{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,8]],"date-time":"2026-11-08T05:21:18Z","timestamp":1794115278669,"version":"4.8.0"},"reference-count":195,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T00:00:00Z","timestamp":1734393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer\u2019s disease, Parkinson\u2019s disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques\u2014Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging\u2014in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and inflammation. Additionally, next-generation PET tracers targeting misfolded proteins, such as tau and alpha-synuclein, along with inflammatory markers, enhance the visualization and quantification of pathological processes in vivo. Deep learning models, including convolutional neural networks and multimodal transformers, further improve diagnostic accuracy by integrating multimodal imaging data and predicting disease progression. Despite challenges such as technical variability, data privacy concerns, and regulatory barriers, the potential of AI-enhanced neuroimaging to revolutionize early diagnosis and personalized treatment in neurodegenerative and neuro-ophthalmic disorders is immense. This review underscores the importance of ongoing efforts to validate, standardize, and implement these technologies to maximize their clinical impact.<\/jats:p>","DOI":"10.3390\/brainsci14121266","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T05:26:02Z","timestamp":1734413162000},"page":"1266","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies"],"prefix":"10.3390","volume":"14","author":[{"given":"Rahul","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1600 NW 10th Ave, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ethan","family":"Waisberg","sequence":"additional","affiliation":[{"name":"Department of Clinical Neurosciences, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4860-827X","authenticated-orcid":false,"given":"Joshua","family":"Ong","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, 1000 Wall St, Ann Arbor, MI 48105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phani","family":"Paladugu","sequence":"additional","affiliation":[{"name":"Sidney Kimmel Medical College, Thomas Jefferson University, 1025 Walnut St, Philadelphia, PA 19107, USA"},{"name":"Brigham and Women\u2019s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2955-7190","authenticated-orcid":false,"given":"Dylan","family":"Amiri","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Miami, 1301 Memorial Dr, Coral Gables, FL 33146, USA"},{"name":"Mecklenburg Neurology Group, 3541 Randolph Rd #301, Charlotte, NC 28211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0243-4211","authenticated-orcid":false,"given":"Jeremy","family":"Saintyl","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1600 NW 10th Ave, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jahnavi","family":"Yelamanchi","sequence":"additional","affiliation":[{"name":"Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Nahouraii","sequence":"additional","affiliation":[{"name":"Mecklenburg Neurology Group, 3541 Randolph Rd #301, Charlotte, NC 28211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram","family":"Jagadeesan","sequence":"additional","affiliation":[{"name":"Whiting School of Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9460-1269","authenticated-orcid":false,"given":"Alireza","family":"Tavakkoli","sequence":"additional","affiliation":[{"name":"Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, 1664 N Virginia St, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dugger, B.N., and Dickson, D.W. (2017). Pathology of neurodegenerative diseases. Cold Spring Harb. Perspect. Biol., 9.","DOI":"10.1101\/cshperspect.a028035"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1038\/s41582-019-0244-7","article-title":"Ageing as a risk factor for neurodegenerative disease","volume":"15","author":"Hou","year":"2019","journal-title":"Nat. Rev. Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Erkkinen, M.G., Kim, M.O., and Geschwind, M.D. (2018). Clinical neurology and epidemiology of the major neurodegenerative diseases. Cold Spring Harb. Perspect. Biol., 10.","DOI":"10.1101\/cshperspect.a033118"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.jalz.2011.03.003","article-title":"Toward defining the preclinical stages of Alzheimer\u2019s disease: Recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease","volume":"7","author":"Sperling","year":"2011","journal-title":"Alzheimers Dement."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1002\/mds.27670","article-title":"Prodromal Parkinson\u2019s disease: The decade past, the decade to come","volume":"34","author":"Postuma","year":"2019","journal-title":"Mov. Disord."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/nrneurol.2012.227","article-title":"The retina as a window to the brain-from eye research to CNS disorders","volume":"9","author":"London","year":"2013","journal-title":"Nat. Rev. Neurol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jalz.2014.04.514","article-title":"At the interface of sensory and motor dysfunctions and Alzheimer\u2019s disease","volume":"11","author":"Albers","year":"2015","journal-title":"Alzheimers Dement."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1016\/S0140-6736(20)32205-4","article-title":"Alzheimer\u2019s disease","volume":"397","author":"Scheltens","year":"2021","journal-title":"Lancet"},{"key":"ref_9","first-page":"15","article-title":"Improving the diagnosis of Parkinson\u2019s disease","volume":"259","author":"Malek","year":"2015","journal-title":"Practitioner"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1056\/NEJMoa1202753","article-title":"Clinical and biomarker changes in dominantly inherited Alzheimer\u2019s disease","volume":"367","author":"Bateman","year":"2012","journal-title":"N. Engl. J. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA Research Framework: Toward a biological definition of Alzheimer\u2019s disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimers Dement."