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Krüger J, Opfer R, Spies L, Hedderich D, Buchert R. Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network. Eur Radiol 2024; 34:3578-3587. [PMID: 37943313 PMCID: PMC11166757 DOI: 10.1007/s00330-023-10356-1] [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: 01/28/2023] [Revised: 08/19/2023] [Accepted: 08/26/2023] [Indexed: 11/10/2023]
Abstract
OBJECTIVES Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, train, and test a deep convolutional neural network (CNN) for single-subject VBM without the need for a NDB (CNN-VBM). MATERIALS AND METHODS The training dataset comprised 8945 T1w scans from 65 different scanners. The gold standard VBM maps were obtained by conventional VBM with a scanner-specific NDB for each of the 65 scanners. CNN-VBM was tested in an independent dataset comprising healthy controls (n = 37) and subjects with Alzheimer's disease (AD, n = 51) or frontotemporal lobar degeneration (FTLD, n = 30). A scanner-specific NDB for the generation of the gold standard VBM maps was available also for the test set. The technical performance of CNN-VBM was characterized by the Dice coefficient of CNN-VBM maps relative to VBM maps from scanner-specific VBM. For clinical testing, VBM maps were categorized visually according to the clinical diagnoses in the test set by two independent readers, separately for both VBM methods. RESULTS The VBM maps from CNN-VBM were similar to the scanner-specific VBM maps (median Dice coefficient 0.85, interquartile range [0.81, 0.90]). Overall accuracy of the visual categorization of the VBM maps for the detection of AD or FTLD was 89.8% for CNN-VBM and 89.0% for scanner-specific VBM. CONCLUSION CNN-VBM without NDB provides a similar performance in the detection of AD- and FTLD-specific atrophy as conventional VBM. CLINICAL RELEVANCE STATEMENT A deep convolutional neural network for voxel-based morphometry eliminates the need of scanner-specific normal databases without relevant performance loss and, therefore, could pave the way for the widespread clinical use of voxel-based morphometry to support the diagnosis of neurodegenerative diseases. KEY POINTS • The need of normal databases is a barrier for widespread use of voxel-based brain morphometry. • A convolutional neural network achieved a similar performance for detection of atrophy than conventional voxel-based morphometry. • Convolutional neural networks can pave the way for widespread clinical use of voxel-based morphometry.
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Affiliation(s)
| | | | | | - Dennis Hedderich
- Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
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Koivumäki M, Ekblad L, Lantero-Rodriguez J, Ashton NJ, Karikari TK, Helin S, Parkkola R, Lötjönen J, Zetterberg H, Blennow K, Rinne JO, Snellman A. Blood biomarkers of neurodegeneration associate differently with amyloid deposition, medial temporal atrophy, and cerebrovascular changes in APOE ε4-enriched cognitively unimpaired elderly. Alzheimers Res Ther 2024; 16:112. [PMID: 38762725 PMCID: PMC11102270 DOI: 10.1186/s13195-024-01477-w] [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: 06/29/2023] [Accepted: 05/06/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β (Aβ) plaques, neurofibrillary tau tangles, and neurodegeneration in the brain parenchyma. Here, we aimed to (i) assess differences in blood and imaging biomarkers used to evaluate neurodegeneration among cognitively unimpaired APOE ε4 homozygotes, heterozygotes, and non-carriers with varying risk for sporadic AD, and (ii) to determine how different cerebral pathologies (i.e., Aβ deposition, medial temporal atrophy, and cerebrovascular pathology) contribute to blood biomarker concentrations in this sample. METHODS Sixty APOE ε4 homozygotes (n = 19), heterozygotes (n = 21), and non-carriers (n = 20) ranging from 60 to 75 years, were recruited in collaboration with Auria biobank (Turku, Finland). Participants underwent Aβ-PET ([11C]PiB), structural brain MRI including T1-weighted and T2-FLAIR sequences, and blood sampling for measuring serum neurofilament light chain (NfL), plasma total tau (t-tau), plasma N-terminal tau fragments (NTA-tau) and plasma glial fibrillary acidic protein (GFAP). [11C]PiB standardized uptake value ratio was calculated for regions typical for Aβ accumulation in AD. MRI images were analysed for regional volumes, atrophy scores, and volumes of white matter hyperintensities. Differences in biomarker levels and associations between blood and imaging biomarkers were tested using uni- and multivariable linear models (unadjusted and adjusted for age and sex). RESULTS Serum NfL concentration was increased in APOE ε4 homozygotes compared with non-carriers (mean 21.4 pg/ml (SD 9.5) vs. 15.5 pg/ml (3.8), p = 0.013), whereas other blood biomarkers did not differ between the groups (p > 0.077 for all). From imaging biomarkers, hippocampal volume was significantly decreased in APOE ε4 homozygotes compared with non-carriers (6.71 ml (0.86) vs. 7.2 ml (0.7), p = 0.029). In the whole sample, blood biomarker levels were differently predicted by the three measured cerebral pathologies; serum NfL concentration was associated with cerebrovascular pathology and medial temporal atrophy, while plasma NTA-tau associated with medial temporal atrophy. Plasma GFAP showed significant association with both medial temporal atrophy and Aβ pathology. Plasma t-tau concentration did not associate with any of the measured pathologies. CONCLUSIONS Only increased serum NfL concentrations and decreased hippocampal volume was observed in cognitively unimpaired APOEε4 homozygotes compared to non-carriers. In the whole population the concentrations of blood biomarkers were affected in distinct ways by different pathologies.
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Affiliation(s)
- Mikko Koivumäki
- Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland.
| | - Laura Ekblad
- Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland
- Department of Geriatric Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Juan Lantero-Rodriguez
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
- Department of Old Age Psychiatry, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
- NIHR Biomedical Research Centre for Mental Health & Biomedical Research Unit for Dementia at South London & Maudsley NHS Foundation, London, UK
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Semi Helin
- Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Juha O Rinne
- Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Anniina Snellman
- Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
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3
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van Gils AM, Rhodius-Meester HFM, Handgraaf D, Hendriksen HMA, van Strien A, Schoonenboom N, Schipper A, Kleijer M, Griffioen A, Muller M, Tolonen A, Lötjönen J, van der Flier WM, Visser LNC. Use of a digital tool to support the diagnostic process in memory clinics-a usability study. Alzheimers Res Ther 2024; 16:75. [PMID: 38589933 PMCID: PMC11003066 DOI: 10.1186/s13195-024-01433-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/21/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Both memory clinic professionals and patients see value in digital tools, yet these hardly find their way to clinical practice. We explored the usability of a digital tool to support the diagnostic work-up in daily memory clinic practice. We evaluated four modules that integrate multi-modal patient data (1.cognitive test; cCOG, and 2. MRI quantification; cMRI) into useful diagnostic information for clinicians (3. cDSI) and understandable and personalized information for patients (4. patient report). METHODS We conducted a mixed-methods study in five Dutch memory clinics. Fourteen clinicians (11 geriatric specialists/residents, two neurologists, one nurse practitioner) were invited to integrate the tool into routine care with 43 new memory clinic patients. We evaluated usability and user experiences through quantitative data from questionnaires (patients, care partners, clinicians), enriched with thematically analyzed qualitative data from interviews (clinicians). RESULTS We observed wide variation in tool use among clinicians. Our core findings were that clinicians: 1) were mainly positive about the patient report, since it contributes to patient-centered and personalized communication. This was endorsed by patients and care partners, who indicated that the patient report was useful and understandable and helped them to better understand their diagnosis, 2) considered the tool acceptable in addition to their own clinical competence, 3) indicated that the usefulness of the tool depended on the patient population and purpose of the diagnostic process, 4) addressed facilitators (ease of use, practice makes perfect) and barriers (high workload, lack of experience, data unavailability). CONCLUSION This multicenter usability study revealed a willingness to adopt a digital tool to support the diagnostic process in memory clinics. Clinicians, patients, and care partners appreciated the personalized diagnostic report. More attention to education and training of clinicians is needed to utilize the full functionality of the tool and foster implementation in actual daily practice. These findings provide an important step towards a lasting adoption of digital tools in memory clinic practice.
