Machine learning in neuroimaging: from research to clinical practice

Springer Science and Business Media LLC - Tập 62 Số S1 - Trang 1-10 - 2022
Karl-Heinz Nenning1, Georg Langs2
1Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
2Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Währinger Gürtel 18–20, 1090, Vienna, Austria

Tóm tắt

AbstractNeuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain’s morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.

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Tài liệu tham khảo

Symms M, Jäger HR, Schmierer K, Yousry TA (2004) A review of structural magnetic resonance neuroimaging. J Neurol Neurosurg Psychiatry 75(9):1235–1244

Raichle ME (1998) Behind the scenes of functional brain imaging: a historical and physiological perspective. Proc Natl Acad Sci U S A 95(3):765–772

Mateos-Pérez JM, Dadar M, Lacalle-Aurioles M, Iturria-Medina Y, Zeighami Y, Evans AC (2018) Structural neuroimaging as clinical predictor: a review of machine learning applications. Neuroimage Clin 20:506–522

Silva MA, See AP, Essayed WI, Golby AJ, Tie Y (2018) Challenges and techniques for presurgical brain mapping with functional MRI. Neuroimage Clin 17:794–803

Petrella JR et al (2006) Preoperative functional MR imaging localization of language and motor areas: effect on therapeutic decision making in patients with potentially resectable brain tumors. Radiology 240(3):793–802

Lee MH, Smyser CD, Shimony JS (2013) Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol 34(10):1866–1872

Leuthardt EC et al (2018) Integration of resting state functional MRI into clinical practice—a large single institution experience. PLoS ONE 13(6):e198349

Specht K (2020) Current challenges in translational and clinical fMRI and future directions. Front Psychiatry. https://doi.org/10.3389/fpsyt.2019.00924

Wu C et al (2021) Clinical applications of magnetic resonance imaging based functional and structural connectivity. Neuroimage 244:118649

Logothetis NK (2008) What we can do and what we cannot do with fMRI. Nature 453(7197):869–878

Vaquero JJ, Kinahan P (2015) Positron emission tomography: current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Annu Rev Biomed Eng 17:385–414

Soares DP, Law M (2009) Magnetic resonance spectroscopy of the brain: review of metabolites and clinical applications. Clin Radiol 64(1):12–21

Juweid ME, Cheson BD (2006) Positron-emission tomography and assessment of cancer therapy. N Engl J Med 354(5):496–507

Herholz K, Coope D, Jackson A (2007) Metabolic and molecular imaging in neuro-oncology. Lancet 6(8):711–724

Pelletier D et al (2014) Pathogenesis of multiple sclerosis: insights from molecular and metabolic imaging. Lancet Neurol 13(8):807–822

Meyer JH, Cervenka S, Kim MJ, Kreisl WC, Henter ID, Innis RB (2020) Neuroinflammation in psychiatric disorders: PET imaging and promising new targets. Lancet Psychiatry. https://doi.org/10.1016/S2215-0366(20)30255-8

Jirsa VK et al (2017) The virtual epileptic patient: individualized whole-brain models of epilepsy spread. Neuroimage 145:377–388

Cocchi L, Harding IH, Lord A, Pantelis C, Yucel M, Zalesky A (2014) Disruption of structure—function coupling in the schizophrenia connectome. Neuroimage Clin 4:779–787

Rosenthal G et al (2018) Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes. Nat Commun 9(1):2178

Fischl B (2012) FreeSurfer. Neuroimage 62(2):774–781

Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11(6):805–821

Ceccarelli A et al (2008) A voxel-based morphometry study of grey matter loss in MS patients with different clinical phenotypes. Neuroimage 42(1):315–322

Friston KJ, Holmes AP, Worsley KJ, Poline J‑P, Frith CD, Frackowiak RSJ (1994) Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 2(4):189–210

Kanwisher N, McDermott J, Chun MM (1997) The fusiform face area: a module in human extrastriate cortex specialized for face perception. J Neurosci 17(11):4302–4311

Kriegeskorte N, Goebel R, Bandettini P (2006) Information-based functional brain mapping. Proc Natl Acad Sci U S A 103(10):3863–3868

Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P (2001) Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539):2425–2430

Langs G, Menze BH, Lashkari D, Golland P (2011) Detecting stable distributed patterns of brain activation using Gini contrast. Neuroimage 56(2):497–507

Haxby JV et al (2011) A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72(2):404–416

Langs G et al (2014) Decoupling function and anatomy in atlases of functional connectivity patterns: language mapping in tumor patients. Neuroimage 103:462–475

Wachinger C, Reuter M, Klein T (2018) DeepNAT: deep convolutional neural network for segmenting neuroanatomy. Neuroimage 170:434–445

Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV (2019) VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2019.2897538