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/s41582-018-0079-7","article-title":"Blood-based biomarkers for Alzheimer disease: Mapping the road to the clinic","volume":"14","author":"Hampel","year":"2018","journal-title":"Nat. Rev. Neurol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1002\/nbm.3272","article-title":"Clinical applications at ultrahigh field (7 T). Where does it make the difference?","volume":"29","author":"Trattnig","year":"2016","journal-title":"NMR Biomed."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.neuroimage.2011.10.065","article-title":"The future of ultra-high field MRI and fMRI for study of the human brain","volume":"62","author":"Duyn","year":"2012","journal-title":"Neuroimage"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wisse, L.E.M., Biessels, G.J., and Geerlings, M.I. (2014). A critical appraisal of the hippocampal subfield segmentation package in FreeSurfer. Front. Aging Neurosci., 6.","DOI":"10.3389\/fnagi.2014.00261"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1002\/mds.26043","article-title":"7 Tesla magnetic resonance imaging: A closer look at substantia nigra anatomy in Parkinson\u2019s disease","volume":"29","author":"Bardinet","year":"2014","journal-title":"Mov. Disord."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.nurt.2007.05.011","article-title":"Diffusion tensor imaging of the brain","volume":"4","author":"Alexander","year":"2007","journal-title":"Neurotherapeutics"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Acosta-Cabronero, J., and Nestor, P.J. (2014). Diffusion tensor imaging in Alzheimer\u2019s disease: Insights into the limbic-diencephalic network and methodological considerations. Front. Aging Neurosci., 6.","DOI":"10.3389\/fnagi.2014.00266"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.neuroimage.2017.03.057","article-title":"A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer\u2019s disease and its prodromal stages","volume":"155","author":"Rathore","year":"2017","journal-title":"Neuroimage"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","article-title":"An overview of deep learning in medical imaging focusing on MRI","volume":"29","author":"Lundervold","year":"2019","journal-title":"Z. Med. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","article-title":"Deep learning for brain MRI segmentation: State of the art and future directions","volume":"30","author":"Akkus","year":"2017","journal-title":"J. Digit Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Varoquaux, G., Schwartz, Y., Poldrack, R.A., Gauthier, B., Bzdok, D., Poline, J.B., and Thirion, B. (2018). Atlases of cognition with large-scale human brain mapping. PLoS Comput. Biol., 14.","DOI":"10.1371\/journal.pcbi.1006565"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.neuroimage.2011.01.008","article-title":"Multimodal classification of Alzheimer\u2019s disease and mild cognitive impairment","volume":"55","author":"Zhang","year":"2011","journal-title":"Neuroimage"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rieke, J., Eitel, F., Weygandt, M., Haynes, J.D., and Ritter, K. (2018). Visualizing convolutional networks for MRI-based diagnosis of Alzheimer\u2019s disease. Understanding and Interpreting Machine Learning in Medical Image Computing Applications, Springer.","DOI":"10.1007\/978-3-030-02628-8_3"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101694","DOI":"10.1016\/j.media.2020.101694","article-title":"Convolutional neural networks for classification of Alzheimer\u2019s disease: Overview and reproducible evaluation","volume":"63","author":"Wen","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jo, T., Nho, K., and Saykin, A.J. (2019). Deep learning in Alzheimer\u2019s disease: Diagnostic classification and prognostic prediction using neuroimaging data. Front. Aging Neurosci., 11.","DOI":"10.3389\/fnagi.2019.00220"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1038\/nrneurol.2018.9","article-title":"Imaging tau and amyloid-\u03b2 proteinopathies in Alzheimer disease and other conditions","volume":"14","author":"Villemagne","year":"2018","journal-title":"Nat. Rev. Neurol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1038\/s41380-018-0342-8","article-title":"Tau PET imaging in neurodegenerative tauopathies\u2014Still a challenge","volume":"24","author":"Leuzy","year":"2019","journal-title":"Mol. Psychiatry"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.neuroimage.2016.02.079","article-title":"Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls","volume":"145 Pt B","author":"Arbabshirani","year":"2017","journal-title":"Neuroimage"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1038\/nn.4478","article-title":"Building better biomarkers: Brain models in translational neuroimaging","volume":"20","author":"Woo","year":"2017","journal-title":"Nat. Neurosci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.neuroimage.2011.11.002","article-title":"Diagnostic neuroimaging across diseases","volume":"61","author":"Abdulkadir","year":"2012","journal-title":"Neuroimage"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1038\/nmeth.4642","article-title":"Statistics versus machine learning","volume":"15","author":"Bzdok","year":"2018","journal-title":"Nat. Methods"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1056\/NEJMp1714229","article-title":"Implementing machine learning in health care-addressing ethical challenges","volume":"378","author":"Char","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.neuroimage.2016.11.031","article-title":"Key clinical benefits of neuroimaging at 7T","volume":"168","author":"Trattnig","year":"2018","journal-title":"Neuroimage"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.neuroimage.2017.07.007","article-title":"Imaging at ultrahigh magnetic fields: History, challenges, and solutions","volume":"168","year":"2018","journal-title":"Neuroimage"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1002\/nbm.3275","article-title":"Recent applications of UHF-MRI in the study of human brain function and structure: A review","volume":"29","author":"Marques","year":"2016","journal-title":"NMR Biomed."