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Affiliation(s)
- Aniek M van Gils
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands.
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands
- Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
- Department of Internal Medicine, Geriatric Medicine Section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - Dédé Handgraaf
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands
| | - Heleen M A Hendriksen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands
| | - Astrid van Strien
- Department of Geriatric medicine, Jeroen Bosch Ziekenhuis, Den Bosch, The Netherlands
| | | | - Annemieke Schipper
- Department of Neurology, HagaZiekenhuis, location Zoetermeer, Zoetermeer, The Netherlands
| | - Mariska Kleijer
- Department of Neurology, HagaZiekenhuis, location Zoetermeer, Zoetermeer, The Netherlands
| | - Annemiek Griffioen
- Department of Internal Medicine, Geriatric Medicine Section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - Majon Muller
- Department of Internal Medicine, Geriatric Medicine Section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | | | | | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands
- Department of Epidemiology and Data Sciences, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Leonie N C Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience Neurodegeneration, Amsterdam, The Netherlands
- Department of Medical Psychology, Amsterdam UMC location University of Amsterdam/AMC, Amsterdam, The Netherlands
- Amsterdam Public Health, Quality of Care, Amsterdam, The Netherlands
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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Mai Y, Cao Z, Zhao L, Yu Q, Xu J, Liu W, Liu B, Tang J, Luo Y, Liao W, Fang W, Ruan Y, Lei M, Mok VCT, Shi L, Liu J. The role of visual rating and automated brain volumetry in early detection and differential diagnosis of Alzheimer's disease. CNS Neurosci Ther 2024; 30:e14492. [PMID: 37864441 PMCID: PMC11017425 DOI: 10.1111/cns.14492] [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: 12/15/2022] [Revised: 09/07/2023] [Accepted: 09/26/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Medial temporal lobe atrophy (MTA) is a diagnostic marker for mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the accuracy of quantitative MTA (QMTA) in diagnosing early AD is unclear. This study aimed to investigate the accuracy of QMTA and its related components (inferior lateral ventricle [ILV] and hippocampus) with MTA in the early diagnosis of MCI and AD. METHODS This study included four groups: normal (NC), MCI stable (MCIs), MCI converted to AD (MCIs), and mild AD (M-AD) groups. Magnetic resonance image analysis software was used to quantify the hippocampus, ILV, and QMTA. MTA was rated by two experienced neurologists. Receiver operating characteristic area under the curve (AUC) analysis was performed to compare their capability in differentiating AD from NC and MCI, and optimal thresholds were determined using the Youden index. RESULTS QMTA distinguished M-AD from NC and MCI with higher diagnostic accuracy than MTA, hippocampus, and ILV (AUCNC = 0.976, AUCMCI = 0.836, AUCMCIs = 0.894, AUCMCIc = 0.730). The diagnostic accuracy of QMTA was superior to that of MTA, the hippocampus, and ILV in differentiating MCI from AD. The diagnostic accuracy of QMTA was found to remain the best across age, sex, and pathological subgroups analyzed. The sensitivity (92.45%) and specificity (90.64%) were higher in this study when a cutoff value of 0.635 was chosen for QMTA. CONCLUSIONS QMTA may be a better choice than the MTA scale or the associated quantitative components alone in identifying AD patients and MCI individuals with higher progression risk.
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Affiliation(s)
- Yingren Mai
- Department of NeurologyThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Zhiyu Cao
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Lei Zhao
- BrainNow Research InstituteShenzhenChina
| | - Qun Yu
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Jiaxin Xu
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Wenyan Liu
- BrainNow Research InstituteShenzhenChina
| | - Bowen Liu
- Department of Statistics, College of Liberal Art and SciencesUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Jingyi Tang
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Yishan Luo
- BrainNow Research InstituteShenzhenChina
| | - Wang Liao
- Department of NeurologyThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Wenli Fang
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Yuting Ruan
- Department of RehabilitationThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Ming Lei
- Department of Neurology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Vincent C. T. Mok
- BrainNow Research InstituteShenzhenChina
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative MedicineThe Chinese University of Hong KongHong Kong, SARChina
| | - Lin Shi
- BrainNow Research InstituteShenzhenChina
- Department of Imaging and Interventional RadiologyThe Chinese University of Hong KongHong Kong, SARChina
| | - Jun Liu
- Department of NeurologyThe Second Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
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Clancy U, Kancheva AK, Valdés Hernández MDC, Jochems ACC, Muñoz Maniega S, Quinn TJ, Wardlaw JM. Imaging Biomarkers of VCI: A Focused Update. Stroke 2024; 55:791-800. [PMID: 38445496 DOI: 10.1161/strokeaha.123.044171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Vascular cognitive impairment is common after stroke, in memory clinics, medicine for the elderly services, and undiagnosed in the community. Vascular disease is said to be the second most common cause of dementia after Alzheimer disease, yet vascular dysfunction is now known to predate cognitive decline in Alzheimer disease, and most dementias at older ages are mixed. Neuroimaging has a major role in identifying the proportion of vascular versus other likely pathologies in patients with cognitive impairment. Here, we aim to provide a pragmatic but evidence-based summary of the current state of potential imaging biomarkers, focusing on magnetic resonance imaging and computed tomography, which are relevant to diagnosing, estimating prognosis, monitoring vascular cognitive impairment, and incorporating our own experiences. We focus on markers that are well-established, with a known profile of association with cognitive measures, but also consider more recently described, including quantitative tissue markers of vascular injury. We highlight the gaps in accessibility and translation to more routine clinical practice.