Menze BH et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024

Furtner J et al (2017) Survival prediction using temporal muscle thickness measurements on cranial magnetic resonance images in patients with newly diagnosed brain metastases. Eur Radiol. https://doi.org/10.1007/s00330-016-4707-6

Sabuncu MR (2015) Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinform 13(1):31

Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage. https://doi.org/10.1016/j.neuroimage.2005.02.018

Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20(1):45–57

Ding Y et al (2020) Using deep convolutional neural networks for neonatal brain image segmentation. Front Neurosci. https://doi.org/10.3389/fnins.2020.00207

Payette K et al (2021) An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset. Sci Data 8(1):1–14

Cai JC et al (2020) Fully automated segmentation of head CT neuroanatomy using deep learning. Radiol Artif Intell. https://doi.org/10.1148/ryai.2020190183

Ronneberger O, Fischer P, Brox T (2015) U‑net: convolutional networks for biomedical image segmentation. Med Image Comput Comput Assist Interv. https://doi.org/10.48550/arXiv.1505.04597

Mi E, Mauricaite R, Pakzad-Shahabi L, Chen J, Ho A, Williams M (2021) Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma. Br J Cancer 126(2):196–203

Park G et al (2021) White matter hyperintensities segmentation using the ensemble U‑Net with multi-scale highlighting foregrounds. Neuroimage 237:118140

Livne M et al (2019) A U‑Net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease. Front Neurosci. https://doi.org/10.3389/fnins.2019.00097

Chen X, Konukoglu E (2018) Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. http://arxiv.org/abs/1806.04972. Accessed 15 Feb 2022

Kickingereder P et al (2019) Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 20(5):728–740

Bakas S et al (2017) Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 4(1):1–13

Aerts HJWL et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

Zhou M et al (2018) Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol 39(2):208–216

Choi SW et al (2020) Multi-habitat radiomics unravels distinct phenotypic subtypes of glioblastoma with clinical and genomic significance. Cancers. https://doi.org/10.3390/cancers12071707

Kim Y, Cho H‑H, Kim ST, Park H, Nam D, Kong D‑S (2018) Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI. Neuroradiology 60(12):1297–1305

Kang D et al (2018) Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol 20(9):1251–1261

Jin B et al (2018) Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning. Epilepsia 59(5):982–992

Ganji Z, Hakak MA, Zamanpour SA, Zare H (2021) Automatic detection of focal cortical dysplasia type II in MRI: is the application of surface-based morphometry and machine learning promising? Front Hum Neurosci 15:608285

Lee HM et al (2020) Unsupervised machine learning reveals lesional variability in focal cortical dysplasia at mesoscopic scale. Neuroimage Clin 28:102438

Eshaghi A et al (2021) Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun 12(1):2078

Mouridsen K, Thurner P, Zaharchuk G (2020) Artificial intelligence applications in stroke. Stroke 51(8):2573–2579

Singh G et al (2021) Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer 125(5):641–657

Haxby JV (2012) Multivariate pattern analysis of fMRI: the early beginnings. Neuroimage 62(2):852–855

Mitchell TM et al (2008) Predicting human brain activity associated with the meanings of nouns. Science 320(5880):1191–1195

Kay KN, Naselaris T, Prenger RJ, Gallant JL (2008) Identifying natural images from human brain activity. Nature 452(7185):352–355

Huth AG, Lee T, Nishimoto S, Bilenko NY, Vu AT, Gallant JL (2016) Decoding the semantic content of natural movies from human brain activity. Front Syst Neurosci. https://doi.org/10.3389/fnsys.2016.00081

Martino FD et al (2008) Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuroimage 43(1):44–58. https://doi.org/10.1016/j.neuroimage.2008.06.037

Hanson SJ, Halchenko YO (2008) Brain reading using full brain support vector machines for object recognition: there is no ‘face’ identification area. Neural Comput 20(2):486–503

Farah MJ, Hutchinson JB, Phelps EA, Wagner AD (2014) Functional MRI-based lie detection: scientific and societal challenges. Nat Rev Neurosci 15(2):123–131

Horikawa T, Tamaki M, Miyawaki Y, Kamitani Y (2013) Neural decoding of visual imagery during sleep. Science 340(6132):639–642

Nishimoto S et al (2011) Reconstructing visual experiences from brain activity evoked by natural movies. Curr Biol 21(19):1641–1646

Huth AG, de Heer WA, Griffiths TL, Theunissen FE, Gallant JL (2016) Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532(7600):453–458

Frey M, Nau M, Doeller CF (2021) Magnetic resonance-based eye tracking using deep neural networks. Nat Neurosci 24(12):1772–1779

Schulz M‑A et al (2020) Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat Commun 11(1):4238

Abrol A et al (2021) Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning. Nat Commun 12(1):353