},{"key":"ref_39","first-page":"538","article-title":"European Ultrahigh-Field Imaging Network for Neurodegenerative Diseases (EUFIND)","volume":"11","author":"Berron","year":"2019","journal-title":"Alzheimers Dement."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.neuroimage.2017.04.044","article-title":"How to choose the right MR sequence for your research question at 7T and above?","volume":"168","author":"Marques","year":"2018","journal-title":"Neuroimage"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1002\/hbm.25234","article-title":"Hippocampal subfield volumetry from structural isotropic 1 mm3 MRI scans: A note of caution","volume":"42","author":"Wisse","year":"2021","journal-title":"Hum. Brain Mapp."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1002\/hbm.24011","article-title":"The impact of B1+ correction on MP2RAGE cortical T1 and apparent cortical thickness at 7T","volume":"39","author":"Haast","year":"2018","journal-title":"Hum. Brain Mapp."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1002\/mds.23465","article-title":"Direct visualization of Parkinson\u2019s disease by in vivo human brain imaging using 7.0T magnetic resonance imaging","volume":"26","author":"Cho","year":"2011","journal-title":"Mov. Disord."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1136\/bjophthalmol-2017-311258","article-title":"Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe in 2015: Magnitude, temporal trends and projections","volume":"102","author":"Bourne","year":"2018","journal-title":"Br. J. Ophthalmol."},{"key":"ref_45","first-page":"162","article-title":"Retinal thickness in Alzheimer\u2019s disease: A systematic review and meta-analysis","volume":"6","author":"Verbraak","year":"2017","journal-title":"Alzheimers Dement."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1016\/j.neurobiolaging.2015.05.022","article-title":"Activated iron-containing microglia in the human hippocampus identified by magnetic resonance imaging in Alzheimer disease","volume":"36","author":"Zeineh","year":"2015","journal-title":"Neurobiol. Aging."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.preteyeres.2017.01.001","article-title":"Imaging retina to study dementia and stroke","volume":"57","author":"Cheung","year":"2017","journal-title":"Prog. Retin Eye Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.neuroimage.2017.02.035","article-title":"DeepNAT: Deep convolutional neural network for segmenting neuroanatomy","volume":"170","author":"Wachinger","year":"2018","journal-title":"Neuroimage"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/S0006-3495(94)80775-1","article-title":"MR diffusion tensor spectroscopy and imaging","volume":"66","author":"Basser","year":"1994","journal-title":"Biophys. J."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1002\/jmri.1076","article-title":"Diffusion tensor imaging: Concepts and applications","volume":"13","author":"Mangin","year":"2001","journal-title":"J. Magn. Reson Imaging"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s12031-007-0029-0","article-title":"Diffusion tensor imaging (DTI)-based white matter mapping in brain research: A review","volume":"34","author":"Assaf","year":"2008","journal-title":"J. Mol. Neurosci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.neuroimage.2012.06.081","article-title":"White matter integrity, fiber count, and other fallacies: The do\u2019s and don\u2019ts of diffusion MRI","volume":"73","author":"Jones","year":"2013","journal-title":"Neuroimage"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1016\/j.neuroimage.2012.03.072","article-title":"NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain","volume":"61","author":"Zhang","year":"2012","journal-title":"Neuroimage"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.neuroimage.2014.12.009","article-title":"One diffusion acquisition and different white matter models: How does microstructure predict fractional anisotropy?","volume":"107","author":"Jelescu","year":"2015","journal-title":"Neuroimage"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1002\/acn3.445","article-title":"Neurite dispersion: A new marker of multiple sclerosis spinal cord pathology?","volume":"4","author":"Grussu","year":"2017","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1007\/s00330-015-4066-8","article-title":"Neurite orientation dispersion and density imaging in the substantia nigra in idiopathic Parkinson disease","volume":"26","author":"Kamagata","year":"2016","journal-title":"Eur. Radiol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.parkreldis.2018.02.046","article-title":"Neurite orientation dispersion and density imaging of the nigrostriatal pathway in Parkinson\u2019s disease: Retrograde degeneration observed by tract-profile analysis","volume":"51","author":"Andica","year":"2018","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1002\/mrm.20508","article-title":"Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging","volume":"53","author":"Jensen","year":"2005","journal-title":"Magn. Reson. Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.neuroimage.2011.06.006","article-title":"White matter characterization with diffusional kurtosis imaging","volume":"58","author":"Fieremans","year":"2011","journal-title":"Neuroimage"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1002\/jmri.21604","article-title":"Age-related non-Gaussian diffusion patterns in the prefrontal brain","volume":"28","author":"Falangola","year":"2008","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1148\/radiol.11102277","article-title":"Parkinson disease: Diagnostic utility of diffusion kurtosis imaging","volume":"261","author":"Wang","year":"2011","journal-title":"Radiology"},{"key":"ref_64","first-page":"530","article-title":"White matter microstructure from nonparametric axon diameter distribution mapping","volume":"111","author":"Timmers","year":"2015","journal-title":"Neuroimage"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.nicl.2013.07.006","article-title":"Effectiveness of regional DTI measures in distinguishing Alzheimer\u2019s disease, MCI, and normal aging","volume":"3","author":"Nir","year":"2013","journal-title":"Neuroimage Clin."