3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer’s disease sub-scores
Tóm tắt
Different aspects of cognitive functions are affected in patients with Alzheimer’s disease. To date, little is known about the associations between features from brain-imaging and individual Alzheimer’s disease (AD)-related cognitive functional changes. In addition, how these associations differ among different imaging modalities is unclear. Here, we trained and investigated 3D convolutional neural network (CNN) models that predicted sub-scores of the 13-item Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS–Cog13) based on MRI and FDG–PET brain-imaging data. Analysis of the trained network showed that each key ADAS–Cog13 sub-score was associated with a specific set of brain features within an imaging modality. Furthermore, different association patterns were observed in MRI and FDG–PET modalities. According to MRI, cognitive sub-scores were typically associated with structural changes of subcortical regions, including amygdala, hippocampus, and putamen. Comparatively, according to FDG–PET, cognitive functions were typically associated with metabolic changes of cortical regions, including the cingulated gyrus, occipital cortex, middle front gyrus, precuneus cortex, and the cerebellum. These findings brought insights into complex AD etiology and emphasized the importance of investigating different brain-imaging modalities.
Tài liệu tham khảo
Nelson PT et al (2011) Alzheimer’s disease is not “brain aging”: neuropathological, genetic, and epidemiological human studies. Acta Neuropathol 121:571–587
Kent SA, Spires-Jones TL, Durrant CS (2020) The physiological roles of tau and Aβ: implications for Alzheimer’s disease pathology and therapeutics. Acta Neuropathol. https://doi.org/10.1007/s00401-020-02196-w
Corey-Bloom J (2002) The ABC of Alzheimer’s disease: cognitive changes and their management in Alzheimer’s disease and related dementias. Int Psychogeriatr 14(Suppl 1):51–75. https://doi.org/10.1017/s1041610203008664
Coughlan G, Laczo J, Hort J, Minihane AM, Hornberger M (2018) Spatial navigation deficits - overlooked cognitive marker for preclinical Alzheimer disease? Nat Rev Neurol 14:496–506. https://doi.org/10.1038/s41582-018-0031-x
Whitwell JL et al (2018) Imaging correlations of tau, amyloid, metabolism, and atrophy in typical and atypical Alzheimer’s disease. Alzheimers Dement 14:1005–1014
Dukart J et al (2013) Relationship between imaging biomarkers, age, progression and symptom severity in Alzheimer’s disease. NeuroImage Clin 3:84–94
Cano SJ et al (2010) The ADAS-cog in Alzheimer’s disease clinical trials: psychometric evaluation of the sum and its parts. J Neurol Neurosurg Psychiatry 81:1363–1368
Balsis S, Benge JF, Lowe DA, Geraci L, Doody RS (2015) How do scores on the ADAS-cog, MMSE, and CDR-SOB correspond? Clin Neuropsychol 29:1002–1009
Johnson KA, Fox NC, Sperling RA, Klunk WE (2012) Brain imaging in Alzheimer disease. Cold Spring Harb Perspect Med 2:a006213. https://doi.org/10.1101/cshperspect.a006213
Weiner MW et al (2015) 2014 update of the Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement 11:e1-120. https://doi.org/10.1016/j.jalz.2014.11.001
Choi H et al (2020) Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer’s disease to Parkinson’s disease. Eur J Nucl Med Mol Imaging 47:403–412. https://doi.org/10.1007/s00259-019-04538-7
Mullins R, Reiter D, Kapogiannis D (2018) Magnetic resonance spectroscopy reveals abnormalities of glucose metabolism in the Alzheimer’s brain. Ann Clin Transl Neurol 5:262–272. https://doi.org/10.1002/acn3.530
Scheltens P et al (1992) Atrophy of medial temporal lobes on MRI in “probable” Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 55:967–972. https://doi.org/10.1136/jnnp.55.10.967
Ning K et al (2018) Classifying Alzheimer’s disease with brain imaging and genetic data using a neural network framework. Neurobiol Aging 68:151–158. https://doi.org/10.1016/j.neurobiolaging.2018.04.009
Young J et al (2013) Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Neuroimage Clin 2:735–745. https://doi.org/10.1016/j.nicl.2013.05.004
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313:504–507. https://doi.org/10.1126/science.1127647
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539
Levakov G, Rosenthal G, Shelef I, Raviv TR, Avidan G (2020) From a deep learning model back to the brain-Identifying regional predictors and their relation to aging. Hum Brain Mapp 41:3235–3252. https://doi.org/10.1002/hbm.25011
Yang C, Rangarajan A, Ranka S (2018) Visual explanations from deep 3D convolutional neural networks for Alzheimer’s disease classification. AMIA Annu Symp Proc 2018:1571–1580
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. ECCV 2014: Computer Vision.
Breiman L (2001) Random forests. Mach Learn 45:5–32
Bergstra J, Yoshua B (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305
Fischl B (2012) FreeSurfer. Neuroimage 62:774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021
Mazziotta J et al (2001) A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc B Biol Sci 356:1293–1322
Shinohara RT et al (2014) Statistical normalization techniques for magnetic resonance imaging. NeuroImage Clin 6:9–19
Jagust WJ et al (2010) The Alzheimer’s disease neuroimaging initiative positron emission tomography core. Alzheimers Dement 6:221–229
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Martín Abadi AA, Barham P, Brevdo E, et al (2015) TensorFlow: large-scale machine learning on heterogeneous systems.
Argyrious A, Evgenious T, Pontil M (2006) Multi-task feature learning. NeurIPS, 41–48
He K, Zhang X, Ren S, Sun J Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980
Huang G et al (2017) Snapshot ensembles: Train 1, get m for free. arXiv preprint arXiv:1704.00109
Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J (2008) LIBLINEAR: A library for large linear classification. J Mach Learn Res 9:1871–1874
Bentley J (1975) Multidimensional binary search trees used for associative searching. CACM 18(9):509–517
Bennett D, Schneider J, Arvanitakis Z, Wilson R (2012) Overview and findings from the religious orders study. Curr Alzheimer Res 9:628–645
Landau SM et al (2012) Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol 72:578–586. https://doi.org/10.1002/ana.23650
Podhorna J, Krahnke T, Shear M, Harrison JE, Alzheimer׳s Disease Neuroimaging Initiative (2016) Alzheimer’s Disease Assessment Scale-Cognitive subscale variants in mild cognitive impairment and mild Alzheimer’s disease: change over time and the effect of enrichment strategies. Alzheimers Res Ther 8:8. https://doi.org/10.1186/s13195-016-0170-5
Selkoe DJ (2019) Alzheimer disease and aducanumab: adjusting our approach. Nat Rev Neurol 15:365–366
Du J et al (2009) Metabolites of cerebellar neurons and hippocampal neurons play opposite roles in pathogenesis of Alzheimer’s disease. PLoS ONE 4:e5530. https://doi.org/10.1371/journal.pone.0005530
Ishii K et al (1997) Reduction of cerebellar glucose metabolism in advanced Alzheimer’s disease. J Nucl Med 38:925–928
Selvaraju RR et al (2017) Grad-CAM: Visual explanations from deep networks via gradient-based localization. 618–626
Tondelli M et al (2012) Structural MRI changes detectable up to ten years before clinical Alzheimer’s disease. Neurobiol Aging 33(825):e825-836. https://doi.org/10.1016/j.neurobiolaging.2011.05.018
Cabral C, Morgado PM, Campos Costa D, Silveira M, Alzheimer׳s Disease Neuroimaging Initiative (2015) Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages. Comput Biol Med 58:101–109. https://doi.org/10.1016/j.compbiomed.2015.01.003