Alzheimer's Disease and Other Neurodegenerative Diseases

Fuller, Stephanie J.; Sohrabi, Hamid R.; Goozee, Kathryn; Sankaranarayanan, Anoop; Martins, Ralph N.

doi:10.1002/9781119356752.ch2

Cited by 9 publications

(7 citation statements)

References 141 publications

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“…The only limitation of such an approach is that it is hard to exclude the early stages of neurodegeneration in the examinees with non-invasive methods. The way to overcome the issue is to use the recent practice parameters of the American Academy of Neurology that recommends resorting to neuroimaging (e.g., MRI) to detect reversible and potentially treatable neurodegenerative conditions (Fuller et al, 2019 ). The very low number of cases that remain undiagnosed with the radiological studies do not compromise the obtained classification model.…”

Section: Introductionmentioning

confidence: 99%

Brain Morphometry and Cognitive Performance in Normal Brain Aging: Age- and Sex-Related Structural and Functional Changes

Statsenko

Habuza

Smetanina

et al. 2022

Front. Aging Neurosci.

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BackgroundThe human brain structure undergoes considerable changes throughout life. Cognitive function can be affected either negatively or positively. It is challenging to segregate normal brain aging from the accelerated one.ObjectiveTo work out a descriptive model of brain structural and functional changes in normal aging.Materials and MethodsBy using voxel-based morphometry and lesion segmentation along with linear statistics and machine learning (ML), we analyzed the structural changes in the major brain compartments and modeled the dynamics of neurofunctional performance throughout life. We studied sex differences in lifelong dynamics of brain volumetric data with Mann-Whitney U-test. We tested the hypothesis that performance in some cognitive domains might decline as a linear function of age while other domains might have a non-linear dependence on it. We compared the volumetric changes in the major brain compartments with the dynamics of psychophysiological performance in 4 age groups. Then, we tested linear models of structural and functional decline for significant differences between the slopes in age groups with the T-test.ResultsWhite matter hyperintensities (WMH) are not the major structural determinant of the brain normal aging. They should be viewed as signs of a disease. There is a sex difference in the speed and/or in the onset of the gray matter atrophy. It either starts earlier or goes faster in males. Marked sex difference in the proportion of total cerebrospinal fluid (CSF) and intraventricular CSF (iCSF) justifies that elderly men are more prone to age-related brain atrophy than women of the same age.ConclusionThe article gives an overview and description of the conceptual structural changes in the brain compartments. The obtained data justify distinct patterns of age-related changes in the cognitive functions. Cross-life slowing of decision-making may follow the linear tendency of enlargement of the interhemispheric fissure because the center of task switching and inhibitory control is allocated within the medial wall of the frontal cortex, and its atrophy accounts for the expansion of the fissure. Free online tool at https://med-predict.com illustrates the tests and study results.

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Section: Introductionmentioning

confidence: 99%

Brain Morphometry and Cognitive Performance in Normal Brain Aging: Age- and Sex-Related Structural and Functional Changes

Statsenko

Habuza

Smetanina

et al. 2022

Front. Aging Neurosci.

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“…AD commonly exhibits neuropathological features that include the formation of neurofibrillary tangles (NFTs) composed of intraneuronal aggregates of hyperphosphorylated Tau protein and senile plaques as well as extracellular aggregates of amyloid β (Aβ) peptides . In addition, these neuropathological characteristics are accompanied by a neuroinflammatory response. , The loss of synapses and neurons in the cerebral cortex (temporal lobe and parietal lobe), parts of the frontal cortex, and the hippocampus and other subcortical regions are the pathobiological characteristic features of AD . Moreover, people with AD demonstrate memory loss, confusion, difficulty in thinking, and changes in behavior, personality, and language …”

Section: Introductionmentioning

confidence: 99%

“…19,20 The loss of synapses and neurons in the cerebral cortex (temporal lobe and parietal lobe), parts of the frontal cortex, and the hippocampus and other subcortical regions are the pathobiological characteristic features of AD. 21 Moreover, people with AD demonstrate memory loss, confusion, difficulty in thinking, and changes in behavior, personality, and language. 22 There is a growing understanding of AD being a risk factor for the development of epileptic seizures, as well as seizures that might occur throughout the AD process, including in the presymptomatic phase.…”

Section: Introductionmentioning

confidence: 99%

Tau Related Pathways as a Connecting Link between Epilepsy and Alzheimer’s Disease

Paudel

Angelopoulou

Jones

et al. 2019

ACS Chem. Neurosci.

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Emerging findings point toward an important interconnection between epilepsy and Alzheimer's disease (AD) pathogenesis. Patients with epilepsy (PWE) commonly exhibit cognitive impairment similar to AD patients, who in turn are at a higher risk of developing epilepsy compared to age-matched controls. To date, no disease-modifying treatment strategy is available for either epilepsy or AD, reflecting an immediate need for exploring common molecular targets, which can delineate a possible mechanistic link between epilepsy and AD. This review attempts to disentangle the interconnectivity between epilepsy and AD pathogenesis via the crucial contribution of Tau protein. Tau protein is a microtubule-associated protein (MAP) that has been implicated in the pathophysiology of both epilepsy and AD. Hyperphosphorylation of Tau contributes to the different forms of human epilepsy and inhibition of the same exerted seizure inhibitions and altered disease progression in a range of animal models. Moreover, Tau-protein-mediated therapy has demonstrated promising outcomes in experimental models of AD. In this review, we discuss how Tau-related mechanisms might present a link between the cause of seizures in epilepsy and cognitive disruption in AD. Untangling this interconnection might be instrumental in designing novel therapies that can minimize epileptic seizures and cognitive deficits in patients with epilepsy and AD.

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“…They were found healthy based on the clinical examination and the negative results of MRI which was conducted to rule out any underlying pathology. In this way, we followed the recent practice parameters of the American Academy of Neurology that recommend neuroimaging (e.g., MRI) to detect neurodegenerative conditions in early stages (Fuller et al, 2019 ). Supplementary Table 1 shows the distribution of the subjects across age and sex groups.…”

Section: Methodsmentioning

confidence: 99%

Proportional Changes in Cognitive Subdomains During Normal Brain Aging

Statsenko

Habuza

Gorkom

et al. 2021

Front. Aging Neurosci.

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Background: Neuroscience lacks a reliable method of screening the early stages of dementia.Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains.Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes.Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p > 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p < 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p > 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age.Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.

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Alzheimer's Disease and Other Neurodegenerative Diseases

Cited by 9 publications

References 141 publications

Brain Morphometry and Cognitive Performance in Normal Brain Aging: Age- and Sex-Related Structural and Functional Changes

Brain Morphometry and Cognitive Performance in Normal Brain Aging: Age- and Sex-Related Structural and Functional Changes

Tau Related Pathways as a Connecting Link between Epilepsy and Alzheimer’s Disease

Proportional Changes in Cognitive Subdomains During Normal Brain Aging

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