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Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI

Published on Aug 1, 2017in European Radiology3.96
· DOI :10.1007/s00330-016-4691-x
Esther E. Bron12
Estimated H-index: 12
(EUR: Erasmus University Rotterdam),
Marion Smits25
Estimated H-index: 25
(EUR: Erasmus University Rotterdam)
+ 6 AuthorsStefan Klein25
Estimated H-index: 25
(EUR: Erasmus University Rotterdam)
Cite
Abstract
Objectives To investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classification of Alzheimer's disease (AD), frontotemporal dementia (FTD), and controls.
  • References (41)
  • Citations (15)
Cite
References41
Newest
Published on Jun 1, 2016in Radiology7.61
Christiane Möller14
Estimated H-index: 14
,
Yolande A.L. Pijnenburg45
Estimated H-index: 45
+ 12 AuthorsJohn VanSwieten68
Estimated H-index: 68
This study shows that image-based machine learning techniques can be used to distinguish between disease-specific gray matter atrophy patterns in patients with Alzheimer disease and in patients with behavioral variant frontotemporal dementia by using T1-weighted structural MR imaging for single-subject diagnosis.
Published on Jan 1, 2016in European Radiology3.96
Rebecca M. E. Steketee6
Estimated H-index: 6
(EUR: Erasmus University Rotterdam),
Esther E. Bron12
Estimated H-index: 12
(EUR: Erasmus University Rotterdam)
+ 8 AuthorsA. van der Lugt65
Estimated H-index: 65
(EUR: Erasmus University Rotterdam)
Objective To investigate arterial spin labeling (ASL)-MRI for the early diagnosis of and differentiation between the two most common types of presenile dementia: Alzheimer’s disease (AD) and frontotemporal dementia (FTD), and for distinguishing age-related from pathological perfusion changes.
Published on Aug 1, 2015in Medical Image Analysis8.88
Bilwaj Gaonkar10
Estimated H-index: 10
,
Russell T. Shinohara21
Estimated H-index: 21
(UPenn: University of Pennsylvania),
Christos Davatzikos76
Estimated H-index: 76
Abstract Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision,...
Published on Jul 1, 2015in Alzheimers & Dementia14.42
Clifford R Jr. Jack133
Estimated H-index: 133
(Mayo Clinic),
Josephine Barnes41
Estimated H-index: 41
(UCL: University College London)
+ 40 AuthorsCharles D. Smith117
Estimated H-index: 117
(UC Davis: University of California, Davis)
Abstract Introduction Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. Methods We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. Results Contributions of the MRI core include creating sta...
Published on Jun 1, 2015in Human Brain Mapping4.55
Martin Dyrba11
Estimated H-index: 11
(German Center for Neurodegenerative Diseases),
Michel J. Grothe25
Estimated H-index: 25
(German Center for Neurodegenerative Diseases)
+ 1 AuthorsStefan J. Teipel45
Estimated H-index: 45
(German Center for Neurodegenerative Diseases)
Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance i...
Published on Feb 1, 2015in Alzheimers & Dementia14.42
Jennifer Harris10
Estimated H-index: 10
(University of Manchester),
Jennifer C. Thompson32
Estimated H-index: 32
(Salford Royal NHS Foundation Trust)
+ 7 AuthorsMatthew Jones16
Estimated H-index: 16
(University of Manchester)
Abstract Background Clinical criteria are important for improving diagnostic accuracy and ensuring comparability of patient cohorts in research studies. Objective The aim was to assess the National Institute on Aging and Alzheimer's Association (NIA-AA) criteria for Alzheimer's disease (AD) dementia in AD and frontotemporal lobar degeneration (FTLD). Methods Two hundred twelve consecutive patients with pathologically confirmed AD or FTLD who were clinically assessed in a specialist cognitive uni...
Published on Jan 1, 2015in Magnetic Resonance in Medicine3.86
David C. Alsop69
Estimated H-index: 69
(Harvard University),
John A. Detre84
Estimated H-index: 84
(UPenn: University of Pennsylvania)
+ 11 AuthorsMarion Smits25
Estimated H-index: 25
(EUR: Erasmus University Rotterdam)
This review provides a summary statement of recommended implementations of arterial spin labeling (ASL) for clinical applications. It is a consensus of the ISMRM Perfusion Study Group and the European ASL in Dementia consortium, both of whom met to reach this consensus in October 2012 in Amsterdam. Although ASL continues to undergo rapid technical development, we believe that current ASL methods are robust and ready to provide useful clinical information, and that a consensus statement on recomm...
Published on Sep 1, 2014in Human Brain Mapping4.55
Corey T. McMillan32
Estimated H-index: 32
(UPenn: University of Pennsylvania),
Brian B. Avants41
Estimated H-index: 41
(UPenn: University of Pennsylvania)
+ 3 AuthorsMurray Grossman85
Estimated H-index: 85
(UPenn: University of Pennsylvania)
Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodege- nerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer's disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase the feasibility of clinical trials. We assessed three broad catego- ries of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: gl...
Cited By15
Newest
Patricio Donnelly‐Kehoe1
Estimated H-index: 1
,
G. Pascariello + 14 AuthorsCecilia Serrano4
Estimated H-index: 4
Abstract Introduction Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem. Methods We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy control...
