healthy control adults (CONT; mean age [SD] = 23.2 [3.8] years; F/M = 28/17) and further examine if these abnormalities correlated with symptom severity. Use of tract-profiles rather than a conventional diffusion imaging approach allowed us to characterize white matter microstructural properties along (100 segments), as opposed to averaging these measures across, the entire tract. To account for these 100 different segments of the cingulum bundle, a repeated measures analysis of variance revealed a main effect of group (OCD < CONT; F[1,87] = 5.3; P = 0.024) upon fractional anisotropy (FA, a measure of fiber collinearity and/or white matter integrity), in the cingulum bundle, bilaterally. Further analyses revealed that these abnormalities were focal (middle portion) within the left and right cingulum bundle, although did not correlate with symptom severity in OCD. Findings indicate that focal abnormalities in connectivity between the anterior cingulate cortex and other prefrontal cortical regions may represent neural mechanisms of OCD.
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Affiliation(s)
- A. Versace
- 0000 0004 1936 9000grid.21925.3dDepartment of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA USA
| | - S. Graur
- 0000 0004 1936 9000grid.21925.3dDepartment of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA USA
| | - T. Greenberg
- 0000 0004 1936 9000grid.21925.3dDepartment of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA USA
| | - J. P. Lima Santos
- 0000 0004 1936 9000grid.21925.3dDepartment of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA USA
| | - H. W. Chase
- 0000 0004 1936 9000grid.21925.3dDepartment of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA USA
| | - L. Bonar
- 0000 0004 1936 9000grid.21925.3dDepartment of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA USA
| | - R. S. Stiffler
- 0000 0004 1936 9000grid.21925.3dDepartment of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA USA
| | - R. Hudak
- 0000 0004 1936 9000grid.21925.3dDepartment of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA USA
| | - Tae Kim
- 0000 0004 1936 9000grid.21925.3dDepartment of Radiology, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA USA
| | - A. Yendiki
- 000000041936754Xgrid.38142.3cAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - B. Greenberg
- 0000 0004 0420 4094grid.413904.bDepartment of Psychiatry and Human Behavior, Brown Medical School, Butler Hospital and Providence VA Medical Center, Providence, RI USA
| | - S. Rasmussen
- 0000 0004 0420 4094grid.413904.bDepartment of Psychiatry and Human Behavior, Brown Medical School, Butler Hospital and Providence VA Medical Center, Providence, RI USA
| | - H. Liu
- 000000041936754Xgrid.38142.3cAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - S. Haber
- 0000 0004 1936 9166grid.412750.5Department of Pharmacology and Physiology, University of Rochester Medical Center, Rochester, NY 14642 USA
| | - M. L. Phillips
- 0000 0004 1936 9000grid.21925.3dDepartment of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA USA
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13
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Massaro AR, Pieri A. White matter hyperintensities and the pulsatility index: fellow travelers or partners in crime? ARQUIVOS DE NEURO-PSIQUIATRIA 2019; 77:297-299. [PMID: 31188991 DOI: 10.1590/0004-282x20190061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 05/20/2019] [Indexed: 06/09/2023]
Affiliation(s)
| | - Alexandre Pieri
- Instituto Dante Pazzanese de Cardiologia, São Paulo SP, Brasil
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14
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The many ages of man: diverse approaches to assessing ageing-related biological and psychological measures and their relationship to chronological age. Curr Opin Psychiatry 2019; 32:130-137. [PMID: 30461440 DOI: 10.1097/yco.0000000000000473] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
PURPOSE OF REVIEW Chronological age is a crude measure and may not be the best indicator of the ageing process. Establishing valid and reliable biomarkers to understand the true effect of ageing is of great interest. We provide an overview of biological and psychological characteristics that change with age and can potentially serve as markers of the ageing process, and discuss if an integration of these characteristics may more accurately measure the true age of a person. We also describe the clinicopathological continuum of these ageing-related changes. RECENT FINDINGS Ageing-related changes in the biological and psychological systems of the body have been studied to varying degrees and with differing emphases. Despite the development of ageing indices, there is no single indicator that can holistically estimate the ageing process. Differential ageing of bodily systems remains poorly understood, and valid methods have not been developed for composite markers of biological and psychological processes. SUMMARY The ageing process is complex and heterogeneous. Incorporating biological and psychological measures may improve accuracy in reflecting an individual's 'true age,' and elucidate why some people age successfully, whereas others show ageing-related decline and disease.
