1
|
Shir D, Graff-Radford J, Fought AJ, Lesnick TG, Przybelski SA, Vassilaki M, Lowe VJ, Knopman DS, Machulda MM, Petersen RC, Jack CR, Mielke MM, Vemuri P. Complex relationships of socioeconomic status with vascular and Alzheimer's pathways on cognition. Neuroimage Clin 2024; 43:103634. [PMID: 38909419 DOI: 10.1016/j.nicl.2024.103634] [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: 01/12/2024] [Revised: 06/12/2024] [Accepted: 06/14/2024] [Indexed: 06/25/2024]
Abstract
INTRODUCTION AD and CVD, which frequently co-occur, are leading causes of age-related cognitive decline. We assessed how demographic factors, socioeconomic status (SES) as indicated by education and occupation, vascular risk factors, and a range of biomarkers associated with both CVD (including white matter hyperintensities [WMH], diffusion MRI abnormalities, infarctions, and microbleeds) and AD (comprising amyloid-PET and tau-PET) collectively influence cognitive function. METHODS In this cross-sectional population study, structural equation models were utilized to understand these associations in 449 participants (mean age (SD) = 74.5 (8.4) years; 56% male; 7.5% cognitively impaired). RESULTS (1) Higher SES had a protective effect on cognition with mediation through the vascular pathway. (2) The effect of amyloid directly on cognition and through tau was 11-fold larger than the indirect effect of amyloid on cognition through WMH. (3) There is a significant effect of vascular risk on tau deposition. DISCUSSION The utilized biomarkers captured the impact of CVD and AD on cognition. The overall effect of vascular risk and SES on these biomarkers are complex and need further investigation.
Collapse
Affiliation(s)
- Dror Shir
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Angela J Fought
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, 55905 USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Michelle M Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA; Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
| | | |
Collapse
|
2
|
Tranfa M, Lorenzini L, Collij LE, Vállez García D, Ingala S, Pontillo G, Pieperhoff L, Maranzano A, Wolz R, Haller S, Blennow K, Frisoni G, Sudre CH, Chételat G, Ewers M, Payoux P, Waldman A, Martinez‐Lage P, Schwarz AJ, Ritchie CW, Wardlaw JM, Gispert JD, Brunetti A, Mutsaerts HJMM, Wink AM, Barkhof F. Alzheimer's Disease and Small Vessel Disease Differentially Affect White Matter Microstructure. Ann Clin Transl Neurol 2024; 11:1541-1556. [PMID: 38757392 PMCID: PMC11187968 DOI: 10.1002/acn3.52071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 04/09/2024] [Indexed: 05/18/2024] Open
Abstract
OBJECTIVE Alzheimer's disease (AD) and cerebral small vessel disease (cSVD), the two most common causes of dementia, are characterized by white matter (WM) alterations diverging from the physiological changes occurring in healthy aging. Diffusion tensor imaging (DTI) is a valuable tool to quantify WM integrity non-invasively and identify the determinants of such alterations. Here, we investigated main effects and interactions of AD pathology, APOE-ε4, cSVD, and cardiovascular risk on spatial patterns of WM alterations in non-demented older adults. METHODS Within the prospective European Prevention of Alzheimer's Dementia study, we selected 606 participants (64.9 ± 7.2 years, 376 females) with baseline cerebrospinal fluid samples of amyloid β1-42 and p-Tau181 and MRI scans, including DTI scans. Longitudinal scans (mean follow-up time = 1.3 ± 0.5 years) were obtained in a subset (n = 223). WM integrity was assessed by extracting fractional anisotropy and mean diffusivity in relevant tracts. To identify the determinants of WM disruption, we performed a multimodel inference to identify the best linear mixed-effects model for each tract. RESULTS AD pathology, APOE-ε4, cSVD burden, and cardiovascular risk were all associated with WM integrity within several tracts. While limbic tracts were mainly impacted by AD pathology and APOE-ε4, commissural, associative, and projection tract integrity was more related to cSVD burden and cardiovascular risk. AD pathology and cSVD did not show any significant interaction effect. INTERPRETATION Our results suggest that AD pathology and cSVD exert independent and spatially different effects on WM microstructure, supporting the role of DTI in disease monitoring and suggesting independent targets for preventive medicine approaches.
Collapse
Affiliation(s)
- Mario Tranfa
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
| | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
- Clinical Memory Research Unit, Department of Clinical SciencesLund UniversityMalmöSweden
| | - David Vállez García
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
- Department of RadiologyCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Cerebriu A/SCopenhagenDenmark
| | - Giuseppe Pontillo
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
| | - Leonard Pieperhoff
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
| | - Alessio Maranzano
- Department of Neurology and Laboratory of NeuroscienceIRCCS Istituto Auxologico ItalianoMilanItaly
| | | | - Sven Haller
- CIMC ‐ Centre d'Imagerie Médicale de CornavinGenevaSwitzerland
- Department of Surgical Sciences, RadiologyUppsala UniversityUppsalaSweden
- Department of Radiology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at the University of GothenburgGothenburgSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Giovanni Frisoni
- Laboratory Alzheimer's Neuroimaging & EpidemiologyIRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
- University Hospitals and University of GenevaGenevaSwitzerland
| | - Carole H. Sudre
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at the University of GothenburgGothenburgSweden
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC)University College London (UCL)LondonUK
- MRC Unit for Lifelong Health & Ageing at UCLUniversity College LondonLondonUK
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Gael Chételat
- Normandie Univ, Unicaen, Inserm, U1237, PhIND “Physiopathology and Imaging of Neurological Disorders”, institut Blood‐and‐Brain @ Caen‐Normandie, CyceronUniversité de NormandieCaenFrance
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
| | - Pierre Payoux
- Department of Nuclear MedicineToulouse University HospitalToulouseFrance
- ToNIC, Toulouse NeuroImaging CenterUniversity of Toulouse, Inserm, UPSToulouseFrance
| | - Adam Waldman
- Centre for Clinical Brain SciencesThe University of EdinburghEdinburghUK
- Department of MedicineImperial College LondonLondonUK
| | - Pablo Martinez‐Lage
- Centro de Investigación y Terapias Avanzadas, Neurología, CITA‐Alzheimer FoundationSan SebastiánSpain
| | - Adam J. Schwarz
- Takeda Pharmaceuticals, Ltd.CambridgeMassachusettsUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Craig W. Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General HospitalUniversity of EdinburghEdinburghUK
- Brain Health ScotlandEdinburghUK
| | - Joanna M. Wardlaw
- Centre for Clinical Brain SciencesThe University of EdinburghEdinburghUK
- UK Dementia Research Institute Centre at the University of EdinburghEdinburghUK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
- CIBER Bioingeniería, Biomateriales y Nanomedicina (CIBER‐BBN)MadridSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - Arturo Brunetti
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | - Henk J. M. M. Mutsaerts
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
- Ghent Institute for Functional and Metabolic Imaging (GIfMI)Ghent UniversityGhentBelgium
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Institute of Neurology and Healthcare EngineeringUniversity College LondonLondonUK
| |
Collapse
|
3
|
Ter Telgte A, Duering M. Cerebral Small Vessel Disease: Advancing Knowledge With Neuroimaging. Stroke 2024; 55:1686-1688. [PMID: 38328947 DOI: 10.1161/strokeaha.123.044294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Affiliation(s)
- Annemieke Ter Telgte
- VASCage-Center on Clinical Stroke Research, Innsbruck, Austria (A.t.T.)
- Department of Neurology, Medical University of Innsbruck, Austria (A.t.T.)
| | - Marco Duering
- Institute for Stroke and Dementia Research, LMU University Hospital, Munich, Germany (M.D.)
- Medical Image Analysis Center and Department of Biomedical Engineering, University of Basel, Switzerland (M.D.)
| |
Collapse
|
4
|
Wei YC, Kung YC, Lin CP, Chen CK, Lin C, Tseng RY, Chen YL, Huang WY, Chen PY, Chong ST, Shyu YC, Chang WC, Yeh CH. White matter alterations and their associations with biomarkers and behavior in subjective cognitive decline individuals: a fixel-based analysis. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2024; 20:12. [PMID: 38778325 PMCID: PMC11110460 DOI: 10.1186/s12993-024-00238-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 05/04/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Subjective cognitive decline (SCD) is an early stage of dementia linked to Alzheimer's disease pathology. White matter changes were found in SCD using diffusion tensor imaging, but there are known limitations in voxel-wise tensor-based methods. Fixel-based analysis (FBA) can help understand changes in white matter fibers and how they relate to neurodegenerative proteins and multidomain behavior data in individuals with SCD. METHODS Healthy adults with normal cognition were recruited in the Northeastern Taiwan Community Medicine Research Cohort in 2018-2022 and divided into SCD and normal control (NC). Participants underwent evaluations to assess cognitive abilities, mental states, physical activity levels, and susceptibility to fatigue. Neurodegenerative proteins were measured using an immunomagnetic reduction technique. Multi-shell diffusion MRI data were collected and analyzed using whole-brain FBA, comparing results between groups and correlating them with multidomain assessments. RESULTS The final enrollment included 33 SCD and 46 NC participants, with no significant differences in age, sex, or education between the groups. SCD had a greater fiber-bundle cross-section than NC (pFWE < 0.05) at bilateral frontal superior longitudinal fasciculus II (SLFII). These white matter changes correlate negatively with plasma Aβ42 level (r = -0.38, p = 0.01) and positively with the AD8 score for subjective cognitive complaints (r = 0.42, p = 0.004) and the Hamilton Anxiety Rating Scale score for the degree of anxiety (Ham-A, r = 0.35, p = 0.019). The dimensional analysis of FBA metrics and blood biomarkers found positive correlations of plasma neurofilament light chain with fiber density at the splenium of corpus callosum (pFWE < 0.05) and with fiber-bundle cross-section at the right thalamus (pFWE < 0.05). Further examination of how SCD grouping interacts between the correlations of FBA metrics and multidomain assessments showed interactions between the fiber density at the corpus callosum with letter-number sequencing cognitive score (pFWE < 0.01) and with fatigue to leisure activities (pFWE < 0.05). CONCLUSION Based on FBA, our investigation suggests white matter structural alterations in SCD. The enlargement of SLFII's fiber cross-section is linked to plasma Aβ42 and neuropsychiatric symptoms, which suggests potential early axonal dystrophy associated with Alzheimer's pathology in SCD. The splenium of the corpus callosum is also a critical region of axonal degeneration and cognitive alteration for SCD.