Published on Mar 20, 2019in Alzheimer's Research & Therapy
Marie Bruun (Copenhagen University Hospital), Kristian Steen Frederiksen12
Estimated H-index: 12
(Copenhagen University Hospital)
+ 20 AuthorsDaniel Rueckert69
Estimated H-index: 69
(Imperial College London)
Background In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics.
Published on Sep 1, 2019in Advances in Medical Sciences2.08
Maksymilian Aleksander Brzezicki1
Estimated H-index: 1
(UoB: University of Bristol),
Matthew David Kobetić1
Estimated H-index: 1
(UoB: University of Bristol)
+ 1 AuthorsCatherine Pennington8
Estimated H-index: 8
(UoB: University of Bristol)
Abstract Purpose Frontotemporal dementia (FTD) is a neurodegenerative disorder associated with a poor prognosis and a substantial reduction in quality of life. The rate of misdiagnosis of FTD is very high, with patients often waiting for years without a firm diagnosis. This study investigates the current state of the misdiagnosis of FTD using a novel artificial intelligence-based algorithm. Patients & Methods An artificial intelligence algorithm has been developed to retrospectively analyse the ...
Published on Jun 15, 2019in Journal of Neurology, Neurosurgery, and Psychiatry8.27
Rogier A. Feis2
Estimated H-index: 2
,
Mark J.R.J. Bouts2
Estimated H-index: 2
+ 7 AuthorsSerge A.R.B. Rombouts64
Estimated H-index: 64
Background Multimodal MRI-based classification may aid early frontotemporal dementia (FTD) diagnosis. Recently, presymptomatic FTD mutation carriers, who have a high risk of developing FTD, were separated beyond chance level from controls using MRI-based classification. However, it is currently unknown how these scores from classification models progress as mutation carriers approach symptom onset. In this longitudinal study, we investigated multimodal MRI-based classification scores between pre...
Published on Apr 8, 2019in Journal of Alzheimer's Disease3.70
Paul Zhutovsky (UvA: University of Amsterdam), Everard G.B. Vijverberg (VUmc: VU University Medical Center)+ 5 AuthorsAnnemiek Dols11
Estimated H-index: 11
(VUmc: VU University Medical Center)
Patients with behavioral variant of frontotemporal dementia (bvFTD) initially may only show behavioral and/or cognitive symptoms that overlap with other neurological and psychiatric disorders. The diagnostic accuracy is dependent on progressive symptoms worsening and frontotemporal abnormalities on neuroimaging findings. Predictive biomarkers could facilitate the early detection of bvFTD. Objective: To determine the prognostic accuracy of clinical and structural MRI data using a support vector m...
Published on Jan 1, 2019
In view of the low efficiency in the measurement of multi-source information synergy structure for the smart city and other problems, a kind of computer aided measurement of smart city multi-source information synergy structure (hereinafter referred to as CAMMISS for short) that is dependent on the adaptive artificial immune network algorithm (hereinafter referred to as AAINA for short) is put forward. Firstly, the number of the input and output nodes in the multi-source information synergy stru...
Published on Jan 1, 2019in IEEE Reviews in Biomedical Engineering
Rishad Ahmed1
Estimated H-index: 1
(UJN: University of Jinan),
Yuan Zhang11
Estimated H-index: 11
(UJN: University of Jinan)
+ 3 AuthorsHongen Liao20
Estimated H-index: 20
(THU: Tsinghua University)
Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review pape...
Published on Jan 1, 2019in NeuroImage: Clinical3.94
Jun Pyo Kim (SMC: Samsung Medical Center), Jeonghun Kim1
Estimated H-index: 1
(KU: Korea University)
+ 10 AuthorsJesse A. Brown18
Estimated H-index: 18
(UCSF: University of California, San Francisco)
Abstract Background In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at...
Published on Jan 1, 2019in NeuroImage: Clinical3.94
Marie Bruun2
Estimated H-index: 2
(UCPH: University of Copenhagen),
Juha Koikkalainen21
Estimated H-index: 21
+ 14 AuthorsGunhild Waldemar58
Estimated H-index: 58
(UCPH: University of Copenhagen)
Abstract Background Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. Methods In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory ...
Published on Oct 1, 2018in Brain Research2.93
Zhizheng Zhuo3
Estimated H-index: 3
(Capital Medical University),
Xiao Mo2
Estimated H-index: 2
(Capital Medical University)
+ 2 AuthorsHaiyun Li6
Estimated H-index: 6
(Capital Medical University)
Abstract Purpose To investigate the subtle functional connectivity alterations of aMCI based on AAL atlas with 1024 regions (AAL_1024 atlas). Materials and methods Functional MRI images of 32 aMCI patients (Male/Female: 15/17, Ages: 66.8 ± 8.36 y) and 35 normal controls (Male/Female:13/22, Ages: 62.4 ± 8.14 y) were obtained in this study. Firstly, functional connectivity networks were constructed by Pearson’s Correlation based on the subtle AAL_1024 atlas. Then, local and global network paramete...
View next paperSensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia