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Noroozi M, Gholami M, Sadeghsalehi H, Behzadi S, Habibzadeh A, Erabi G, Sadatmadani SF, Diyanati M, Rezaee A, Dianati M, Rasoulian P, Khani Siyah Rood Y, Ilati F, Hadavi SM, Arbab Mojeni F, Roostaie M, Deravi N. Machine and deep learning algorithms for classifying different types of dementia: A literature review. APPLIED NEUROPSYCHOLOGY. ADULT 2024:1-15. [PMID: 39087520 DOI: 10.1080/23279095.2024.2382823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.
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Affiliation(s)
- Masoud Noroozi
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Mohammadreza Gholami
- Department of Electrical and Computer Engineering, Tarbiat Modares Univeristy, Tehran, Iran
| | - Hamidreza Sadeghsalehi
- Department of Artificial Intelligence in Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Saleh Behzadi
- Student Research Committee, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Adrina Habibzadeh
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
- USERN Office, Fasa University of Medical Sciences, Fasa, Iran
| | - Gisou Erabi
- Student Research Committee, Urmia University of Medical Sciences, Urmia, Iran
| | | | - Mitra Diyanati
- Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Aryan Rezaee
- Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Maryam Dianati
- Student Research Committee, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Pegah Rasoulian
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Yashar Khani Siyah Rood
- Faculty of Engineering, Computer Engineering, Islamic Azad University of Bandar Abbas, Bandar Abbas, Iran
| | - Fatemeh Ilati
- Student Research Committee, Faculty of Medicine, Islamic Azad University of Mashhad, Mashhad, Iran
| | | | - Fariba Arbab Mojeni
- Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Minoo Roostaie
- School of Medicine, Islamic Azad University Tehran Medical Branch, Tehran, Iran
| | - Niloofar Deravi
- Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Das SR, Ilesanmi A, Wolk DA, Gee JC. Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ? Magn Reson Med Sci 2024; 23:367-376. [PMID: 38880615 PMCID: PMC11234947 DOI: 10.2463/mrms.rev.2024-0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques-for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer's disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.
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Affiliation(s)
- Sandhitsu R. Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ademola Ilesanmi
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - James C. Gee
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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Lust B, Flynn S, Henderson C, Gair J, Sherman JC. Disintegration at the Syntax-Semantics Interface in Prodromal Alzheimer's Disease: New Evidence from Complex Sentence Anaphora in Amnestic Mild Cognitive Impairment (aMCI). JOURNAL OF NEUROLINGUISTICS 2024; 70:101190. [PMID: 38370310 PMCID: PMC10871704 DOI: 10.1016/j.jneuroling.2023.101190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Although diverse language deficits have been widely observed in prodromal Alzheimer's disease (AD), the underlying nature of such deficits and their explanation remains opaque. Consequently, both clinical applications and brain-language models are not well-defined. In this paper we report results from two experiments which test language production in a group of individuals with amnestic Mild Cognitive Impairment (aMCI) in contrast to healthy aging and healthy young. The experiments apply factorial designs informed by linguistic analysis to test two forms of complex sentences involving anaphora (relations between pronouns and their antecedents). Results show that aMCI individuals differentiate forms of anaphora depending on sentence structure, with selective impairment of sentences which involve construal with reference to context (anaphoric coreference). We argue that aMCI individuals maintain core structural knowledge while evidencing deficiency in syntax-semantics integration, thus locating the source of the deficit in the language-thought interface of the Language Faculty.
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Affiliation(s)
- Barbara Lust
- Cognitive Science, Psychology, Cornell University
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Zhao F, Yang H, Gao Z, Liu H, Wu P, Li B, Yu H, Shao J. Novel fabrication of Cu(II)-incorporated chiral d-penicillamine-chitosan nanocomposites enantio-selectively inhibit the induced amyloid β aggregation for Alzheimer's disease therapy. Heliyon 2024; 10:e23563. [PMID: 38223723 PMCID: PMC10784170 DOI: 10.1016/j.heliyon.2023.e23563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 01/16/2024] Open
Abstract
It is well known that the chiral materials combined with metal ion's structure have been identified as promising candidate for the nursing Alzheimer Disease (AD) treatment, particularly to inhibit amyloid (Aβ) due to their significant pharmacological effect on the living bodies. In the present study, Cu(II)/Chitosan nanocomposite caped with chiral penicillamine (Cu@D-PEN/Chitosan) have been synthesized and used as an effective amyloid-β (Aβ) inhibitor. The composite formations of the samples were confirmed from the FTIR and XRD, studies. FE-SEM, TEM and AFM studies have been carried out to depict the morphological analysis of the nanocomposites. The prepared samples have also been subjected to various in vitro studies such as encapsulation efficiency, drug loading capacity, drug release and biodegrading or compatibility of the nanocomposites to support the Aβ aggregation inhibiting ability investigations. It was observed that the increase in the concentration of the Cu@D-PEN/Chitosan enhancing the Aβ inhibiting ability. Thus, the Cu(II)@D-PEN/Chitosan showed improving memory effect suggesting that Cu(II)@D-PEN/Chitosan nanocomposites may be a potential candidate for inhibiting the Aβ aggregation in nursing AD treatment.