Collapse
Affiliation(s)
- Yi-Chia Wei
- Department of Neurology, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Yi-Chia Kung
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Chih-Ken Chen
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
| | - Chemin Lin
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
| | - Rung-Yu Tseng
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, 333, Taiwan
| | - Yao-Liang Chen
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, 333, Taiwan
- Department of Radiology, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
| | - Wen-Yi Huang
- Department of Neurology, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan
| | - Pin-Yuan Chen
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
| | - Shin-Tai Chong
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Yu-Chiau Shyu
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, 204, Taiwan
- Department of Nursing, Chang Gung University of Science and Technology, Taoyuan, 333, Taiwan
| | - Wei-Chou Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114, Taiwan
| | - Chun-Hung Yeh
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, 333, Taiwan.
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, 333, Taiwan.
| |
Collapse
|
5
|
Ahmadi K, Pereira JB, van Westen D, Pasternak O, Zhang F, Nilsson M, Stomrud E, Spotorno N, Hansson O. Fixel-Based Analysis Reveals Tau-Related White Matter Changes in Early Stages of Alzheimer's Disease. J Neurosci 2024; 44:e0538232024. [PMID: 38565289 PMCID: PMC11063818 DOI: 10.1523/jneurosci.0538-23.2024] [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: 03/24/2023] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Several studies have shown white matter (WM) abnormalities in Alzheimer's disease (AD) using diffusion tensor imaging (DTI). Nonetheless, robust characterization of WM changes has been challenging due to the methodological limitations of DTI. We applied fixel-based analyses (FBA) to examine microscopic differences in fiber density (FD) and macroscopic changes in fiber cross-section (FC) in early stages of AD (N = 393, 212 females). FBA was also compared with DTI, free-water corrected (FW)-DTI and diffusion kurtosis imaging (DKI). We further investigated the correlation of FBA and tensor-derived metrics with AD pathology and cognition. FBA metrics were decreased in the entire cingulum bundle, uncinate fasciculus and anterior thalamic radiations in Aβ-positive patients with mild cognitive impairment compared to control groups. Metrics derived from DKI, and FW-DTI showed similar alterations whereas WM degeneration detected by DTI was more widespread. Tau-PET uptake in medial temporal regions was only correlated with reduced FC mainly in the parahippocampal cingulum in Aβ-positive individuals. This tau-related WM alteration was also associated with impaired memory. Despite the spatially extensive between-group differences in DTI-metrics, the link between WM and tau aggregation was only revealed using FBA metrics implying high sensitivity but low specificity of DTI-based measures in identifying subtle tau-related WM degeneration. No relationship was found between amyloid load and any diffusion-MRI measures. Our results indicate that early tau-related WM alterations in AD are due to macrostructural changes specifically captured by FBA metrics. Thus, future studies assessing the effects of AD pathology in WM tracts should consider using FBA metrics.
Collapse
Affiliation(s)
- Khazar Ahmadi
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
- Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum 44801, Germany
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
- Division of Neuro, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm 17176, Sweden
| | - Danielle van Westen
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund 22185, Sweden
| | - Ofer Pasternak
- Departments of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114
| | - Fan Zhang
- Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Markus Nilsson
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund 22185, Sweden
- Department of Medical Radiation Physics, Lund University, Lund 22185, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
- Memory Clinic, Skåne University Hospital, Malmö 21428, Sweden
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
- Memory Clinic, Skåne University Hospital, Malmö 21428, Sweden
| |
Collapse
|
6
|
Zarkali A, Hannaway N, McColgan P, Heslegrave AJ, Veleva E, Laban R, Zetterberg H, Lees AJ, Fox NC, Weil RS. Neuroimaging and plasma evidence of early white matter loss in Parkinson's disease with poor outcomes. Brain Commun 2024; 6:fcae130. [PMID: 38715714 PMCID: PMC11073930 DOI: 10.1093/braincomms/fcae130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/26/2024] [Accepted: 04/23/2024] [Indexed: 06/30/2024] Open
Abstract
Parkinson's disease is a common and debilitating neurodegenerative disorder, with over half of patients progressing to postural instability, dementia or death within 10 years of diagnosis. However, the onset and rate of progression to poor outcomes is highly variable, underpinned by heterogeneity in underlying pathological processes. Quantitative and sensitive measures predicting poor outcomes will be critical for targeted treatment, but most studies to date have been limited to a single modality or assessed patients with established cognitive impairment. Here, we used multimodal neuroimaging and plasma measures in 98 patients with Parkinson's disease and 28 age-matched controls followed up over 3 years. We examined: grey matter (cortical thickness and subcortical volume), white matter (fibre cross-section, a measure of macrostructure; and fibre density, a measure of microstructure) at whole-brain and tract level; structural and functional connectivity; and plasma levels of neurofilament light chain and phosphorylated tau 181. We evaluated relationships with subsequent poor outcomes, defined as development of mild cognitive impairment, dementia, frailty or death at any time during follow-up, in people with Parkinson's disease. We show that extensive white matter macrostructural changes are already evident at baseline assessment in people with Parkinson's disease who progress to poor outcomes (n = 31): with up to 19% reduction in fibre cross-section in multiple tracts, and a subnetwork of reduced structural connectivity strength, particularly involving connections between right frontoparietal and left frontal, right frontoparietal and left parietal and right temporo-occipital and left parietal modules. In contrast, grey matter volumes and functional connectivity were preserved in people with Parkinson's disease with poor outcomes. Neurofilament light chain, but not phosphorylated tau 181 levels were increased in people with Parkinson's disease with poor outcomes, and correlated with white matter loss. These findings suggest that imaging sensitive to white matter macrostructure and plasma neurofilament light chain may be useful early markers of poor outcomes in Parkinson's disease. As new targeted treatments for neurodegenerative disease are emerging, these measures show important potential to aid patient selection for treatment and improve stratification for clinical trials.
