Translating state-of-the-art brain magnetic resonance imaging (MRI) techniques into clinical practice: multimodal MRI differentiates dementia subtypes in a traditional clinical setting

被引:13
作者
Kuhn, Taylor [1 ]
Becerra, Sergio [2 ]
Duncan, John [2 ]
Spivak, Norman [1 ]
Dang, Bianca Huan [1 ]
Habelhah, Barshen [2 ]
Mahdavi, Kennedy D. [2 ]
Mamoun, Michael [3 ]
Whitney, Michael [4 ]
Pereles, F. Scott [4 ]
Bystritsky, Alexander [1 ]
Jordan, Sheldon E. [2 ,5 ]
机构
[1] Univ Calif Los Angeles, Dept Psychiat & Biobehav Sci, Los Angeles, CA 90095 USA
[2] Neurol Management Assoc, Los Angeles, CA USA
[3] CNS Hlth, Los Angeles, CA USA
[4] RadAlliance, Los Angeles, CA USA
[5] Calif State Univ Los Angeles, Dept Neurol, Los Angeles, CA USA
关键词
Magnetic resonance imaging (MRI); neurodegenerative; dementia; Alzheimer's; Parkinson's; MILD COGNITIVE IMPAIRMENT; DIAGNOSTIC-ACCURACY; ALZHEIMERS-DISEASE; PARKINSONS-DISEASE; NEURODEGENERATIVE DISEASES; FRONTOTEMPORAL DEMENTIA; BEHAVIORAL VARIANT; LEWY BODIES; FDG-PET; METAANALYSIS;
D O I
10.21037/qims-20-1355
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background: This study sought to validate the clinical utility of multimodal magnetic resonance imaging (MRI) techniques in the assessment of neurodegenerative disorders. We intended to demonstrate that advanced neuroimaging techniques commonly used in research can effectively be employed in clinical practice to accurately differentiate heathy aging and dementia subtypes. Methods: Twenty patients with dementia of the Alzheimer's type (DAT) and 18 patients with Parkinson's disease dementia (PDD) were identified using gold-standard techniques. Twenty-three healthy, age and sex matched control participants were also recruited. All participants underwent multimodal MRI including T1 structural, diffusion tensor imaging (DTI), arterial spin labeling (ASL), and magnetic resonance spectroscopy (MRS). MRI modalities were evaluated by trained neuroimaging readers and were separately assessed using cross-validated, iterative discriminant function analyses with subsequent feature reduction techniques. In this way, each modality was evaluated for its ability to differentiate patients with dementia from healthy controls as well as to differentiate dementia subtypes. Results: Following individual and group feature reduction, each of the multimodal MRI metrics except MRS successfully differentiated healthy aging from dementia and also demonstrated distinct dementia subtypes. Using the following ten metrics, excellent separation (95.5% accuracy, 92.3% sensitivity; 100.0% specificity) was achieved between healthy aging and neurodegenerative conditions: volume of the left frontal pole, left occipital pole, right posterior superior temporal gyrus, left posterior cingulate gyrus, right planum temporale; perfusion of the left hippocampus and left occipital lobe; fractional anisotropy (FA) of the forceps major and bilateral anterior thalamic radiation. Using volume of the left frontal pole, right posterior superior temporal gyrus, left posterior cingulate gyrus, perfusion of the left hippocampus and left occipital lobe; FA of the forceps major and bilateral anterior thalamic radiation, neurodegenerative subtypes were accurately differentiated as well (87.8% accuracy, 95.2% sensitivity; 85.0% specificity). Conclusions: Regional volumetrics, DTI metrics, and ASL successfully differentiated dementia patients from controls with sufficient sensitivity to differentiate dementia subtypes. Similarly, feature reduction results suggest that advanced analyses can meaningfully identify brain regions with the most positive predictive value and discriminant validity. Together, these advanced neuroimaging techniques can contribute significantly to diagnosis and treatment planning for individual patients.