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Affiliation(s)
- Feng Zhao
- Department of Neurology, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210012, China
| | - Hui Yang
- School of Nursing, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Zehong Gao
- Department of Neurology, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210012, China
| | - Huamei Liu
- School of Nursing, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Pingling Wu
- School of Nursing, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Binbin Li
- School of Nursing, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Heming Yu
- Department of Neurology, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210012, China
| | - Jiahui Shao
- Department of Neurology, Wenling First People's Hospital Affiliated to Wenzhou Medical University, Wenling 317500, China
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Xu X, Jang I, Zhang J, Zhang M, Wang L, Ye G, Zhao A, Zhang Y, Li B, Liu J, Li B. Cortical gray to white matter signal intensity ratio as a sign of neurodegeneration and cognition independent of β-amyloid in dementia. Hum Brain Mapp 2024; 45:e26532. [PMID: 38013633 PMCID: PMC10789219 DOI: 10.1002/hbm.26532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 09/28/2023] [Accepted: 10/19/2023] [Indexed: 11/29/2023] Open
Abstract
Cortical gray to white matter signal intensity ratio (GWR) measured from T1-weighted magnetic resonance (MR) images was associated with neurodegeneration and dementia. We characterized topological patterns of GWR during AD pathogenesis and investigated its association with cognitive decline. The study included a cross-sectional dataset and a longitudinal dataset. The cross-sectional dataset included 60 cognitively healthy controls, 61 mild cognitive impairment (MCI), and 63 patients with dementia. The longitudinal dataset included 26 participants who progressed from cognitively normal to dementia and 26 controls that remained cognitively normal. GWR was compared across the cross-sectional groups, adjusted for amyloid PET. The correlation between GWR and cognition performance was also evaluated. The longitudinal dataset was used to investigate GWR alteration during the AD pathogenesis. Dementia with β-amyloid deposition group exhibited the largest area of increased GWR, followed by MCI with β-amyloid deposition, MCI without β-amyloid deposition, and controls. The spatial pattern of GWR-increased regions was not influenced by β-amyloid deposits. Correlation between regional GWR alteration and cognitive decline was only detected among individuals with β-amyloid deposition. GWR showed positive correlation with tau PET in the left supramarginal, lateral occipital gyrus, and right middle frontal cortex. The longitudinal study showed that GWR increased around the fusiform, inferior/superior temporal lobe, and entorhinal cortex in MCI and progressed to larger cortical regions after progression to AD. The spatial pattern of GWR-increased regions was independent of β-amyloid deposits but overlapped with tauopathy. The GWR can serve as a promising biomarker of neurodegeneration in AD.
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Affiliation(s)
- Xiaomeng Xu
- Department of Neurology and Institute of NeurologyRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Ikbeom Jang
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- Division of Computer EngineeringHankuk University of Foreign StudiesYonginSouth Korea
| | - Junfang Zhang
- Department of Neurology and Institute of NeurologyRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Miao Zhang
- Department of Nuclear MedicineRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Lijun Wang
- Department of Neurology and Institute of NeurologyRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Guanyu Ye
- Department of Neurology and Institute of NeurologyRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Aonan Zhao
- Department of Neurology and Institute of NeurologyRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Yichi Zhang
- Department of Neurology and Institute of NeurologyRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Biao Li
- Department of Nuclear MedicineRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Jun Liu
- Department of Neurology and Institute of NeurologyRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
- Clinical Neuroscience CenterRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Binyin Li
- Department of Neurology and Institute of NeurologyRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
- Clinical Neuroscience CenterRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
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Ho NH, Jeong YH, Kim J. Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay. Sci Rep 2023; 13:11243. [PMID: 37433809 PMCID: PMC10336016 DOI: 10.1038/s41598-023-37500-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 06/22/2023] [Indexed: 07/13/2023] Open
Abstract
Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer's disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ([Formula: see text]). Moreover, our performance was equivalent to that of contemporary research.
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Affiliation(s)
- Ngoc-Huynh Ho
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea
| | - Yang-Hyung Jeong
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea.
| | - Jahae Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea
- Department of Nuclear Medicine, Chonnam National University Hospital, Gwangju, 61469, South Korea
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Liu F, Wang H, Liang SN, Jin Z, Wei S, Li X. MPS-FFA: A multiplane and multiscale feature fusion attention network for Alzheimer's disease prediction with structural MRI. Comput Biol Med 2023; 157:106790. [PMID: 36958239 DOI: 10.1016/j.compbiomed.2023.106790] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/13/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023]
Abstract
Structural magnetic resonance imaging (sMRI) is a popular technique that is widely applied in Alzheimer's disease (AD) diagnosis. However, only a few structural atrophy areas in sMRI scans are highly associated with AD. The degree of atrophy in patients' brain tissues and the distribution of lesion areas differ among patients. Therefore, a key challenge in sMRI-based AD diagnosis is identifying discriminating atrophy features. Hence, we propose a multiplane and multiscale feature-level fusion attention (MPS-FFA) model. The model has three components, (1) A feature encoder uses a multiscale feature extractor with hybrid attention layers to simultaneously capture and fuse multiple pathological features in the sagittal, coronal, and axial planes. (2) A global attention classifier combines clinical scores and two global attention layers to evaluate the feature impact scores and balance the relative contributions of different feature blocks. (3) A feature similarity discriminator minimizes the feature similarities among heterogeneous labels to enhance the ability of the network to discriminate atrophy features. The MPS-FFA model provides improved interpretability for identifying discriminating features using feature visualization. The experimental results on the baseline sMRI scans from two databases confirm the effectiveness (e.g., accuracy and generalizability) of our method in locating pathological locations. The source code is available at https://github.com/LiuFei-AHU/MPSFFA.
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Affiliation(s)
- Fei Liu
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China
| | - Huabin Wang
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China.
| | - Shiuan-Ni Liang
- School of Engineering, Monash University Malaysia, Kuala Lumpur, Malaysia
| | - Zhe Jin
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China
| | - Shicheng Wei
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China
| | - Xuejun Li
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China
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