Collapse
Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Naomi Hannaway
- Dementia Research Centre, Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Peter McColgan
- Huntington’s Disease Centre, Institute of Neurology, University College London, London WC1B 5EH, UK
| | - Amanda J Heslegrave
- UK DRI Fluid Biomarker Lab and Biomarker Factory, University College London, London WC1E 6BT, UK
| | - Elena Veleva
- UK DRI Fluid Biomarker Lab and Biomarker Factory, University College London, London WC1E 6BT, UK
| | - Rhiannon Laban
- UK DRI Fluid Biomarker Lab and Biomarker Factory, University College London, London WC1E 6BT, UK
| | - Henrik Zetterberg
- Dementia Research Centre, Institute of Neurology, University College London, London WC1N 3AR, UK
- UK DRI Fluid Biomarker Lab and Biomarker Factory, University College London, London WC1E 6BT, UK
| | - Andrew J Lees
- Reta Lila Weston Institute of Neurological Studies, University College London, London WC1N 1PJ, UK
| | - Nick C Fox
- Dementia Research Centre, Institute of Neurology, University College London, London WC1N 3AR, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London WC1N 3BG, UK
| | - Rimona S Weil
- Dementia Research Centre, Institute of Neurology, University College London, London WC1N 3AR, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London WC1N 3BG, UK
- Movement Disorders Centre, University College London, London WC1N 3BG, UK
- The Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3AR, UK
| |
Collapse
|
7
|
Feng L, Gao L. The role of neurovascular coupling dysfunction in cognitive decline of diabetes patients. Front Neurosci 2024; 18:1375908. [PMID: 38576869 PMCID: PMC10991808 DOI: 10.3389/fnins.2024.1375908] [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: 01/24/2024] [Accepted: 03/05/2024] [Indexed: 04/06/2024] Open
Abstract
Neurovascular coupling (NVC) is an important mechanism to ensure adequate blood supply to active neurons in the brain. NVC damage can lead to chronic impairment of neuronal function. Diabetes is characterized by high blood sugar and is considered an important risk factor for cognitive impairment. In this review, we provide fMRI evidence of NVC damage in diabetic patients with cognitive decline. Combined with the exploration of the major mechanisms and signaling pathways of NVC, we discuss the effects of chronic hyperglycemia on the cellular structure of NVC signaling, including key receptors, ion channels, and intercellular connections. Studying these diabetes-related changes in cell structure will help us understand the underlying causes behind diabetes-induced NVC damage and early cognitive decline, ultimately helping to identify the most effective drug targets for treatment.