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1111\/jon.12214","article-title":"Predicting prodromal Alzheimer\u2019s disease in subjects with mild cognitive impairment using machine learning classification of multimodal multicenter diffusion-tensor and magnetic resonance imaging data","volume":"25","author":"Dyrba","year":"2015","journal-title":"J. Neuroimaging"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"S254","DOI":"10.1016\/j.jalz.2014.04.013","article-title":"In vivo tau imaging: Obstacles and progress","volume":"10","author":"Villemagne","year":"2014","journal-title":"Alzheimers Dement."},{"key":"ref_68","first-page":"221","article-title":"PET imaging of tau pathology in mild cognitive impairment and Alzheimer\u2019s disease with [18F]THK5351","volume":"57","author":"Okamura","year":"2016","journal-title":"J. Nucl. Med."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1007\/s00259-019-04397-2","article-title":"Discovery and preclinical characterization of [18F]PI-2620, a next-generation tau PET tracer for the assessment of tau pathology in Alzheimer\u2019s disease and other tauopathies","volume":"46","author":"Kroth","year":"2019","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"93","DOI":"10.2967\/jnumed.118.209650","article-title":"In vivo characterization and quantification of neurofibrillary tau PET radioligand 18F-MK-6240 in humans from Alzheimer disease dementia to young controls","volume":"60","author":"Betthauser","year":"2019","journal-title":"J. Nucl. Med."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"107","DOI":"10.2967\/jnumed.118.208215","article-title":"Brain imaging of Alzheimer dementia patients and elderly controls with 18F-MK-6240, a PET tracer targeting neurofibrillary tangles","volume":"60","author":"Lohith","year":"2019","journal-title":"J. Nucl. Med."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1001\/jama.2018.12917","article-title":"Discriminative accuracy of [18F]flortaucipir positron emission tomography for Alzheimer disease vs other neurodegenerative disorders","volume":"320","author":"Ossenkoppele","year":"2018","journal-title":"JAMA"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1001\/jamaneurol.2020.0528","article-title":"Positron emission tomography imaging with [18F]flortaucipir and postmortem assessment of Alzheimer disease neuropathologic changes","volume":"77","author":"Fleisher","year":"2020","journal-title":"JAMA Neurol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1001\/jamaneurol.2020.0989","article-title":"Diagnostic performance of RO948 F 18 tau positron emission tomography in the differentiation of Alzheimer disease from other neurodegenerative disorders","volume":"77","author":"Leuzy","year":"2020","journal-title":"JAMA Neurol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1093\/brain\/awy059","article-title":"Longitudinal tau PET in ageing and Alzheimer\u2019s disease","volume":"141","author":"Jack","year":"2018","journal-title":"Brain"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1093\/brain\/aww027","article-title":"Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer\u2019s disease","volume":"139","author":"Ossenkoppele","year":"2016","journal-title":"Brain"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1093\/brain\/awz090","article-title":"A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer\u2019s disease dementia","volume":"142","author":"Pontecorvo","year":"2019","journal-title":"Brain"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1002\/mds.27013","article-title":"Fluorescence and autoradiographic evaluation of tau PET ligand PBB3 to \u03b1-synuclein pathology","volume":"32","author":"Koga","year":"2017","journal-title":"Mov. Disord."},{"key":"ref_79","first-page":"3116","article-title":"[11C]MODAG-001\u2014Towards a PET tracer targeting \u03b1-synuclein aggregates","volume":"47","author":"Kuebler","year":"2020","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.pneurobio.2011.09.005","article-title":"The Parkinson Progression Marker Initiative (PPMI)","volume":"95","author":"Marek","year":"2011","journal-title":"Prog. Neurobiol."},{"key":"ref_81","first-page":"158","article-title":"Translational study of a novel alpha-synuclein PET tracer designed for first-in-human investigating","volume":"92","author":"Shi","year":"2021","journal-title":"Nucl. Med. Biol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3153","DOI":"10.1021\/acs.molpharmaceut.8b00229","article-title":"In silico, in vitro, and in vivo evaluation of new candidates for \u03b1-synuclein PET imaging","volume":"15","author":"Verdurand","year":"2018","journal-title":"Mol. Pharm."},{"key":"ref_83","first-page":"1207","article-title":"Focused review: Recent developments in the search for alpha-synuclein PET radiotracers","volume":"62","author":"Mach","year":"2021","journal-title":"J. Nucl. Med."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"6002","DOI":"10.1021\/acs.jmedchem.5b00571","article-title":"Design, synthesis, and characterization of 3-(benzylidene)indolin-2-one derivatives as ligands for \u03b1-synuclein fibrils","volume":"58","author":"Chu","year":"2015","journal-title":"J. Med. Chem."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s40336-016-0217-4","article-title":"Current status of the development of PET radiotracers for imaging alpha synuclein aggregates in Lewy bodies and Lewy neurites","volume":"5","author":"Kotzbauer","year":"2017","journal-title":"Clin. Transl. Imaging"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1148\/radiol.2018180958","article-title":"A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain","volume":"290","author":"Ding","year":"2019","journal-title":"Radiology"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s12021-018-9370-4","article-title":"Multi-modality cascaded convolutional neural networks for Alzheimer\u2019s disease diagnosis","volume":"16","author":"Liu","year":"2018","journal-title":"Neuroinformatics"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"3769","DOI":"10.1002\/hbm.24210","article-title":"Adaptive template generation for amyloid PET using a deep learning approach","volume":"39","author":"Kang","year":"2018","journal-title":"Hum. Brain Mapp."