引用
收藏
页码:4056 / +
页数:22
相关论文
共 59 条
  • [1] 2018 Alzheimer's disease facts and figures
    不详
    [J]. ALZHEIMERS & DEMENTIA, 2018, 14 (03) : 367 - 425
  • [2] In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease
    Baron, JC
    Chételat, G
    Desgranges, B
    Perchey, G
    Landeau, B
    de la Sayette, V
    Eustache, F
    [J]. NEUROIMAGE, 2001, 14 (02) : 298 - 309
  • [3] Bishop C.M., 2006, Pattern Recognition and Machine Learning
  • [4] CSF markers for incipient Alzheimer's disease
    Blennow, K
    Hampel, H
    [J]. LANCET NEUROLOGY, 2003, 2 (10) : 605 - 613
  • [5] Human Lateral Frontal Pole Contributes to Control over Emotional Approach Avoidance Actions
    Bramson, Bob
    Folloni, Davide
    Verhagen, Lennart
    Hartogsveld, Bart
    Mars, Rogier B.
    Toni, Ivan
    Roelofs, Karin
    [J]. JOURNAL OF NEUROSCIENCE, 2020, 40 (14) : 2925 - 2934
  • [6] Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge
    Bron, Esther E.
    Smits, Marion
    van der Flier, Wiesje M.
    Vrenken, Hugo
    Barkhof, Frederik
    Scheltens, Philip
    Papma, Janne M.
    Steketee, Rebecca M. E.
    Orellana, Carolina Mendez
    Meijboom, Rozanna
    Pinto, Madalena
    Meireles, Joana R.
    Garrett, Carolina
    Bastos-Leite, Antonio J.
    Abdulkadir, Ahmed
    Ronneberger, Olaf
    Amoroso, Nicola
    Bellotti, Roberto
    Cardenas-Pena, David
    Alvarez-Meza, Andres M.
    Dolph, Chester V.
    Iftekharuddin, Khan M.
    Eskildsen, Simon F.
    Coupe, Pierrick
    Fonov, Vladimir S.
    Franke, Katja
    Gaser, Christian
    Ledig, Christian
    Guerrero, Ricardo
    Tong, Tong
    Gray, Katherine R.
    Moradi, Elaheh
    Tohka, Jussi
    Routier, Alexandre
    Durrleman, Stanley
    Sarica, Alessia
    Di Fatta, Giuseppe
    Sensi, Francesco
    Chincarini, Andrea
    Smith, Garry M.
    Stoyanov, Zhivko V.
    Sorensen, Lauge
    Nielsen, Mads
    Tangaro, Sabina
    Inglese, Paolo
    Wachinger, Christian
    Reuter, Martin
    van Swieten, John C.
    Niessen, Wiro J.
    Klein, Stefan
    [J]. NEUROIMAGE, 2015, 111 : 562 - 579
  • [7] Feature reduction for classification of multidimensional data
    Brunzell, H
    Eriksson, J
    [J]. PATTERN RECOGNITION, 2000, 33 (10) : 1741 - 1748
  • [8] FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort
    Caminiti, Silvia Paola
    Ballarini, Tommaso
    Sala, Arianna
    Cerami, Chiara
    Presotto, Luca
    Santangelo, Roberto
    Fallanca, Federico
    Vanoli, Emilia Giovanna
    Gianolli, Luigi
    Iannaccone, Sandro
    Magnani, Giuseppe
    Perani, Daniela
    [J]. NEUROIMAGE-CLINICAL, 2018, 18 : 167 - 177
  • [9] Partial Volume Correction of Multiple Inversion Time Arterial Spin Labeling MRI Data
    Chappell, M. A.
    Groves, A. R.
    MacIntosh, B. J.
    Donahue, M. J.
    Jezzard, P.
    Woolrich, M. W.
    [J]. MAGNETIC RESONANCE IN MEDICINE, 2011, 65 (04) : 1173 - 1183
  • [10] Variational Bayesian Inference for a Nonlinear Forward Model
    Chappell, Michael A.
    Groves, Adrian R.
    Whitcher, Brandon
    Woolrich, Mark W.
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2009, 57 (01) : 223 - 236