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Affiliation(s)
- Una Clancy
- Centre for Clinical Brain Sciences and UK Dementia Research Institute, The University of Edinburgh, United Kingdom (U.C., M.d.C.V.H. A.C.C.J., S.M.M., J.M.W.)
| | - Angelina K Kancheva
- School of Cardiovascular and Metabolic Health, University of Glasgow, United Kingdom (A.K.K., T.J.Q.)
| | - Maria Del C Valdés Hernández
- Centre for Clinical Brain Sciences and UK Dementia Research Institute, The University of Edinburgh, United Kingdom (U.C., M.d.C.V.H. A.C.C.J., S.M.M., J.M.W.)
| | - Angela C C Jochems
- Centre for Clinical Brain Sciences and UK Dementia Research Institute, The University of Edinburgh, United Kingdom (U.C., M.d.C.V.H. A.C.C.J., S.M.M., J.M.W.)
| | - Susana Muñoz Maniega
- Centre for Clinical Brain Sciences and UK Dementia Research Institute, The University of Edinburgh, United Kingdom (U.C., M.d.C.V.H. A.C.C.J., S.M.M., J.M.W.)
| | - Terence J Quinn
- School of Cardiovascular and Metabolic Health, University of Glasgow, United Kingdom (A.K.K., T.J.Q.)
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences and UK Dementia Research Institute, The University of Edinburgh, United Kingdom (U.C., M.d.C.V.H. A.C.C.J., S.M.M., J.M.W.)
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Wittens MMJ, Allemeersch GJ, Sima DM, Vanderhasselt T, Raeymaeckers S, Fransen E, Smeets D, de Mey J, Bjerke M, Engelborghs S. Towards validation in clinical routine: a comparative analysis of visual MTA ratings versus the automated ratio between inferior lateral ventricle and hippocampal volumes in Alzheimer's disease diagnosis. Neuroradiology 2024; 66:487-506. [PMID: 38240767 PMCID: PMC10937807 DOI: 10.1007/s00234-024-03280-8] [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: 07/18/2023] [Accepted: 12/28/2023] [Indexed: 03/14/2024]
Abstract
PURPOSE To assess the performance of the inferior lateral ventricle (ILV) to hippocampal (Hip) volume ratio on brain MRI, for Alzheimer's disease (AD) diagnostics, comparing it to individual automated ILV and hippocampal volumes, and visual medial temporal lobe atrophy (MTA) consensus ratings. METHODS One-hundred-twelve subjects (mean age ± SD, 66.85 ± 13.64 years) with varying degrees of cognitive decline underwent MRI using a Philips Ingenia 3T. The MTA scale by Scheltens, rated on coronal 3D T1-weighted images, was determined by three experienced radiologists, blinded to diagnosis and sex. Automated volumetry was computed by icobrain dm (v. 5.10) for total, left, right hippocampal, and ILV volumes. The ILV/Hip ratio, defined as the percentage ratio between ILV and hippocampal volumes, was calculated and compared against a normative reference population (n = 1903). Inter-rater agreement, association, classification accuracy, and clinical interpretability on patient level were reported. RESULTS Visual MTA scores showed excellent inter-rater agreement. Ordinal logistic regression and correlation analyses demonstrated robust associations between automated brain segmentations and visual MTA ratings, with the ILV/Hip ratio consistently outperforming individual hippocampal and ILV volumes. Pairwise classification accuracy showed good performance without statistically significant differences between the ILV/Hip ratio and visual MTA across disease stages, indicating potential interchangeability. Comparison to the normative population and clinical interpretability assessments showed commensurability in classifying MTA "severity" between visual MTA and ILV/Hip ratio measurements. CONCLUSION The ILV/Hip ratio shows the highest correlation to visual MTA, in comparison to automated individual ILV and hippocampal volumes, offering standardized measures for diagnostic support in different stages of cognitive decline.
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Affiliation(s)
- Mandy M J Wittens
- Dept. of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
- Dept. of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
| | - Gert-Jan Allemeersch
- Dept. of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium.
| | - Diana M Sima
- Icometrix, Kolonel Begaultlaan 1b, 3012, Leuven, Belgium
- AI Supported Modelling in Clinical Sciences (AIMS), Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Tim Vanderhasselt
- Dept. of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
| | - Steven Raeymaeckers
- Dept. of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
| | - Erik Fransen
- StatUa Center for Statistics, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Dirk Smeets
- Icometrix, Kolonel Begaultlaan 1b, 3012, Leuven, Belgium
| | - Johan de Mey
- Dept. of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
| | - Maria Bjerke
- Dept. of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
- NEUR (Neuroprotection & Neuromodulation), Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Av. du Laerbeek 101, 1090, Brussels, Belgium
- Laboratory of Neurochemistry, Dept. of Clinical Chemistry, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
| | - Sebastiaan Engelborghs
- Dept. of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
- Dept. of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
- NEUR (Neuroprotection & Neuromodulation), Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Av. du Laerbeek 101, 1090, Brussels, Belgium
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7
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O'Shea DM, Camacho S, Ezzeddine R, Besser L, Tolea MI, Wang L, Galvin C, Gibbs G, Galvin JE. The Mediating Role of Cortical Atrophy on the Relationship between the Resilience Index and Cognitive Function: Findings from the Healthy Brain Initiative. J Alzheimers Dis 2024; 98:1017-1027. [PMID: 38489189 DOI: 10.3233/jad-231346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background Lifestyle factors are linked to differences in brain aging and risk for Alzheimer's disease, underscored by concepts like 'cognitive reserve' and 'brain maintenance'. The Resilience Index (RI), a composite of 6 factors (cognitive reserve, physical and cognitive activities, social engagement, diet, and mindfulness) provides such a holistic measure. Objective This study aims to examine the association of RI scores with cognitive function and assess the mediating role of cortical atrophy. Methods Baseline data from 113 participants (aged 45+, 68% female) from the Healthy Brain Initiative were included. Life course resilience was estimated with the RI, cognitive performance with Cognivue®, and brain health using a machine learning derived Cortical Atrophy Score (CAS). Mediation analysis probed the relationship between RI, cognitive outcomes, and cortical atrophy. Results In age and sex adjusted models, the RI was significantly associated with CAS (β= -0.25, p = 0.006) and Cognivue® scores (β= 0.32, p < 0.001). The RI-Cognivue® association was partially mediated by CAS (β= 0.07; 95% CI [0.02, 0.14]). Conclusions Findings revealed that the collective effect of early and late-life lifestyle resilience factors on cognition are partially explained by their association with less brain atrophy. These findings underscore the value of comprehensive lifestyle assessments in understanding the risk and progression of cognitive decline and Alzheimer's disease in an aging population.