Collapse
Affiliation(s)
| | - Ling Gao
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan, China
| |
Collapse
|
8
|
Rubinski A, Dewenter A, Zheng L, Franzmeier N, Stephenson H, Deming Y, Duering M, Gesierich B, Denecke J, Pham AV, Bendlin B, Ewers M. Florbetapir PET-assessed demyelination is associated with faster tau accumulation in an APOE ε4-dependent manner. Eur J Nucl Med Mol Imaging 2024; 51:1035-1049. [PMID: 38049659 PMCID: PMC10881623 DOI: 10.1007/s00259-023-06530-8] [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: 05/18/2023] [Accepted: 11/14/2023] [Indexed: 12/06/2023]
Abstract
PURPOSE The main objectives were to test whether (1) a decrease in myelin is associated with enhanced rate of fibrillar tau accumulation and cognitive decline in Alzheimer's disease, and (2) whether apolipoprotein E (APOE) ε4 genotype is associated with worse myelin decrease and thus tau accumulation. METHODS To address our objectives, we repurposed florbetapir-PET as a marker of myelin in the white matter (WM) based on previous validation studies showing that beta-amyloid (Aβ) PET tracers bind to WM myelin. We assessed 43 Aβ-biomarker negative (Aβ-) cognitively normal participants and 108 Aβ+ participants within the AD spectrum with florbetapir-PET at baseline and longitudinal flortaucipir-PET as a measure of fibrillar tau (tau-PET) over ~ 2 years. In linear regression analyses, we tested florbetapir-PET in the whole WM and major fiber tracts as predictors of tau-PET accumulation in a priori defined regions of interest (ROIs) and fiber-tract projection areas. In mediation analyses we tested whether tau-PET accumulation mediates the effect of florbetapir-PET in the whole WM on cognition. Finally, we assessed the role of myelin alteration on the association between APOE and tau-PET accumulation. RESULTS Lower florbetapir-PET in the whole WM or at a given fiber tract was predictive of faster tau-PET accumulation in Braak stages or the connected grey matter areas in Aβ+ participants. Faster tau-PET accumulation in higher cortical brain areas mediated the association between a decrease in florbetapir-PET in the WM and a faster rate of decline in global cognition and episodic memory. APOE ε4 genotype was associated with a worse decrease in the whole WM florbetapir-PET and thus enhanced tau-PET accumulation. CONCLUSION Myelin alterations are associated in an APOE ε4 dependent manner with faster tau progression and cognitive decline, and may therefore play a role in the etiology of AD.
Collapse
Affiliation(s)
- Anna Rubinski
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Anna Dewenter
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Lukai Zheng
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Henry Stephenson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin- Madison, Madison, WI, USA
| | - Yuetiva Deming
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin- Madison, Madison, WI, USA
| | - Marco Duering
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
- Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Benno Gesierich
- Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Jannis Denecke
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
| | - An-Vi Pham
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Barbara Bendlin
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin- Madison, Madison, WI, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
| |
Collapse
|
9
|
Rebello CJ. Triglyceride-Glucose Index and Cognitive Impairment: Is Cerebral Microvascular Pathology a Link? Am J Geriatr Psychiatry 2024; 32:163-165. [PMID: 37951818 PMCID: PMC10911067 DOI: 10.1016/j.jagp.2023.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 11/14/2023]
|
10
|
Cheng Y, Lin L, Jiang S, Huang P, Zhang J, Xin J, Xu H, Wang Y, Pan X. Aberrant microstructural integrity of white matter in mild and severe orthostatic hypotension: A NODDI study. CNS Neurosci Ther 2024; 30:e14586. [PMID: 38421091 PMCID: PMC10851318 DOI: 10.1111/cns.14586] [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: 10/08/2023] [Revised: 12/11/2023] [Accepted: 12/20/2023] [Indexed: 03/02/2024] Open
Abstract
OBJECTIVE Scarce evidence is available to elucidate the association between the abnormal microstructure of white matter (WM) and cognitive performance in patients with orthostatic hypotension (OH). This study investigated the microstructural integrity of WM in patients with mild OH (MOH) and severe OH (SOH) and evaluated the association of abnormal WM microstructure with the broad cognitive domains and cognition-related plasma biomarkers. METHODS Our study included 72 non-OH (NOH), 17 MOH, and 11 SOH participants. Across the groups, the WM integrity was analyzed by neurite orientation dispersion and density imaging (NODDI), and differences in WM microstructure were evaluated by nonparametric tests and post hoc models. The correlations between WM microstructure and broad cognitive domains and cognition-related plasma biomarkers were assessed by Spearman's correlation analysis. RESULTS The abnormal WM microstructure was localized to the WM fiber bundles in MOH patients but distributed widely in SOH cohorts (p < 0.05). Further analysis showed that the neurite density index of the left cingulate gyrus was negatively associated with amyloid β-40, glial fibrillary acidic protein, neurofilament light chain, phospho-tau181 (p < 0.05) but positively with global cognitive function (MOCA, MMSE, AER-III), memory, attention, language, language fluency, visuospatial function and amyloid β-40 / amyloid β-42 (p < 0.05). Additionally, other abnormal WM microstructures of OH were associated with broad cognitive domains and cognition-related plasma biomarkers to varying degrees. CONCLUSION The findings evidence that abnormal WM microstructures may present themselves as early as in the MOH phase and that these structural abnormalities are associated with cognitive functions and cognition-related plasma biomarkers.