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1016\/j.nicl.2016.05.004","article-title":"Classification of amyloid status using machine learning with histograms of oriented 3D gradients","volume":"12","author":"Cattell","year":"2016","journal-title":"Neuroimage Clin."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Lu, D., Popuri, K., Ding, G.W., Balachandar, R., Beg, M.F., and Alzheimer\u2019s Disease Neuroimaging Initiative (2018). Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer\u2019s disease using structural MR and FDG-PET images. Sci. Rep., 8.","DOI":"10.1016\/j.media.2018.02.002"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.bbr.2018.02.017","article-title":"Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging","volume":"344","author":"Choi","year":"2018","journal-title":"Behav. Brain Res."},{"key":"ref_92","unstructured":"Marinescu, R.V., Oxtoby, N.P., Young, A.L., Bron, E.E., Toga, A.W., Weiner, M.W., Barkhof, F., Fox, N.C., Klein, S., and Alexander, D.C. (2018). TADPOLE Challenge: Prediction of longitudinal evolution in Alzheimer\u2019s disease. arXiv."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Bhagwat, N., Viviano, J.D., Voineskos, A.N., Chakravarty, M.M., and Alzheimer\u2019s Disease Neuroimaging Initiative (2018). Modeling and prediction of clinical symptom trajectories in Alzheimer\u2019s disease using longitudinal data. PLoS Comput. Biol., 14.","DOI":"10.1371\/journal.pcbi.1006376"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.neuroimage.2019.01.031","article-title":"A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer\u2019s disease","volume":"189","author":"Spasov","year":"2019","journal-title":"Neuroimage"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","article-title":"Hierarchical feature representation and multimodal fusion with deep learning for AD\/MCI diagnosis","volume":"101","author":"Suk","year":"2014","journal-title":"Neuroimage"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1016\/j.biopsych.2020.02.016","article-title":"Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: Methods and promises","volume":"88","author":"Sui","year":"2020","journal-title":"Biol. Psychiatry"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Plis, S.M., Sarwate, A.D., Wood, D., Dieringer, C., Landis, D., Reed, C., Panta, S.R., Turner, J.A., Shoemaker, J.M., and Carter, K.W. (2016). COINSTAC: A privacy enabled model and prototype for leveraging and processing decentralized brain imaging data. Front. Neurosci., 10.","DOI":"10.3389\/fnins.2016.00365"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neubiorev.2017.01.002","article-title":"Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications","volume":"74 Pt A","author":"Vieira","year":"2017","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_99","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in vision: A survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_101","first-page":"e628","article-title":"Is it possible to perform \u201cfree-viewing\u201d eye-tracking experiments on a smartphone?","volume":"7","author":"Matsoukas","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","article-title":"Handcrafted vs. non-handcrafted features for computer vision classification","volume":"71","author":"Nanni","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","article-title":"Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer\u2019s disease","volume":"48","author":"Parisot","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"102233","DOI":"10.1016\/j.media.2021.102233","article-title":"BrainGNN: Interpretable brain graph neural network for fMRI analysis","volume":"74","author":"Li","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.neuroimage.2017.12.052","article-title":"Metric learning with spectral graph convolutions on brain connectivity networks","volume":"169","author":"Ktena","year":"2018","journal-title":"Neuroimage"},{"key":"ref_106","first-page":"102308","article-title":"Fusing structural and functional MRIs using graph convolutional networks for autism classification","volume":"75","author":"Arya","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1109\/TBME.2017.2715281","article-title":"Automatic recognition of fMRI-derived functional networks using 3D convolutional neural networks","volume":"65","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1111\/j.1749-6632.2012.06650.x","article-title":"What brain signals are suitable for feedback control of deep brain stimulation in Parkinson\u2019s disease?","volume":"1265","author":"Little","year":"2012","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1002\/mds.27022","article-title":"Adaptive deep brain stimulation for movement disorders: The long road to clinical therapy","volume":"32","author":"Meidahl","year":"2017","journal-title":"Mov. Disord."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/s12984-017-0295-1","article-title":"Advances in closed-loop deep brain stimulation devices","volume":"14","author":"Parastarfeizabadi","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"046006","DOI":"10.1088\/1741-2552\/aabc9b","article-title":"Adaptive deep brain stimulation for Parkinson\u2019s disease using motor cortex sensing","volume":"15","author":"Swann","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"e971","DOI":"10.1212\/WNL.0000000000005121","article-title":"Eight-hours adaptive deep brain stimulation in patients with Parkinson disease","volume":"90","author":"Arlotti","year":"2018","journal-title":"Neurology"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1093\/brain\/awx010","article-title":"The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson\u2019s disease","volume":"140","author":"Tinkhauser","year":"2017","journal-title":"Brain"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1016\/j.brs.2019.02.020","article-title":"Dual threshold neural closed loop deep brain stimulation in Parkinson disease patients","volume":"12","author":"Velisar","year":"2019","journal-title":"Brain Stimul."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1016\/j.brs.2020.06.018","article-title":"Neural closed-loop deep brain stimulation for freezing of gait","volume":"13","author":"Petrucci","year":"2020","journal-title":"Brain Stimul."},{"key":"ref_116","first-page":"610","article-title":"Vascular retinal biomarkers improves the detection of the likely cerebral amyloid status from hyperspectral retinal images","volume":"5","author":"Sharafi","year":"2019","journal-title":"Alzheimers Dement."},{"key":"ref_117","first-page":"990","article-title":"Convolutional neural network to identify symptomatic Alzheimer\u2019s disease using multimodal retinal imaging","volume":"105","author":"Wisely","year":"2021","journal-title":"Br. J. Ophthalmol."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"e93621","DOI":"10.1172\/jci.insight.93621","article-title":"Retinal amyloid pathology and proof-of-concept imaging trial in Alzheimer\u2019s disease","volume":"2","author":"Koronyo","year":"2017","journal-title":"JCI Insight"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"642208","DOI":"10.3389\/fnins.2021.731614","article-title":"The utility of retinal imaging in Alzheimer\u2019s disease","volume":"15","author":"Shi","year":"2021","journal-title":"Front Neurosci."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1111\/aos.13472","article-title":"Can the retina be used to diagnose and plot the progression of Alzheimer\u2019s disease?","volume":"95","author":"Mahajan","year":"2017","journal-title":"Acta Ophthalmol."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","article-title":"The future of digital health with federated learning","volume":"3","author":"Rieke","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","article-title":"Secure, privacy-preserving and federated machine learning in medical imaging","volume":"2","author":"Kaissis","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_123","first-page":"50","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process Mag."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Sheller, M.J., Edwards, B., Reina, G.A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., and Colen, R.R. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-69250-1"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41666-020-00082-4","article-title":"Federated learning for healthcare informatics","volume":"5","author":"Xu","year":"2021","journal-title":"J. Healthc. Inform. Res."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","article-title":"Federated learning for predicting clinical outcomes in patients with COVID-19","volume":"27","author":"Dayan","year":"2021","journal-title":"Nat. Med."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1093\/jamia\/ocaa341","article-title":"Federated learning improves site performance in multicenter deep learning without data sharing","volume":"28","author":"Sarma","year":"2021","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1038\/nature25456","article-title":"High performance plasma amyloid-\u03b2 biomarkers for Alzheimer\u2019s disease","volume":"554","author":"Nakamura","year":"2018","journal-title":"Nature"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1038\/s41591-020-0755-1","article-title":"Plasma P-tau181 in Alzheimer\u2019s disease: Relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer\u2019s dementia","volume":"26","author":"Janelidze","year":"2020","journal-title":"Nat. Med."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1001\/jamaneurol.2019.1632","article-title":"Performance of Fully Automated Plasma Assays as Screening Tests for Alzheimer Disease-Related \u03b2-Amyloid Status","volume":"76","author":"Palmqvist","year":"2019","journal-title":"JAMA Neurol."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1038\/s41591-020-0762-2","article-title":"Diagnostic value of plasma phosphorylated tau181 in Alzheimer\u2019s disease and frontotemporal lobar degeneration","volume":"26","author":"Thijssen","year":"2020","journal-title":"Nat. Med."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"e1647","DOI":"10.1212\/WNL.0000000000008081","article-title":"High-precision plasma \u03b2-amyloid 42\/40 predicts current and future brain amyloidosis","volume":"93","author":"Schindler","year":"2019","journal-title":"Neurology"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/S1474-4422(20)30071-5","article-title":"Blood phosphorylated tau 181 as a biomarker for Alzheimer\u2019s disease: A diagnostic performance and prediction modelling study using data from four prospective cohorts","volume":"19","author":"Karikari","year":"2020","journal-title":"Lancet Neurol."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1038\/s41591-018-0304-3","article-title":"Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer\u2019s disease","volume":"25","author":"Preische","year":"2019","journal-title":"Nat. Med."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1001\/jamaneurol.2019.0765","article-title":"Association Between Longitudinal Plasma Neurofilament Light and Neurodegeneration in Patients With Alzheimer Disease","volume":"76","author":"Mattsson","year":"2019","journal-title":"JAMA Neurol."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1016\/j.neuron.2012.09.004","article-title":"Neuronal dynamics and neuropsychiatric disorders: Toward a translational paradigm for dysfunctional large-scale networks","volume":"75","author":"Uhlhaas","year":"2012","journal-title":"Neuron"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1038\/nrn.2016.164","article-title":"Closed-loop brain training: The science of neurofeedback","volume":"18","author":"Sitaram","year":"2017","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.neurobiolaging.2017.03.030","article-title":"Abnormalities of cortical neural synchronization mechanisms in patients with dementia due to Alzheimer\u2019s and Lewy body diseases: An EEG study","volume":"55","author":"Babiloni","year":"2017","journal-title":"Neurobiol. Aging"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Mandal, P.K., Banerjee, A., Tripathi, M., and Sharma, A. (2018). A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer\u2019s Disease (AD). Front. Comput. Neurosci., 12.","DOI":"10.3389\/fncom.2018.00060"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.neurobiolaging.2020.03.009","article-title":"Tau pathology in early Alzheimer\u2019s disease is linked to selective disruptions in neurophysiological network dynamics","volume":"92","author":"Kocagoncu","year":"2020","journal-title":"Neurobiol. Aging"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"10325","DOI":"10.1523\/JNEUROSCI.0704-15.2015","article-title":"Network Disruption and Cerebrospinal Fluid Amyloid-Beta and Phospho-Tau Levels in Mild Cognitive Impairment","volume":"35","author":"Canuet","year":"2015","journal-title":"J. Neurosci."