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Affiliation(s)
- Deirdre M O'Shea
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, USA
| | - Simone Camacho
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, USA
| | - Reem Ezzeddine
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, USA
| | - Lilah Besser
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, USA
| | - Magdalena I Tolea
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, USA
| | - Lily Wang
- Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Conor Galvin
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, USA
| | - Gregory Gibbs
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, USA
| | - James E Galvin
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, FL, USA
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Hedderich DM, Schmitz-Koep B, Schuberth M, Schultz V, Schlaeger SJ, Schinz D, Rubbert C, Caspers J, Zimmer C, Grimmer T, Yakushev I. Impact of normative brain volume reports on the diagnosis of neurodegenerative dementia disorders in neuroradiology: A real-world, clinical practice study. Front Aging Neurosci 2022; 14:971863. [PMID: 36313028 PMCID: PMC9597632 DOI: 10.3389/fnagi.2022.971863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/20/2022] [Indexed: 12/02/2022] Open
Abstract
Background: Normative brain volume reports (NBVR) are becoming more available in the work-up of patients with suspected dementia disorders, potentially leveraging the value of structural MRI in clinical settings. The present study aims to investigate the impact of NBVRs on the diagnosis of neurodegenerative dementia disorders in real-world clinical practice. Methods: We retrospectively analyzed data of 112 memory clinic patients, who were consecutively referred for MRI and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) during a 12-month period. Structural MRI was assessed by two residents with 2 and 3 years of neuroimaging experience. Statements and diagnostic confidence regarding the presence of a neurodegenerative disorder in general (first level) and Alzheimer’s disease (AD) pattern in particular (second level) were recorded without and with NBVR information. FDG-PET served as the reference standard. Results: Overall, despite a trend towards increased accuracy, the impact of NBVRs on diagnostic accuracy was low and non-significant. We found a significant drop of sensitivity (0.75–0.58; p < 0.001) and increase of specificity (0.62–0.85; p < 0.001) for rater 1 at identifying patients with neurodegenerative dementia disorders. Diagnostic confidence increased for rater 2 (p < 0.001). Conclusions: Overall, NBVRs had a limited impact on diagnostic accuracy in real-world clinical practice. Potentially, NBVR might increase diagnostic specificity and confidence of neuroradiology residents. To this end, a well-defined framework for integration of NBVR in the diagnostic process and improved algorithms of NBVR generation are essential.
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Affiliation(s)
- Dennis M. Hedderich
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
- *Correspondence: Dennis M. Hedderich
| | - Benita Schmitz-Koep
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Madeleine Schuberth
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Vivian Schultz
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Sarah J. Schlaeger
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - David Schinz
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Dusseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Dusseldorf, Germany
| | - Claus Zimmer
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Sch, Munich, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
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9
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Thyreau B, Tatewaki Y, Chen L, Takano Y, Hirabayashi N, Furuta Y, Hata J, Nakaji S, Maeda T, Noguchi‐Shinohara M, Mimura M, Nakashima K, Mori T, Takebayashi M, Ninomiya T, Taki Y. Higher-resolution quantification of white matter hypointensities by large-scale transfer learning from 2D images on the JPSC-AD cohort. Hum Brain Mapp 2022; 43:3998-4012. [PMID: 35524684 PMCID: PMC9374893 DOI: 10.1002/hbm.25899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/24/2022] [Accepted: 04/20/2022] [Indexed: 12/14/2022] Open
Abstract
White matter lesions (WML) commonly occur in older brains and are quantifiable on MRI, often used as a biomarker in Aging research. Although algorithms are regularly proposed that identify these lesions from T2‐fluid‐attenuated inversion recovery (FLAIR) sequences, none so far can estimate lesions directly from T1‐weighted images with acceptable accuracy. Since 3D T1 is a polyvalent and higher‐resolution sequence, it could be beneficial to obtain the distribution of WML directly from it. However a serious difficulty, both for algorithms and human, can be found in the ambiguities of brain signal intensity in T1 images. This manuscript shows that a cross‐domain ConvNet (Convolutional Neural Network) approach can help solve this problem. Still, this is non‐trivial, as it would appear to require a large and varied dataset (for robustness) labelled at the same high resolution (for spatial accuracy). Instead, our model was taught from two‐dimensional FLAIR images with a loss function designed to handle the super‐resolution need. And crucially, we leveraged a very large training set for this task, the recently assembled, multi‐sites Japan Prospective Studies Collaboration for Aging and Dementia (JPSC‐AD) cohort. We describe the two‐step procedure that we followed to handle such a large number of imperfectly labeled samples. A large‐scale accuracy evaluation conducted against FreeSurfer 7, and a further visual expert rating revealed that WML segmentation from our ConvNet was consistently better. Finally, we made a directly usable software program based on that trained ConvNet model, available at https://github.com/bthyreau/deep-T1-WMH.
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Affiliation(s)
- Benjamin Thyreau
- Smart‐Aging Research Center, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
| | - Yasuko Tatewaki
- Department of Aging Research and Geriatric Medicine, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
- Department of Geriatric Medicine and NeuroimagingTohoku University HospitalSendaiJapan
| | - Liying Chen
- Smart‐Aging Research Center, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
| | - Yuji Takano
- Smart‐Aging Research Center, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
- Department of Psychological SciencesUniversity of Human EnvironmentsMatsuyamaJapan
| | - Naoki Hirabayashi
- Department of Epidemiology and Public Health, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Yoshihiko Furuta
- Department of Epidemiology and Public Health, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Jun Hata
- Department of Epidemiology and Public Health, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Shigeyuki Nakaji
- Department of Social Medicine, Graduate School of MedicineHirosaki UniversityHirosakiJapan
| | - Tetsuya Maeda
- Division of Neurology and Gerontology, Department of Internal Medicine, School of MedicineIwate Medical UniversityIwateJapan
| | - Moeko Noguchi‐Shinohara
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical SciencesKanazawa UniversityKanazawaJapan
| | | | - Kenji Nakashima
- National Hospital Organization, Matsue Medical CenterShimaneJapan
| | - Takaaki Mori
- Department of Neuropsychiatry, Ehime University Graduate School of MedicineEhime UniversityEhimeJapan
| | - Minoru Takebayashi
- Faculty of Life Sciences, Department of NeuropsychiatryKumamoto UniversityKumamotoJapan
| | - Toshiharu Ninomiya
- Department of Epidemiology and Public Health, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Yasuyuki Taki
- Smart‐Aging Research Center, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
- Department of Aging Research and Geriatric Medicine, Institute of Development, Aging, and CancerTohoku UniversitySendaiJapan
- Department of Geriatric Medicine and NeuroimagingTohoku University HospitalSendaiJapan
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10
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Ingala S, van Maurik IS, Altomare D, Wurm R, Dicks E, van Schijndel RA, Zwan M, Bouwman F, Schoonenboom N, Boelaarts L, Roks G, van Marum R, van Harten B, van Uden I, Claus J, Wottschel V, Vrenken H, Wattjes MP, van der Flier WM, Barkhof F. Clinical applicability of quantitative atrophy measures on MRI in patients suspected of Alzheimer's disease. Eur Radiol 2022; 32:7789-7799. [PMID: 35639148 PMCID: PMC9668763 DOI: 10.1007/s00330-021-08503-7] [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: 11/21/2020] [Revised: 11/03/2021] [Accepted: 12/01/2021] [Indexed: 01/03/2023]
Abstract
OBJECTIVES Neurodegeneration in suspected Alzheimer's disease can be determined using visual rating or quantitative volumetric assessments. We examined the feasibility of volumetric measurements of gray matter (GMV) and hippocampal volume (HCV) and compared their diagnostic performance with visual rating scales in academic and non-academic memory clinics. MATERIALS AND METHODS We included 231 patients attending local memory clinics (LMC) in the Netherlands and 501 of the academic Amsterdam Dementia Cohort (ADC). MRI scans were acquired using local protocols, including a T1-weighted sequence. Quantification of GMV and HCV was performed using FSL and FreeSurfer. Medial temporal atrophy and global atrophy were assessed with visual rating scales. ROC curves were derived to determine which measure discriminated best between cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's dementia (AD). RESULTS Patients attending LMC (age 70.9 ± 8.9 years; 47% females; 19% CN; 34% MCI; 47% AD) were older, had more cerebrovascular pathology, and had lower GMV and HCV compared to those of the ADC (age 64.9 ± 8.2 years; 42% females; 35% CN, 43% MCI, 22% AD). While visual ratings were feasible in > 95% of scans in both cohorts, quantification was achieved in 94-98% of ADC, but only 68-85% of LMC scans, depending on the software. Visual ratings and volumetric outcomes performed similarly in discriminating CN vs AD in both cohorts. CONCLUSION In clinical settings, quantification of GM and hippocampal atrophy currently fails in up to one-third of scans, probably due to lack of standardized acquisition protocols. Diagnostic accuracy is similar for volumetric measures and visual rating scales, making the latter suited for clinical practice. In a real-life clinical setting, volumetric assessment of MRI scans in dementia patients may require acquisition protocol optimization and does not outperform visual rating scales. KEY POINTS • In a real-life clinical setting, the diagnostic performance of visual rating scales is similar to that of automatic volumetric quantification and may be sufficient to distinguish Alzheimer's disease groups. • Volumetric assessment of gray matter and hippocampal volumes from MRI scans of patients attending non-academic memory clinics fails in up to 32% of cases. • Clinical MR acquisition protocols should be optimized to improve the output of quantitative software for segmentation of Alzheimer's disease-specific outcomes.
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Affiliation(s)
- Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Department of Radiology and Nuclear Medicine, Noordwest Hospital Group, Alkmaar, The Netherlands
| | - Ingrid S. van Maurik
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands ,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Daniele Altomare
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands ,Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland ,Memory Clinic, University Hospitals of Geneva, Geneva, Switzerland
| | - Raphael Wurm
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Ellen Dicks
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Ronald A. van Schijndel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands
| | - Marissa Zwan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Femke Bouwman
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Niki Schoonenboom
- Geriatric Department, Noordwest Ziekenhuis Groep, Alkmaar, The Netherlands
| | - Leo Boelaarts
- Geriatric Department, Noordwest Ziekenhuis Groep, Alkmaar, The Netherlands
| | - Gerwin Roks
- Department of Neurology, Elisabeth-TweeSteden Ziekenhuis, Tilburg, The Netherlands
| | - Rob van Marum
- Department of Geriatrics, Jeroen Bosch Hospital, ‘S-Hertogenbosch, The Netherlands ,Department of Family Medicine and Elderly Care Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Barbera van Harten
- Department of Neurology, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Inge van Uden
- Department of Neurology, Catharina Hospital, Eindhoven, The Netherlands
| | - Jules Claus
- Department of Neurology, Tergooi Hospital, Blaricum, The Netherlands
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands
| | - Mike P. Wattjes
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands ,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Institutes of Neurology and Healthcare Engineering, UCL, London, UK
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11
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Pemberton HG, Zaki LAM, Goodkin O, Das RK, Steketee RME, Barkhof F, Vernooij MW. Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review. Neuroradiology 2021; 63:1773-1789. [PMID: 34476511 PMCID: PMC8528755 DOI: 10.1007/s00234-021-02746-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/02/2021] [Indexed: 12/22/2022]
Abstract
Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.
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Affiliation(s)
- Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lara A M Zaki
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Olivia Goodkin
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ravi K Das
- Clinical, Educational and Health Psychology, University College London, London, UK
| | - Rebecca M E Steketee
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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12
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Kaipainen AL, Pitkänen J, Haapalinna F, Jääskeläinen O, Jokinen H, Melkas S, Erkinjuntti T, Vanninen R, Koivisto AM, Lötjönen J, Koikkalainen J, Herukka SK, Julkunen V. A novel CT-based automated analysis method provides comparable results with MRI in measuring brain atrophy and white matter lesions. Neuroradiology 2021; 63:2035-2046. [PMID: 34389887 PMCID: PMC8589740 DOI: 10.1007/s00234-021-02761-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/30/2021] [Indexed: 11/26/2022]
Abstract
Purpose Automated analysis of neuroimaging data is commonly based on magnetic resonance imaging (MRI), but sometimes the availability is limited or a patient might have contradictions to MRI. Therefore, automated analyses of computed tomography (CT) images would be beneficial. Methods We developed an automated method to evaluate medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and the severity of white matter lesions (WMLs) from a CT scan and compared the results to those obtained from MRI in a cohort of 214 subjects gathered from Kuopio and Helsinki University Hospital registers from 2005 - 2016. Results The correlation coefficients of computational measures between CT and MRI were 0.9 (MTA), 0.82 (GCA), and 0.86 (Fazekas). CT-based measures were identical to MRI-based measures in 60% (MTA), 62% (GCA) and 60% (Fazekas) of cases when the measures were rounded to the nearest full grade variable. However, the difference in measures was 1 or less in 97–98% of cases. Similar results were obtained for cortical atrophy ratings, especially in the frontal and temporal lobes, when assessing the brain lobes separately. Bland–Altman plots and weighted kappa values demonstrated high agreement regarding measures based on CT and MRI. Conclusions MTA, GCA, and Fazekas grades can also be assessed reliably from a CT scan with our method. Even though the measures obtained with the different imaging modalities were not identical in a relatively extensive cohort, the differences were minor. This expands the possibility of using this automated analysis method when MRI is inaccessible or contraindicated. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-021-02761-4.
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Affiliation(s)
- Aku L Kaipainen
- University of Eastern Finland, Institute of Clinical Medicine / Neurology, P.O. Box 1627, (Yliopistonranta 1 C), 70211, Kuopio, Finland.
- Neurosurgery of NeuroCenter, Kuopio University Hospital, P.O. Box 100, 70029 KYS, Kuopio, Finland.
| | - Johanna Pitkänen
- Department of Neurology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, 00029 HUS, Helsinki, Finland
| | - Fanni Haapalinna
- University of Eastern Finland, Institute of Clinical Medicine / Neurology, P.O. Box 1627, (Yliopistonranta 1 C), 70211, Kuopio, Finland
| | - Olli Jääskeläinen
- University of Eastern Finland, Institute of Clinical Medicine / Neurology, P.O. Box 1627, (Yliopistonranta 1 C), 70211, Kuopio, Finland
| | - Hanna Jokinen
- Division of Neuropsychology, HUS Neurocenter, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Susanna Melkas
- Department of Neurology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, 00029 HUS, Helsinki, Finland
| | - Timo Erkinjuntti
- Department of Neurology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, 00029 HUS, Helsinki, Finland
| | - Ritva Vanninen
- Department of Radiology, Kuopio University Hospital, P.O. Box 1777, 70211, Kuopio, Finland
- University of Eastern Finland, Institute of Clinical Medicine / Radiology, P.O. Box 1627, (Yliopistonranta 1 C), 70211, Kuopio, Finland
| | - Anne M Koivisto
- University of Eastern Finland, Institute of Clinical Medicine / Neurology, P.O. Box 1627, (Yliopistonranta 1 C), 70211, Kuopio, Finland
- Department of Neurology, Kuopio University Hospital, P.O. Box 1777, 70211, Kuopio, Finland
- Department of Neurosciences, Department of Geriatrics / Rehabilitation and Internal Medicine, University of Helsinki, Helsinki University Hospital, P.O. Box 340, 00029 HUS, Helsinki, Finland
| | - Jyrki Lötjönen
- Combinostics Oy, Hatanpään valtatie 24, 33100, Tampere, Finland
| | | | - Sanna-Kaisa Herukka
- University of Eastern Finland, Institute of Clinical Medicine / Neurology, P.O. Box 1627, (Yliopistonranta 1 C), 70211, Kuopio, Finland
- Department of Neurology, Kuopio University Hospital, P.O. Box 1777, 70211, Kuopio, Finland
| | - Valtteri Julkunen
- University of Eastern Finland, Institute of Clinical Medicine / Neurology, P.O. Box 1627, (Yliopistonranta 1 C), 70211, Kuopio, Finland
- Department of Neurology, Kuopio University Hospital, P.O. Box 1777, 70211, Kuopio, Finland
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13
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Håkansson C, Tamaddon A, Andersson H, Torisson G, Mårtensson G, Truong M, Annertz M, Londos E, Björkman-Burtscher IM, Hansson O, van Westen D. Inter-modality assessment of medial temporal lobe atrophy in a non-demented population: application of a visual rating scale template across radiologists with varying clinical experience. Eur Radiol 2021; 32:1127-1134. [PMID: 34328536 PMCID: PMC8794965 DOI: 10.1007/s00330-021-08177-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 06/03/2021] [Accepted: 06/25/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To assess inter-modality agreement and accuracy for medial temporal lobe atrophy (MTA) ratings across radiologists with varying clinical experience in a non-demented population. METHODS Four raters (two junior radiologists and two senior neuroradiologists) rated MTA on CT and MRI scans using Scheltens' MTA scale. Ratings were compared to a consensus rating by two experienced neuroradiologists for estimation of true positive and negative rates (TPR and TNR) and over- and underestimation of MTA. Inter-modality agreement expressed as Cohen's κ (dichotomized data), Cohen's κw, and two-way mixed, single measures, consistency ICC (ordinal data) were determined. Adequate agreement was defined as κ/κw ≥ 0.80 and ICC ≥ 0.80 (significance level at 95% CI ≥ 0.65). RESULTS Forty-nine subjects (median age 72 years, 27% abnormal MTA) with cognitive impairment were included. Only junior radiologists achieved adequate agreement expressed as Cohen's κ. All raters achieved adequate agreement expressed as Cohen's κw and ICC. True positive rates varied from 69 to 100% and TNR varied from 85 to 100%. No under- or overestimation of MTA was observed. Ratings did not differ between radiologists. CONCLUSION We conclude that radiologists with varying experience achieve adequate inter-modality agreement and similar accuracy when Scheltens' MTA scale is used to rate MTA on a non-demented population. However, TPR varied between radiologists which could be attributed to rating style differences. KEY POINTS • Radiologists with varying experience achieve adequate inter-modality agreement with similar accuracy when Scheltens' MTA scale is used to rate MTA on a non-demented population. • Differences in rating styles might affect accuracy, this was most evident for senior neuroradiologists, and only junior radiologists achieved adequate agreement on dichotomized (abnormal/normal) ratings. • The use of an MTA scale template might compensate for varying clinical experience which could make it applicable for clinical use.
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Affiliation(s)
- Claes Håkansson
- Department of Imaging and Function, Skåne University Hospital, Lund, Sweden.
- Department of Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden.
| | - Ashkan Tamaddon
- Department of Imaging and Function, Skåne University Hospital, Lund, Sweden
| | - Henrik Andersson
- Department of Imaging and Function, Skåne University Hospital, Lund, Sweden
| | - Gustav Torisson
- Department of Translational Medicine, Clinical Infection Medicine, Lund University, Malmö, Sweden
- Department of Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Malmö, Sweden
| | - Gustav Mårtensson
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - My Truong
- Department of Imaging and Function, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
| | - Mårten Annertz
- Department of Imaging and Function, Skåne University Hospital, Lund, Sweden
| | - Elisabet Londos
- Department of Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | | | - Oskar Hansson
- Department of Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Danielle van Westen
- Department of Imaging and Function, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
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14
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Molinder A, Ziegelitz D, Maier SE, Eckerström C. Validity and reliability of the medial temporal lobe atrophy scale in a memory clinic population. BMC Neurol 2021; 21:289. [PMID: 34301202 PMCID: PMC8305846 DOI: 10.1186/s12883-021-02325-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 07/12/2021] [Indexed: 11/30/2022] Open
Abstract
Background Visual rating of medial temporal lobe atrophy (MTA) is often performed in conjunction with dementia workup. Most prior studies involved patients with known or probable Alzheimer’s disease (AD). This study investigated the validity and reliability of MTA in a memory clinic population. Methods MTA was rated in 752 MRI examinations, of which 105 were performed in cognitively healthy participants (CH), 184 in participants with subjective cognitive impairment, 249 in subjects with mild cognitive impairment, and 214 in patients with dementia, including AD, subcortical vascular dementia and mixed dementia. Hippocampal volumes, measured manually or using FreeSurfer, were available in the majority of cases. Intra- and interrater reliability was tested using Cohen’s weighted kappa. Correlation between MTA and quantitative hippocampal measurements was ascertained with Spearman’s rank correlation coefficient. Moreover, diagnostic ability of MTA was assessed with receiver operating characteristic (ROC) analysis and suitable, age-dependent MTA thresholds were determined. Results Rater agreement was moderate to substantial. MTA correlation with quantitative volumetric methods ranged from -0.20 (p< 0.05) to -0.68 (p < 0.001) depending on the quantitative method used. Both MTA and FreeSurfer are able to distinguish dementia subgroups from CH. Suggested age-dependent MTA thresholds are 1 for the age group below 75 years and 1.5 for the age group 75 years and older. Conclusions MTA can be considered a valid marker of medial temporal lobe atrophy and may thus be valuable in the assessment of patients with cognitive impairment, even in a heterogeneous patient population.