Collapse
Affiliation(s)
- Yingzhe Cheng
- Department of Neurology, Center for Cognitive NeurologyFujian Medical University Union HospitalFuzhou CityChina
- Fujian Institute of GeriatricsFujian Medical University Union HospitalFuzhou CityChina
- Institute of Clinical NeurologyFujian Medical UniversityFuzhou CityChina
- Fujian Key Laboratory of Molecular NeurologyFujian Medical UniversityFuzhou CityChina
| | - Lin Lin
- Department of Neurology, Center for Cognitive NeurologyFujian Medical University Union HospitalFuzhou CityChina
- Fujian Institute of GeriatricsFujian Medical University Union HospitalFuzhou CityChina
- Institute of Clinical NeurologyFujian Medical UniversityFuzhou CityChina
- Fujian Key Laboratory of Molecular NeurologyFujian Medical UniversityFuzhou CityChina
| | - Shaofan Jiang
- Department of RadiologyFujian Medical University Union HospitalFuzhou CityChina
- Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for TumorsFujian Medical UniversityFuzhou CityChina
| | - Peilin Huang
- Department of Neurology, Center for Cognitive NeurologyFujian Medical University Union HospitalFuzhou CityChina
- Fujian Institute of GeriatricsFujian Medical University Union HospitalFuzhou CityChina
- Institute of Clinical NeurologyFujian Medical UniversityFuzhou CityChina
- Fujian Key Laboratory of Molecular NeurologyFujian Medical UniversityFuzhou CityChina
| | - Jiejun Zhang
- Department of Neurology, Center for Cognitive NeurologyFujian Medical University Union HospitalFuzhou CityChina
- Fujian Institute of GeriatricsFujian Medical University Union HospitalFuzhou CityChina
- Institute of Clinical NeurologyFujian Medical UniversityFuzhou CityChina
- Fujian Key Laboratory of Molecular NeurologyFujian Medical UniversityFuzhou CityChina
- Center for GeriatricsHainan General HospitalHainanChina
| | - Jiawei Xin
- Department of Neurology, Center for Cognitive NeurologyFujian Medical University Union HospitalFuzhou CityChina
- Fujian Institute of GeriatricsFujian Medical University Union HospitalFuzhou CityChina
- Institute of Clinical NeurologyFujian Medical UniversityFuzhou CityChina
- Fujian Key Laboratory of Molecular NeurologyFujian Medical UniversityFuzhou CityChina
| | - Haibin Xu
- Fujian Medical UniversityFuzhou CityChina
| | - Yanping Wang
- Department of EndocrinologyFujian Medical University Union HospitalFuzhou CityChina
| | - Xiaodong Pan
- Department of Neurology, Center for Cognitive NeurologyFujian Medical University Union HospitalFuzhou CityChina
- Fujian Institute of GeriatricsFujian Medical University Union HospitalFuzhou CityChina
- Institute of Clinical NeurologyFujian Medical UniversityFuzhou CityChina
- Fujian Key Laboratory of Molecular NeurologyFujian Medical UniversityFuzhou CityChina
| |
Collapse
|
11
|
Vogel JW, Corriveau-Lecavalier N, Franzmeier N, Pereira JB, Brown JA, Maass A, Botha H, Seeley WW, Bassett DS, Jones DT, Ewers M. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat Rev Neurosci 2023; 24:620-639. [PMID: 37620599 DOI: 10.1038/s41583-023-00731-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
Collapse
Affiliation(s)
- Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden.
| | - Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Acadamy, University of Gothenburg, Mölndal and Gothenburg, Sweden
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institute, Stockholm, Sweden
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Dani S Bassett
- Departments of Bioengineering, Electrical and Systems Engineering, Physics and Astronomy, Neurology and Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
| |
Collapse
|
12
|
Alexopoulos GS. What is the Value of MRI-Based Models of Geriatric Psychopathology Now That MRI Findings are Challenged? A View From Epistemology. Am J Geriatr Psychiatry 2023; 31:553-558. [PMID: 37291021 DOI: 10.1016/j.jagp.2023.05.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 05/30/2023] [Accepted: 05/31/2023] [Indexed: 06/10/2023]
Affiliation(s)
- George S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY.
| |
Collapse
|
13
|
Duering M, Biessels GJ, Brodtmann A, Chen C, Cordonnier C, de Leeuw FE, Debette S, Frayne R, Jouvent E, Rost NS, Ter Telgte A, Al-Shahi Salman R, Backes WH, Bae HJ, Brown R, Chabriat H, De Luca A, deCarli C, Dewenter A, Doubal FN, Ewers M, Field TS, Ganesh A, Greenberg S, Helmer KG, Hilal S, Jochems ACC, Jokinen H, Kuijf H, Lam BYK, Lebenberg J, MacIntosh BJ, Maillard P, Mok VCT, Pantoni L, Rudilosso S, Satizabal CL, Schirmer MD, Schmidt R, Smith C, Staals J, Thrippleton MJ, van Veluw SJ, Vemuri P, Wang Y, Werring D, Zedde M, Akinyemi RO, Del Brutto OH, Markus HS, Zhu YC, Smith EE, Dichgans M, Wardlaw JM. Neuroimaging standards for research into small vessel disease-advances since 2013. Lancet Neurol 2023; 22:602-618. [PMID: 37236211 DOI: 10.1016/s1474-4422(23)00131-x] [Citation(s) in RCA: 121] [Impact Index Per Article: 121.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/03/2023] [Accepted: 03/28/2023] [Indexed: 05/28/2023]
Abstract
Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.