Nenning K‑H et al (2021) The impact of hippocampal impairment on task-positive and task-negative language networks in temporal lobe epilepsy. Clin Neurophysiol 132(2):404–411

Xu T et al (2020) Cross-species functional alignment reveals evolutionary hierarchy within the connectome. Neuroimage 223:117346

Sporns O (2012) Discovering the human connectome. MIT Press

Fornito A, Zalesky A, Breakspear M (2013) Graph analysis of the human connectome: promise, progress, and pitfalls. Neuroimage 80:426–444

Zalesky A, Fornito A, Bullmore ET (2010) Network-based statistic: identifying differences in brain networks. Neuroimage 53(4):1197–1207

Jakab A et al (2015) Disrupted developmental organization of the structural connectome in fetuses with corpus callosum agenesis. Neuroimage 111:277–288

Nenning K‑H et al (2020) Joint embedding: a scalable alignment to compare individuals in a connectivity space. Neuroimage 222:117232

Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y (2022) A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. Neuroimage 246:118774

Richards BA et al (2019) A deep learning framework for neuroscience. Nat Neurosci 22(11):1761–1770

Kriegeskorte N (2015) Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu Rev Vis Sci 1:417–446

Goulas A, Damicelli F, Hilgetag CC (2021) Bio-instantiated recurrent neural networks: Integrating neurobiology-based network topology in artificial networks. Neural Netw 142:608–618

Bengio Y, Lee D‑H, Bornschein J, Mesnard T, Lin Z (2015) Towards biologically plausible deep learning. http://arxiv.org/abs/1502.04156. Accessed 30 Aug 2022

Kriegeskorte N et al (2008) Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60(6):1126–1141

Khaligh-Razavi S‑M, Kriegeskorte N (2014) Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Comput Biol 10(11):e1003915. https://doi.org/10.1371/journal.pcbi.1003915

la Tour TD, Lu M, Eickenberg M (2021) A finer mapping of convolutional neural network layers to the visual cortex. https://openreview.net/forum?id=EcoKpq43Ul8 (SVRHM 2021 Workshop). Accessed 30 Aug 2022

Fox MD, Greicius M (2010) Clinical applications of resting state functional connectivity. Front Syst Neurosci 4:19

Du Y, Fu Z, Calhoun VD (2018) Classification and prediction of brain disorders using functional connectivity: promising but challenging. Front Neurosci. https://doi.org/10.3389/fnins.2018.00525

Nenning K‑H et al (2020) Distributed changes of the functional connectome in patients with glioblastoma. Sci Rep 10(1):18312

Stoecklein VM et al (2020) Resting-state fMRI detects alterations in whole brain connectivity related to tumor biology in glioma patients. Neuro Oncol 22(9):1388–1398

Foesleitner O et al (2020) Language network reorganization before and after temporal lobe epilepsy surgery. J Neurosurg 134(6):1–9

Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F (2018) Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Neuroimage Clin 17:16–23

Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F (2019) ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front Neuroinform. https://doi.org/10.3389/fninf.2019.00070

Damaraju E et al (2014) Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin 5:298–308

Siddiqui MK, Morales-Menendez R, Huang X, Hussain N (2020) A review of epileptic seizure detection using machine learning classifiers. Brain Inform 7(1):1–18

Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278

Dubreuil-Vall L, Ruffini G, Camprodon JA (2020) Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG. Front Neurosci. https://doi.org/10.3389/fnins.2020.00251

Klein A et al (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46(3):786–802

Cheng J, Dalca AV, Fischl B, Zöllei L, Alzheimer’s Disease Neuroimaging Initiative (2020) Cortical surface registration using unsupervised learning. Neuroimage 221:117161

Mueller S et al (2013) Individual variability in functional connectivity architecture of the human brain. Neuron 77(3):586–595

Schmitt JE, Raznahan A, Liu S, Neale MC (2021) The heritability of cortical folding: evidence from the human connectome project. Cereb Cortex 31(1):702–715

Wang D et al (2015) Parcellating cortical functional networks in individuals. Nat Neurosci 18(12):1853–1860

Kong R et al (2021) Individual-specific areal-level parcellations improve functional connectivity prediction of behavior. Cereb Cortex 31(10):4477–4500

Margulies DS et al (2016) Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A 113(44):12574–12579

Nenning K‑H, Liu H, Ghosh SS, Sabuncu MR, Schwartz E, Langs G (2017) Diffeomorphic functional brain surface alignment: functional demons. Neuroimage 156:456–465

Burger B et al (2022) Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment. Neuroimage 247:118770

Bazeille T, DuPre E, Richard H, Poline J‑B, Thirion B (2021) An empirical evaluation of functional alignment using inter-subject decoding. Neuroimage 245:118683