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Affiliation(s)
- Anna Molinder
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. .,Neuroradiology, Sahlgrenska sjukhuset, Blå stråket 5, Gothenburg, 413 46, Sweden.
| | - Doerthe Ziegelitz
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Stephan E Maier
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Carl Eckerström
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Immunology and Transfusion Medicine, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
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15
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Jonell P, Moëll B, Håkansson K, Henter GE, Kucherenko T, Mikheeva O, Hagman G, Holleman J, Kivipelto M, Kjellström H, Gustafson J, Beskow J. Multimodal Capture of Patient Behaviour for Improved Detection of Early Dementia: Clinical Feasibility and Preliminary Results. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.642633] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Non-invasive automatic screening for Alzheimer’s disease has the potential to improve diagnostic accuracy while lowering healthcare costs. Previous research has shown that patterns in speech, language, gaze, and drawing can help detect early signs of cognitive decline. In this paper, we describe a highly multimodal system for unobtrusively capturing data during real clinical interviews conducted as part of cognitive assessments for Alzheimer’s disease. The system uses nine different sensor devices (smartphones, a tablet, an eye tracker, a microphone array, and a wristband) to record interaction data during a specialist’s first clinical interview with a patient, and is currently in use at Karolinska University Hospital in Stockholm, Sweden. Furthermore, complementary information in the form of brain imaging, psychological tests, speech therapist assessment, and clinical meta-data is also available for each patient. We detail our data-collection and analysis procedure and present preliminary findings that relate measures extracted from the multimodal recordings to clinical assessments and established biomarkers, based on data from 25 patients gathered thus far. Our findings demonstrate feasibility for our proposed methodology and indicate that the collected data can be used to improve clinical assessments of early dementia.
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16
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Jurek L, Dorey JM, Nourredine M, Galvao F, Brunelin J. Impact of vascular risk factors on clinical outcome in elderly patients with depression receiving electroconvulsive therapy. J Affect Disord 2021; 279:308-315. [PMID: 33096329 DOI: 10.1016/j.jad.2020.10.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 08/29/2020] [Accepted: 10/11/2020] [Indexed: 11/24/2022]
Abstract
BACKGROUND Although electroconvulsive therapy (ECT) is a highly effective, safe, and well-tolerated antidepressant treatment for late-life depression (LLD), there is large variability in response rates across individuals. We hypothesized that these variations would be in part explained by the level of vascular risk in this population. We therefore compared response rates to ECT in patients with LLD presenting with or without vascular risk factors (VRF). METHODS 52 patients with LLD (age > 55) who received a course of ECT were separated into 2 groups according to the presence of VRF (n = 20) or not (n = 32). Framingham score (10-year risk for developing a coronary heart disease) was calculated for each patient. Our primary outcome was the number of responders to ECT in each group (defined as at least 50% decrease of the Montgomery-Åsberg Depression Rating Scale score following ECT course). Scores at the self-rated Beck Depression Inventory are also reported. RESULTS Patients with VRF presented significant lower response rates to ECT (12 out of 20; 60%) than patients without VRF (30 out of 32; 94%; p = 0.004). A negative correlation was found between Framingham score and changes in depression scores pre/post ECT (r = -0.42; p = 0.0039). LIMITATIONS Our study was limited by sample size and retrospective design. CONCLUSION Patients with LLD and VRF showed lower response rates to ECT than those without VRF. The more the VRF increased, the less the antidepressant effect of ECT was observed. Results are discussed in light of the role of apathy in clinical response to ECT.
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Affiliation(s)
- Lucie Jurek
- University Lyon 1, Villeurbanne F-69000, France; Centre Hospitalier Le Vinatier, Bron, France.
| | - Jean-Michel Dorey
- Centre Hospitalier Le Vinatier, Bron, France; Brain Dynamics and Cognition, Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR 5292, Lyon, France
| | - Mikaïl Nourredine
- University Lyon 1, Villeurbanne F-69000, France; INSERM, Lyon Neuroscience Research Center, Psychiatric Disorders: from Resistance to Response, U1028; CNRS, UMR5292, PSYR2 Team, Lyon F-69000, France
| | - Filipe Galvao
- University Lyon 1, Villeurbanne F-69000, France; INSERM, Lyon Neuroscience Research Center, Psychiatric Disorders: from Resistance to Response, U1028; CNRS, UMR5292, PSYR2 Team, Lyon F-69000, France; Centre Hospitalier Le Vinatier, Bron, France
| | - Jérome Brunelin
- University Lyon 1, Villeurbanne F-69000, France; INSERM, Lyon Neuroscience Research Center, Psychiatric Disorders: from Resistance to Response, U1028; CNRS, UMR5292, PSYR2 Team, Lyon F-69000, France; Centre Hospitalier Le Vinatier, Bron, France
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17
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Toppala S, Ekblad LL, Lötjönen J, Helin S, Hurme S, Johansson J, Jula A, Karrasch M, Koikkalainen J, Laine H, Parkkola R, Viitanen M, Rinne JO. Midlife Insulin Resistance as a Predictor for Late-Life Cognitive Function and Cerebrovascular Lesions. J Alzheimers Dis 2020; 72:215-228. [PMID: 31561373 PMCID: PMC6839606 DOI: 10.3233/jad-190691] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background: Type 2 diabetes (T2DM) increases the risk for Alzheimer’s disease (AD) but not for AD neuropathology. The association between T2DM and AD is assumed to be mediated through vascular mechanisms. However, insulin resistance (IR), the hallmark of T2DM, has been shown to associate with AD neuropathology and cognitive decline. Objective: To evaluate if midlife IR predicts late-life cognitive performance and cerebrovascular lesions (white matter hyperintensities and total vascular burden), and whether cerebrovascular lesions and brain amyloid load are associated with cognitive functioning. Methods: This exposure-to-control follow-up study examined 60 volunteers without dementia (mean age 70.9 years) with neurocognitive testing, brain 3T-MRI and amyloid-PET imaging. The volunteers were recruited from the Finnish Health 2000 survey (n = 6062) to attend follow-up examinations in 2014–2016 according to their insulin sensitivity in 2000 and their APOE genotype. The exposure group (n = 30) had IR in 2000 and the 30 controls had normal insulin sensitivity. There were 15 APOEɛ4 carriers per group. Statistical analyses were performed with multivariable linear models. Results: At follow-up the IR+group performed worse on executive functions (p = 0.02) and processing speed (p = 0.007) than the IR- group. The groups did not differ in cerebrovascular lesions. No associations were found between cerebrovascular lesions and neurocognitive test scores. Brain amyloid deposition associated with slower processing speed. Conclusion: Midlife IR predicted poorer executive functions and slower processing speed, but not cerebrovascular lesions. Brain amyloid deposition was associated with slower processing speed. The association between midlife IR and late-life cognition might not be mediated through cerebrovascular lesions measured here.