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1038\/nrneurol.2017.34","article-title":"Restoring brain function after stroke-bridging the gap between animals and humans","volume":"13","author":"Ward","year":"2017","journal-title":"Nat. Rev. Neurol."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1093\/brain\/awr039","article-title":"Harnessing neuroplasticity for clinical applications","volume":"134","author":"Cramer","year":"2011","journal-title":"Brain"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1093\/brain\/awf282","article-title":"Correlation between motor improvements and altered fMRI activity after rehabilitative therapy","volume":"125","author":"Dawes","year":"2002","journal-title":"Brain"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1177\/1545968311418345","article-title":"Acquired control of ventral premotor cortex activity by feedback training: An exploratory real-time FMRI and TMS study","volume":"26","author":"Sitaram","year":"2012","journal-title":"Neurorehabil. Neural Repair."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1002\/ana.23879","article-title":"Brain-machine interface in chronic stroke rehabilitation: A controlled study","volume":"74","author":"Broetz","year":"2013","journal-title":"Ann. Neurol."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1002\/hbm.22761","article-title":"Exploring the brain\u2019s structural connectome: A quantitative stroke lesion-dysfunction mapping study","volume":"36","author":"Kuceyeski","year":"2015","journal-title":"Hum. Brain Mapp."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1002\/hbm.23110","article-title":"Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis","volume":"37","author":"Pustina","year":"2016","journal-title":"Hum. Brain Mapp."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.clinph.2019.11.002","article-title":"Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014-2018)","volume":"131","author":"Lefaucheur","year":"2020","journal-title":"Clin. Neurophysiol."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.clinph.2016.10.087","article-title":"Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS)","volume":"128","author":"Lefaucheur","year":"2017","journal-title":"Clin. Neurophysiol."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1016\/j.clinph.2017.09.007","article-title":"Plasticity induced by non-invasive transcranial brain stimulation: A position paper","volume":"128","author":"Huang","year":"2017","journal-title":"Clin. Neurophysiol."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.neuroimage.2016.02.012","article-title":"Combining non-invasive transcranial brain stimulation with neuroimaging and electrophysiology: Current approaches and future perspectives","volume":"140","author":"Bergmann","year":"2016","journal-title":"Neuroimage"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/j.clinph.2017.01.003","article-title":"Guiding transcranial brain stimulation by EEG\/MEG to interact with ongoing brain activity and associated functions: A position paper","volume":"128","author":"Thut","year":"2017","journal-title":"Clin. Neurophysiol."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41591-018-0307-0","article-title":"The practical implementation of artificial intelligence technologies in medicine","volume":"25","author":"He","year":"2019","journal-title":"Nat. Med."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Vayena, E., Blasimme, A., and Cohen, I.G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002689"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., and Oermann, E.K. (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002683"},{"key":"ref_159","unstructured":"U.S. Food and Drug Administration (2017). Software as a Medical Device (SaMD), U.S. Food and Drug Administration."},{"key":"ref_160","unstructured":"U.S. Food and Drug Administration (2021). Artificial Intelligence and Machine Learning in Software as a Medical Device, U.S. Food and Drug Administration."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1126\/science.aay9547","article-title":"Algorithms on regulatory lockdown in medicine","volume":"366","author":"Babic","year":"2019","journal-title":"Science"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1056\/NEJMsb1609216","article-title":"Real-World Evidence-What Is It and What Can It Tell Us?","volume":"375","author":"Sherman","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"ref_163","first-page":"10","article-title":"Artificial Intelligence in Health Care: Applications and Legal Implications","volume":"14","author":"Price","year":"2017","journal-title":"SciTech Lawyer"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Gerke, S., Minssen, T., and Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, Academic Press.","DOI":"10.2139\/ssrn.3570129"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"e376","DOI":"10.1016\/S2589-7500(20)30112-6","article-title":"The European artificial intelligence strategy: Implications and challenges for digital health","volume":"2","author":"Cohen","year":"2020","journal-title":"Lancet Digit Health"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/s41591-018-0272-7","article-title":"Privacy in the age of medical big data","volume":"25","author":"Price","year":"2019","journal-title":"Nat. Med."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1038\/s41746-019-0158-1","article-title":"Why digital medicine depends on interoperability","volume":"2","author":"Lehne","year":"2019","journal-title":"NPJ Digit. Med."