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15
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Sabisz A, Naumczyk P, Marcinkowska A, Graff B, Gąsecki D, Glińska A, Witkowska M, Jankowska A, Konarzewska A, Kwela J, Jodzio K, Szurowska E, Narkiewicz K. Aging and Hypertension - Independent or Intertwined White Matter Impairing Factors? Insights From the Quantitative Diffusion Tensor Imaging. Front Aging Neurosci 2019; 11:35. [PMID: 30837864 PMCID: PMC6389787 DOI: 10.3389/fnagi.2019.00035] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 02/05/2019] [Indexed: 01/17/2023] Open
Abstract
Aging disrupts white matter integrity, and so does continuous elevated blood pressure that accompanies hypertension (HTN). Yet, our understanding of the interrelationship between these factors is still limited. The study aimed at evaluating patterns of changes in diffusion parameters (as assessed by quantitative diffusion fiber tracking - qDTI) following both aging, and hypertension, as well as the nature of their linkage. 146 participants took part in the study: the control group (N = 61) and the patients with hypertension (N = 85), and were divided into three age subgroups (25-47, 48-56, 57-71 years). qDTI was used to calculate the values of fractional anisotropy, mean, radial and axial diffusivity in 20 main tracts of the brain. The effects of factors (aging and hypertension) on diffusion parameters of tracts were tested with a two-way ANOVA. In the right hemisphere there was no clear effect of the HTN, nor an interaction between the factors, though some age-related effects were observed. Contrary, in the left hemisphere both aging and hypertension contributed to the white matter decline, following a functional pattern. In the projection pathways and the fornix, HTN and aging played part independent of each other, whereas in association fibers and the corpus callosum if the hypertension effect was significant, an interaction was observed. HTN patients manifested faster decline of diffusion parameters but also reached a plateau earlier, with highest between-group differences noted in the middle-aged subgroup. Healthy and hypertensive participants have different brain aging patterns. The HTN is associated with acceleration of white matter integrity decline, observed mainly in association fibers of the left hemisphere.
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Affiliation(s)
- Agnieszka Sabisz
- Second Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland
| | | | - Anna Marcinkowska
- Second Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland
| | - Beata Graff
- Department of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland
| | - Dariusz Gąsecki
- Department of Neurology of Adults, Medical University of Gdańsk, Gdańsk, Poland
| | - Anna Glińska
- Second Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland
| | - Marta Witkowska
- Institute of Psychology, University of Gdańsk, Gdańsk, Poland
| | - Anna Jankowska
- Second Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland
| | | | - Jerzy Kwela
- Institute of Experimental Physics, University of Gdańsk, Gdańsk, Poland
| | | | - Edyta Szurowska
- Second Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland
| | - Krzysztof Narkiewicz
- Department of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland
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16
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Fan Q, Tian Q, Ohringer NA, Nummenmaa A, Witzel T, Tobyne SM, Klawiter EC, Mekkaoui C, Rosen BR, Wald LL, Salat DH, Huang SY. Age-related alterations in axonal microstructure in the corpus callosum measured by high-gradient diffusion MRI. Neuroimage 2019; 191:325-336. [PMID: 30790671 DOI: 10.1016/j.neuroimage.2019.02.036] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 01/26/2019] [Accepted: 02/14/2019] [Indexed: 12/14/2022] Open
Abstract
Cerebral white matter exhibits age-related degenerative changes during the course of normal aging, including decreases in axon density and alterations in axonal structure. Noninvasive approaches to measure these microstructural alterations throughout the lifespan would be invaluable for understanding the substrate and regional variability of age-related white matter degeneration. Recent advances in diffusion magnetic resonance imaging (MRI) have leveraged high gradient strengths to increase sensitivity toward axonal size and density in the living human brain. Here, we examined the relationship between age and indices of axon diameter and packing density using high-gradient strength diffusion MRI in 36 healthy adults (aged 22-72) in well-defined central white matter tracts in the brain. A recently validated method for inferring the effective axonal compartment size and packing density from diffusion MRI measurements acquired with 300 mT/m maximum gradient strength was applied to the in vivo human brain to obtain indices of axon diameter and density in the corpus callosum, its sub-regions, and adjacent anterior and posterior fibers in the forceps minor and forceps major. The relationships between the axonal metrics, corpus callosum area and regional gray matter volume were also explored. Results revealed a significant increase in axon diameter index with advancing age in the whole corpus callosum. Similar analyses in sub-regions of the corpus callosum showed that age-related alterations in axon diameter index and axon density were most pronounced in the genu of the corpus callosum and relatively absent in the splenium, in keeping with findings from previous histological studies. The significance of these correlations was mirrored in the forceps minor and forceps major, consistent with previously reported decreases in FA in the forceps minor but not in the forceps major with age. Alterations in the axonal imaging metrics paralleled decreases in corpus callosum area and regional gray matter volume with age. Among older adults, results from cognitive testing suggested an association between larger effective compartment size in the corpus callosum, particularly within the genu of the corpus callosum, and lower scores on the Montreal Cognitive Assessment, largely driven by deficits in short-term memory. The current study suggests that high-gradient diffusion MRI may be sensitive to the axonal substrate of age-related white matter degeneration reflected in traditional DTI metrics and provides further evidence for regionally selective alterations in white matter microstructure with advancing age.