Collapse
Affiliation(s)
- Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Medical Image Analysis Center, University of Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Geert Jan Biessels
- Department of Neurology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher Chen
- Department of Pharmacology, Memory Aging and Cognition Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Psychological Medicine, Memory Aging and Cognition Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Charlotte Cordonnier
- Université de Lille, INSERM, CHU Lille, U1172-Lille Neuroscience and Cognition (LilNCog), Lille, France
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neuroscience, Radboudumc, Nijmegen, Netherlands
| | - Stéphanie Debette
- Bordeaux Population Health Research Center, University of Bordeaux, INSERM, UMR 1219, Bordeaux, France; Department of Neurology, Institute for Neurodegenerative Diseases, CHU de Bordeaux, Bordeaux, France
| | - Richard Frayne
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, University of Calgary, Calgary, AB, Canada
| | - Eric Jouvent
- AP-HP, Lariboisière Hospital, Translational Neurovascular Centre, FHU NeuroVasc, Université Paris Cité, Paris, France; Université Paris Cité, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Natalia S Rost
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Walter H Backes
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, Netherlands; School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea; Cerebrovascular Disease Center, Seoul National University Bundang Hospital, Seongn-si, South Korea
| | - Rosalind Brown
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Hugues Chabriat
- Centre Neurovasculaire Translationnel, CERVCO, INSERM U1141, FHU NeuroVasc, Université Paris Cité, Paris, France
| | - Alberto De Luca
- Image Sciences Institute, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Charles deCarli
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA, USA
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Thalia S Field
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada; Vancouver Stroke Program, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Aravind Ganesh
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | - Steven Greenberg
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Karl G Helmer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Athinoula A Martinos Center for Biomedical Imaging, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Saima Hilal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Angela C C Jochems
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Hanna Jokinen
- Division of Neuropsychology, HUS Neurocenter, Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Hugo Kuijf
- Image Sciences Institute, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bonnie Y K Lam
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Margaret KL Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jessica Lebenberg
- AP-HP, Lariboisière Hospital, Translational Neurovascular Centre, FHU NeuroVasc, Université Paris Cité, Paris, France; Université Paris Cité, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Bradley J MacIntosh
- Sandra E Black Centre for Brain Resilience and Repair, Hurvitz Brain Sciences, Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Computational Radiology and Artificial Intelligence Unit, Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Pauline Maillard
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA, USA
| | - Vincent C T Mok
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Margaret KL Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Leonardo Pantoni
- Department of Biomedical and Clinical Science, University of Milan, Milan, Italy
| | - Salvatore Rudilosso
- Comprehensive Stroke Center, Department of Neuroscience, Hospital Clinic and August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Neurology, Boston University Medical Center, Boston, MA, USA; Framingham Heart Study, Framingham, MA, USA
| | - Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Colin Smith
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Julie Staals
- School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, Netherlands; Department of Neurology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | | | | | - Yilong Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - David Werring
- Stroke Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Marialuisa Zedde
- Neurology Unit, Stroke Unit, Department of Neuromotor Physiology and Rehabilitation, Azienda Unità Sanitaria-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Rufus O Akinyemi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oscar H Del Brutto
- School of Medicine and Research Center, Universidad de Especialidades Espiritu Santo, Ecuador
| | - Hugh S Markus
- Stroke Research Group, Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Eric E Smith
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; German Centre for Cardiovascular Research (DZHK), Munich, Germany
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK.
| |
Collapse
|
14
|
Tian J, Raghavan S, Reid RI, Przybelski SA, Lesnick TG, Gebre RK, Graff-Radford J, Schwarz CG, Lowe VJ, Kantarci K, Knopman DS, Petersen RC, Jack CR, Vemuri P. White Matter Degeneration Pathways Associated With Tau Deposition in Alzheimer Disease. Neurology 2023; 100:e2269-e2278. [PMID: 37068958 PMCID: PMC10259272 DOI: 10.1212/wnl.0000000000207250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 02/16/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The dynamics of white matter (WM) changes are understudied in Alzheimer disease (AD). Our goal was to study the association between flortaucipir PET and WM health using neurite orientation dispersion and density imaging (NODDI) and evaluate its association with cognitive performance. Specifically, we focused on NODDI's Neurite Density Index (NDI), which aids in capturing axonal degeneration in WM and has greater specificity than single-shell diffusion MRI methods. METHOD We estimated regional flortaucipir PET standard uptake value ratios (SUVRs) from 3 regions corresponding to Braak stage I, III/IV, and V/VI to capture the spatial distribution pattern of the 3R/4R tau in AD. Then, we evaluated the associations between these measurements and NDIs in 29 candidate WM tracts using Pearson correlation and multiple regression models. RESULTS Based on 223 participants who were amyloid positive (mean age of 78 years and 57.0% male, 119 cognitively unimpaired, 56 mild cognitive impairment, and 48 dementia), the results showed that WM tracts NDI decreased with increasing regional Braak tau SUVRs. Of all the significant WM tracts, the uncinate fasciculus (r = -0.274 for Braak I, -0.311 for Braak III/IV, and -0.292 for Braak V/VI, p < 0.05) and cingulum adjoining hippocampus (r = -0.274, -0.288, -0.233, p < 0.05), both tracts anatomically connected to areas of early tau deposition, were consistently found to be within the top 5 distinguishing WM tracts associated with flortaucipir SUVRs. The increase in tau deposition measurable outside the medial temporal lobes in Braak III-VI was associated with a decrease in NDI in the middle and inferior temporal WM tracts. For cognitive performance, WM NDI had similar coefficients of determination (r 2 = 31%) as regional Braak flortaucipir SUVRs (29%), and together WM NDI and regional Braak flortaucipir SUVRs explained 46% of the variance in cognitive performance. DISCUSSION We found spatially dependent WM degeneration associated with regional flortaucipir SUVRs in Braak stages, suggesting a spatial pattern in WM damage. NDI, a specific marker of axonal density, provides complementary information about disease staging and progression in addition to tau deposition. Measurements of WM changes are important for the mechanistic understanding of multifactorial pathways through which AD causes cognitive dysfunction.
