A review on Alzheimer’s disease classification from normal controls and mild cognitive impairment using structural MR images

Garg, Neha; Choudhry, Mahipal Singh; Bodade, Rajesh M.

doi:10.1016/j.jneumeth.2022.109745

Cited by 21 publications

(3 citation statements)

References 224 publications

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“…AD is currently defined as dementia associated with Alzheimer‐type neuropathological change. However, the identification of ADNC in cognitively intact individuals implies pre‐clinical disease, the importance of which has been elevated by the ability to detect ADNC in‐life through imaging 124,125 and CSF biomarkers, 126 and by the therapeutic drive to treat early despite the only study with reduction in cognitive decline observed with medication was at a level that had a priori been deemed as not clinically significant 127,128 . Indeed, a biological definition of AD for research purposes, the ATN system based on Aβ, tau and neurodegenerative biomarkers, has been proposed by the National Institute on Ageing and Alzheimer's Association (NIA‐AA), which defines AD by the presence of pathological markers, 129 and which has provoked debate into its use in practice 130 .…”

Section: Significance Of High Pathology Prevalence and Lack Of Thresh...mentioning

confidence: 99%

Insights into the pathological basis of dementia from population‐based neuropathology studies

Wharton

Simpson

Ince

et al. 2023

Neuropathology Appl Neurobio

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The epidemiological neuropathology perspective of population and community‐based studies allows unbiased assessment of the prevalence of various pathologies and their relationships to late‐life dementia. In addition, this approach provides complementary insights to conventional case–control studies, which tend to be more representative of a younger clinical cohort. The Cognitive Function and Ageing Study (CFAS) is a longitudinal study of cognitive impairment and frailty in the general United Kingdom population. In this review, we provide an overview of the major findings from CFAS, alongside other studies, which have demonstrated a high prevalence of pathology in the ageing brain, particularly Alzheimer's disease neuropathological change and vascular pathology. Increasing burdens of these pathologies are the major correlates of dementia, especially neurofibrillary tangles, but there is substantial overlap in pathology between those with and without dementia, particularly at intermediate burdens of pathology and also at the oldest ages. Furthermore, additional pathologies such as limbic‐predominant age‐related TDP‐43 encephalopathy, ageing‐related tau astrogliopathy and primary age‐related tauopathies contribute to late‐life dementia. Findings from ageing population‐representative studies have implications for the understanding of dementia pathology in the community. The high prevalence of pathology and variable relationship to dementia status has implications for disease definition and indicate a role for modulating factors on cognitive outcome. The complexity of late‐life dementia, with mixed pathologies, indicates a need for a better understanding of these processes across the life‐course to direct the best research for reducing risk in later life of avoidable clinical dementia syndromes.

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Section: Significance Of High Pathology Prevalence and Lack Of Thresh...mentioning

confidence: 99%

Insights into the pathological basis of dementia from population‐based neuropathology studies

Wharton

Simpson

Ince

et al. 2023

Neuropathology Appl Neurobio

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“…While concussion history appears to be related to these measures of brain structure and function, it is currently unclear whether concussion history results in morphologic changes in gray matter. Gray matter morphologic measures change throughout development (20,21) and in neurodegenerative disorders like chronic traumatic encephalopathy (22), Alzheimer's disease (23) and Parkinson's disease (24). If cortical morphology is influenced by neurodegenerative disorders, the possible link between concussion history and these types of disorders later in life (25) may indicate that concussion history results in morphological changes in the brain and could serve as an objective, quantitative marker for cumulative concussion history.…”

Section: Introductionmentioning

confidence: 99%

Self-reported concussion history is not related to cortical volume in college athletes

Schultz,

Bouchard,

Barbot

et al. 2024

Preprint

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The long-term consequences of concussion are still being uncovered but have been linked to disruptions in cognition and psychological well-being. Previous studies focusing on the association between concussion history and structural changes in the brain have reported inconsistent results. We sought to examine the effect of concussion history on cortical volume with a focus on functional networks. These networks are associated with many of the functions that can be disrupted in those with an extensive concussion history. Collegiate athletes (n=296) in the present study who self-reported concussion history did not report different baseline symptoms and did not exhibit consistent differences in cognitive performance relative to those who reported no concussion history. We found that concussion history was not related to cortical volume at the network or region level, even when we compared participants with two or more concussions to those with no concussion history. We did identify relationships between cortical volume in the visual network and dorsal attention network with cognitive performance. In addition to comparing cortical volume between individuals with and without reported concussion history, we also examined whether cortical volume changes could be observed within individuals from baseline to acutely following concussion. We found that network level cortical volume did not change within subjects from baseline measurement to acutely post-concussion. Together, these results suggest that both self-reported concussion history and acute concussion effects are not associated with changes in cortical volume in young adult athletes.

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“…These findings may help identify older individuals at risk of progressive cognitive impairment, thereby facilitating timely intervention. At present, a combination of structural MRI and machine learning is used to classify older individuals with cognitive impairment [ 16 , 17 ]. However, there are few models to identify quantitative regional FC biomarkers sensitive to cognitive scores among healthy older individuals.…”

Section: Introductionmentioning

confidence: 99%

Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine

Zhang,

Ge,

Cheng

et al. 2024

BMC Med Imaging

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Background Quantitative determination of the correlation between cognitive ability and functional biomarkers in the older brain is essential. To identify biomarkers associated with cognitive performance in the older, this study combined an index model specific for resting-state functional connectivity (FC) with a supervised machine learning method. Methods Performance scores on conventional cognitive test scores and resting-state functional MRI data were obtained for 98 healthy older individuals and 90 healthy youth from two public databases. Based on the test scores, the older cohort was categorized into two groups: excellent and poor. A resting-state FC scores model (rs-FCSM) was constructed for each older individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using this model. To suggest the effectiveness of constructed model, the scores of these brain areas were used as feature matrix inputs for training an extreme learning machine. classification accuracy (CA) was then tested in separate groups and validated by N-fold cross-validation. Results This learning study could effectively classify the cognitive status of healthy older individuals according to the model scores of frontal lobe, temporal lobe, and parietal lobe with a mean accuracy of 86.67%, which is higher than that achieved using conventional correlation analysis. Conclusion This classification study of the rs-FCSM may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms.

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A review on Alzheimer’s disease classification from normal controls and mild cognitive impairment using structural MR images

Cited by 21 publications

References 224 publications

Insights into the pathological basis of dementia from population‐based neuropathology studies

Insights into the pathological basis of dementia from population‐based neuropathology studies

Self-reported concussion history is not related to cortical volume in college athletes

Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine

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