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Affiliation(s)
- Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ned A Ohringer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Sean M Tobyne
- Harvard Medical School, Boston, MA, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Eric C Klawiter
- Harvard Medical School, Boston, MA, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Choukri Mekkaoui
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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17
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Bubb EJ, Metzler-Baddeley C, Aggleton JP. The cingulum bundle: Anatomy, function, and dysfunction. Neurosci Biobehav Rev 2018; 92:104-127. [PMID: 29753752 PMCID: PMC6090091 DOI: 10.1016/j.neubiorev.2018.05.008] [Citation(s) in RCA: 417] [Impact Index Per Article: 69.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 05/01/2018] [Accepted: 05/04/2018] [Indexed: 12/16/2022]
Abstract
The cingulum bundle is a prominent white matter tract that interconnects frontal, parietal, and medial temporal sites, while also linking subcortical nuclei to the cingulate gyrus. Despite its apparent continuity, the cingulum's composition continually changes as fibres join and leave the bundle. To help understand its complex structure, this review begins with detailed, comparative descriptions of the multiple connections comprising the cingulum bundle. Next, the impact of cingulum bundle damage in rats, monkeys, and humans is analysed. Despite causing extensive anatomical disconnections, cingulum bundle lesions typically produce only mild deficits, highlighting the importance of parallel pathways and the distributed nature of its various functions. Meanwhile, non-invasive imaging implicates the cingulum bundle in executive control, emotion, pain (dorsal cingulum), and episodic memory (parahippocampal cingulum), while clinical studies reveal cingulum abnormalities in numerous conditions, including schizophrenia, depression, post-traumatic stress disorder, obsessive compulsive disorder, autism spectrum disorder, Mild Cognitive Impairment, and Alzheimer's disease. Understanding the seemingly diverse contributions of the cingulum will require better ways of isolating pathways within this highly complex tract.
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Affiliation(s)
- Emma J Bubb
- School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, Wales, UK
| | | | - John P Aggleton
- School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, Wales, UK.
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18
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Bonifazi P, Erramuzpe A, Diez I, Gabilondo I, Boisgontier MP, Pauwels L, Stramaglia S, Swinnen SP, Cortes JM. Structure-function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging. Hum Brain Mapp 2018; 39:4663-4677. [PMID: 30004604 DOI: 10.1002/hbm.24312] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 06/27/2018] [Accepted: 06/28/2018] [Indexed: 12/15/2022] Open
Abstract
Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age-an age estimator resulting from a multi-scale methodology applied to the structure-function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto-striato-thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural-functional connectivity patterns correlating to other biomarkers than ChA.
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Affiliation(s)
- Paolo Bonifazi
- Biocruces Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
| | | | - Ibai Diez
- Biocruces Health Research Institute, Barakaldo, Spain
| | | | - Matthieu P Boisgontier
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Lisa Pauwels
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Universita di Bari, and INFN, Sezione di Bari, Italy
| | - Stephan P Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium.,Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Jesus M Cortes
- Biocruces Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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