Collapse
Affiliation(s)
- Jianqiao Tian
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Sheelakumari Raghavan
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Robert I Reid
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Scott A Przybelski
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Timothy G Lesnick
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Robel K Gebre
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Jonathan Graff-Radford
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Christopher G Schwarz
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Val J Lowe
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Kejal Kantarci
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - David S Knopman
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Ronald C Petersen
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Clifford R Jack
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Prashanthi Vemuri
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN.
| |
Collapse
|
15
|
Ferris JK, Lo BP, Khlif MS, Brodtmann A, Boyd LA, Liew SL. Optimizing automated white matter hyperintensity segmentation in individuals with stroke. FRONTIERS IN NEUROIMAGING 2023; 2:1099301. [PMID: 37554631 PMCID: PMC10406248 DOI: 10.3389/fnimg.2023.1099301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/15/2023] [Indexed: 08/10/2023]
Abstract
White matter hyperintensities (WMHs) are a risk factor for stroke. Consequently, many individuals who suffer a stroke have comorbid WMHs. The impact of WMHs on stroke recovery is an active area of research. Automated WMH segmentation methods are often employed as they require minimal user input and reduce risk of rater bias; however, these automated methods have not been specifically validated for use in individuals with stroke. Here, we present methodological validation of automated WMH segmentation methods in individuals with stroke. We first optimized parameters for FSL's publicly available WMH segmentation software BIANCA in two independent (multi-site) datasets. Our optimized BIANCA protocol achieved good performance within each independent dataset, when the BIANCA model was trained and tested in the same dataset or trained on mixed-sample data. BIANCA segmentation failed when generalizing a trained model to a new testing dataset. We therefore contrasted BIANCA's performance with SAMSEG, an unsupervised WMH segmentation tool available through FreeSurfer. SAMSEG does not require prior WMH masks for model training and was more robust to handling multi-site data. However, SAMSEG performance was slightly lower than BIANCA when data from a single site were tested. This manuscript will serve as a guide for the development and utilization of WMH analysis pipelines for individuals with stroke.
Collapse
Affiliation(s)
- Jennifer K. Ferris
- Graduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
- Gerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada
| | - Bethany P. Lo
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Mohamed Salah Khlif
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Lara A. Boyd
- Graduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
- Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Sook-Lei Liew
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
- Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| |
Collapse
|
16
|
Yu T, Cai LY, Morgan VL, Goodale SE, Englot DJ, Chang CE, Landman BA, Schilling KG. SynBOLD-DisCo: Synthetic BOLD images for distortion correction of fMRI without additional calibration scans. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12464:1246417. [PMID: 37465092 PMCID: PMC10353777 DOI: 10.1117/12.2653647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
The blood oxygen level dependent (BOLD) signal from functional magnetic resonance imaging (fMRI) is a noninvasive technique that has been widely used in research to study brain function. However, fMRI suffers from susceptibility-induced off resonance fields which may cause geometric distortions and mismatches with anatomical images. State-of-the-art correction methods require acquiring reverse phase encoded images or additional field maps to enable distortion correction. However, not all imaging protocols include these additional scans and thus cannot take advantage of these susceptibility correction capabilities. As such, in this study we aim to enable state-of-the-art distortion correction with FSL's topup algorithm of historical and/or limited fMRI data that include only a structural image and single phase encoded fMRI. To do this, we use 3D U-net models to synthesize undistorted fMRI BOLD contrast images from the structural image and use this undistorted synthetic image as an anatomical target for distortion correction with topup. We evaluate the efficacy of this approach, named SynBOLD-DisCo (synthetic BOLD images for distortion correction), and show that BOLD images corrected using our approach are geometrically more similar to structural images than the distorted BOLD data and are practically equivalent to state-of-the-art correction methods which require reverse phase encoded data. Future directions include additional validation studies, integration with other preprocessing operations, retraining with broader pathologies, and investigating the effects of spin echo versus gradient echo images for training and distortion correction. In summary, we demonstrate SynBOLD-DisCo corrects distortion of fMRI when reverse phase encoding scans or field maps are not available.
Collapse
Affiliation(s)
- Tian Yu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sarah E Goodale
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Catherine E Chang
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| |
Collapse
|