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Affiliation(s)
- Sini Toppala
- Turku PET Centre, University of Turku, Finland.,Turku City Hospital, University of Turku, Finland
| | | | | | - Semi Helin
- Turku PET Centre, University of Turku, Finland
| | - Saija Hurme
- Department of Biostatistics, University of Turku, Finland
| | - Jarkko Johansson
- Turku PET Centre, University of Turku, Finland.,Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Antti Jula
- National Institute for Health and Welfare, Turku, Finland
| | - Mira Karrasch
- Department of Psychology, Åbo Akademi University, Turku, Finland
| | | | - Hanna Laine
- Turku City Hospital, University of Turku, Finland.,Department of Medicine, University of Turku, Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, Turku University and Turku University Hospital, Turku, Finland
| | - Matti Viitanen
- Turku City Hospital, University of Turku, Finland.,Clinical Geriatrics, Karolinska Institutet, Karolinska University Hospital, Huddinge, Sweden
| | - Juha O Rinne
- Turku PET Centre, University of Turku, Finland.,Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
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18
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Mårtensson G, Håkansson C, Pereira JB, Palmqvist S, Hansson O, van Westen D, Westman E. Medial temporal atrophy in preclinical dementia: Visual and automated assessment during six year follow-up. NEUROIMAGE-CLINICAL 2020; 27:102310. [PMID: 32580125 PMCID: PMC7317671 DOI: 10.1016/j.nicl.2020.102310] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 05/10/2020] [Accepted: 06/05/2020] [Indexed: 12/26/2022]
Abstract
Visual MTA ratings can detect longitudinal changes in preclinical dementia patients. Medial temporal atrophy rate is greater in individuals with an AD biomarker profile. Visual MTA ratings provide a robust alternative to automated measures.
Medial temporal lobe (MTL) atrophy is an important morphological marker of many dementias and is closely related to cognitive decline. In this study we aimed to characterize longitudinal progression of MTL atrophy in 93 individuals with subjective cognitive decline and mild cognitive impairment followed up over six years, and to assess if clinical rating scales are able to detect these changes. All MRI images were visually rated according to Scheltens’ scale of medial temporal atrophy (MTA) by two neuroradiologists and AVRA, a software for automated MTA ratings. The images were also segmented using FreeSurfer’s longitudinal pipeline in order to compare the MTA ratings to volumes of the hippocampi and inferior lateral ventricles. We found that MTL atrophy rates increased with CSF biomarker abnormality, used to define preclinical stages of Alzheimer’s Disease. Both AVRA’s and the radiologists’ MTA ratings showed similar longitudinal trends as the subcortical volumes, suggesting that visual rating scales provide a valid alternative to automatic segmentations. Our results further showed that it took more than 8 years on average for individuals with mild cognitive impairment, and an Alzheimer’s disease biomarker profile, to increase the MTA score by one. This suggests that discrete MTA ratings are too coarse for tracking individual MTL atrophy in short time spans. While the MTA scores from each radiologist showed strong correlations to subcortical volumes, the inter-rater agreement was low. We conclude that the main limitation of quantifying MTL atrophy with visual ratings in clinics is the subjectiveness of the assessment.
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Affiliation(s)
- Gustav Mårtensson
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden.
| | - Claes Håkansson
- Diagnostic Radiology, Institution for Clinical Sciences, Lund University, Lund, Sweden
| | - Joana B Pereira
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden; Memory Clinic, Skåne University Hospital, Malmä, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden; Memory Clinic, Skåne University Hospital, Malmä, Sweden
| | - Danielle van Westen
- Diagnostic Radiology, Institution for Clinical Sciences, Lund University, Lund, Sweden; Image and Function, Skåne University Hospital, Lund, Sweden
| | - Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden; Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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19
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Rhodius-Meester HFM, van Maurik IS, Koikkalainen J, Tolonen A, Frederiksen KS, Hasselbalch SG, Soininen H, Herukka SK, Remes AM, Teunissen CE, Barkhof F, Pijnenburg YAL, Scheltens P, Lötjönen J, van der Flier WM. Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy. PLoS One 2020; 15:e0226784. [PMID: 31940390 PMCID: PMC6961870 DOI: 10.1371/journal.pone.0226784] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/03/2019] [Indexed: 11/28/2022] Open
Abstract
INTRODUCTION An accurate and timely diagnosis for Alzheimer's disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination. METHODS We included 535 subjects (139 controls, 286 Alzheimer's disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients. RESULTS The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed). CONCLUSIONS We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.
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Affiliation(s)
- Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Internal Medicine, Geriatric Medicine section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Ingrid S van Maurik
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | | | - Antti Tolonen
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Kristian S Frederiksen
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Steen G Hasselbalch
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Hilkka Soininen
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Sanna-Kaisa Herukka
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Anne M Remes
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Research Neurology, Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
- MRC Oulu, Oulu University Hospital, Oulu, Finland
| | - Charlotte E Teunissen
- Neurochemistry Lab and Biobank, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
- Institutes of Neurology and Healthcare Engineering, UCL, London, England, United Kingdom
| | - Yolande A L Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | | | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
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20
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Haller S, Montandon ML, Rodriguez C, Garibotto V, Herrmann FR, Giannakopoulos P. Hippocampal Volume Loss, Brain Amyloid Accumulation, and APOE Status in Cognitively Intact Elderly Subjects. NEURODEGENER DIS 2019; 19:139-147. [PMID: 31846965 DOI: 10.1159/000504302] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 10/21/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Hippocampal volume loss (HVL), PET-documented brain amyloid accumulation, and APOE-ε4 status are predictive biomarkers of the transition from mild cognitive impairment to Alzheimer disease (AD). In asymptomatic cases, the role of these biomarkers remains ambiguous. In contrast to the idea that HVL occurs in late phases of neurodegeneration, recent contributions indicate that it might occur before abnormal amyloid PET occurrence in elderly subjects and that its severity could be only marginally related to APOE variants. Using a longitudinal design, we examined the determinants of HVL in our sample, i.e., brain amyloid burden and the presence of APOE-ε4, and made a longitudinal assessment of cognitive functions. METHODS We performed a 4.5-year longitudinal study on 81 elderly community dwellers (all right-handed;, 48 (59.3%) women; mean age 73.7 ± 3.7 years) including MRI at baseline and follow-up, PET amyloid during follow-up, neuropsychological assessment at 18 and 54 months, and APOE genotyping. All cases were assessed using a continuous cognitive score (CCS) that took into account the global evolution of neuropsychological performance. Linear regression models were used to identify predictors of HVL. RESULTS There was a negative association between the CCS and HVL bilaterally. In multivariate models adjusting for demographic variables, the presence of APOE-ε4 was related to increased HVL bilaterally. A trend of significance was observed with respect to the impact of amyloid positivity on HVL in the left hemisphere. No significant interaction was found between amyloid positivity and the APOE-ε4 allele. CONCLUSION The progressive decrement of neuropsychological performance is associated with HVL long before the emergence of clinically overt symptoms. In this cohort of healthy individuals, the presence of the APOE-ε4 allele was shown to be an independent predictor of worst hippocampal integrity in asymptomatic cases independently of amyloid positivity.
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Affiliation(s)
- Sven Haller
- CIRD Centre d'Imagerie Rive Droite, Geneva, Switzerland, .,Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden, .,Faculty of Medicine, University of Geneva, Geneva, Switzerland,
| | - Marie-Louise Montandon
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.,Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Cristelle Rodriguez
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Division of Institutional Measures, Medical Direction, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Panteleimon Giannakopoulos
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Division of Institutional Measures, Medical Direction, Geneva University Hospitals, Geneva, Switzerland
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