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"160018","DOI":"10.1038\/sdata.2016.18","article-title":"The FAIR Guiding Principles for scientific data management and stewardship","volume":"3","author":"Wilkinson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1001\/jama.2019.15064","article-title":"Potential Liability for Physicians Using Artificial Intelligence","volume":"322","author":"Price","year":"2019","journal-title":"JAMA"},{"key":"ref_170","unstructured":"U.S. Food and Drug Administration (2016). Postmarket Management of Cybersecurity in Medical Devices, U.S. Food and Drug Administration."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1136\/bmjqs-2018-008370","article-title":"Artificial intelligence, bias and clinical safety","volume":"28","author":"Challen","year":"2019","journal-title":"BMJ Qual. Saf."},{"key":"ref_172","unstructured":"U.S. Food and Drug Administration (2020). Medical Device Reporting (MDR), U.S. Food and Drug Administration."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.neuroimage.2005.09.046","article-title":"Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data","volume":"30","author":"Jovicich","year":"2006","journal-title":"Neuroimage"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.neuroimage.2017.03.055","article-title":"Mindcontrol: A web application for brain segmentation quality control","volume":"170","author":"Keshavan","year":"2018","journal-title":"Neuroimage"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1016\/j.neuroimage.2010.07.020","article-title":"Highly accurate inverse consistent registration: A robust approach","volume":"53","author":"Reuter","year":"2010","journal-title":"Neuroimage"},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Esteban, O., Birman, D., Schaer, M., Koyejo, O.O., Poldrack, R.A., and Gorgolewski, K.J. (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS ONE, 12.","DOI":"10.1101\/111294"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1016\/j.neuroimage.2011.10.018","article-title":"Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion","volume":"59","author":"Power","year":"2012","journal-title":"Neuroimage"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neuroimage.2015.11.054","article-title":"Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion","volume":"127","author":"Tisdall","year":"2016","journal-title":"Neuroimage"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41592-018-0235-4","article-title":"fMRIPrep: A robust preprocessing pipeline for functional MRI","volume":"16","author":"Esteban","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"160044","DOI":"10.1038\/sdata.2016.44","article-title":"The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments","volume":"3","author":"Gorgolewski","year":"2016","journal-title":"Sci. Data"},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1016\/j.neuroimage.2012.02.084","article-title":"Within-subject template estimation for unbiased longitudinal image analysis","volume":"61","author":"Reuter","year":"2012","journal-title":"Neuroimage"},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","article-title":"Harmonization of cortical thickness measurements across scanners and sites","volume":"167","author":"Fortin","year":"2018","journal-title":"Neuroimage"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"4213","DOI":"10.1002\/hbm.24241","article-title":"Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data","volume":"39","author":"Yu","year":"2018","journal-title":"Hum. Brain Mapp."},{"key":"ref_184","unstructured":"U.S. Food and Drug Administration (2021). Artificial Intelligence\/Machine Learning (AI\/ML)-Based Software as a Medical Device (SaMD) Action Plan, U.S. Food and Drug Administration."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1038\/s41746-020-00324-0","article-title":"The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database","volume":"3","author":"Benjamens","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_186","unstructured":"U.S. Food and Drug Administration (2019). Proposed Regulatory Framework for Modifications to Artificial Intelligence\/Machine Learning (AI\/ML)-Based Software as a Medical Device (SaMD), U.S. Food and Drug Administration."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1038\/s41551-019-0466-4","article-title":"Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning","volume":"3","author":"Shen","year":"2019","journal-title":"Nat. Biomed. Eng."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","article-title":"A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction","volume":"37","author":"Schlemper","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"2998","DOI":"10.1007\/s00259-020-04897-6","article-title":"Generalization of deep learning models for ultra-low-count amyloid PET\/MRI using transfer learning","volume":"47","author":"Chen","year":"2020","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1126\/science.aaw4399","article-title":"Adversarial attacks on medical machine learning","volume":"363","author":"Finlayson","year":"2019","journal-title":"Science"},{"key":"ref_192","unstructured":"Glocker, B., Robinson, R., Castro, D.C., Dou, Q., and Konukoglu, E. (2019). Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects. arXiv."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.neuroimage.2016.05.053","article-title":"Domain adaptation for Alzheimer\u2019s disease diagnostics","volume":"139","author":"Wachinger","year":"2016","journal-title":"Neuroimage"},{"key":"ref_194","first-page":"4765","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"ref_195","first-page":"1","article-title":"Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks","volume":"Volume 11037","author":"Shin","year":"2018","journal-title":"Simulation and Synthesis in Medical Imaging\u2014Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings"}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/14\/12\/1266\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:53:36Z","timestamp":1760115216000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/14\/12\/1266"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,17]]},"references-count":195,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["brainsci14121266"],"URL":"https:\/\/doi.org\/10.3390\/brainsci14121266","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,17]]}}}