Structural Brain Network Correlated with Reading Impairment in Alzheimer’s Disease

Ota, Miho; Koshibe, Yuko; Higashi, Shinji; Nemoto, Kiyotaka; Tsukada, Eriko; Tamura, Masashi; Takahashi, Takumi; Arai, Tetsuaki

doi:10.1159/000508406

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…1(c), HSN∈R N×N ) to characterize the temporal-interaction between neurons in the same layer. The results of a large number of previous studies have proved that the clustering coefficient has great advantages in characterizing brain network differences, and has been widely used as the core feature in the classification of neurological diseases [32][33][34]. Thus, we extract the clustering coefficients of HSN to act as the coarse-grained features, the specific definition is as follows:…”

Section: F Feature Extraction and Evaluation Criteriamentioning

confidence: 99%

Recognition of Dementia Biomarkers With Deep Finer-DBN

Xia

Zhou

Mamoon

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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The treatment of neurodegenerative diseases is expensive, and long-term treatment makes families bear a heavy burden. Accumulating evidence suggests that the high conversion rate can possibly be reduced if clinical interventions are applied at the early stage of brain diseases. Thus, a variety of deep learning methods are utilized to recognize the early stages of neurodegenerative diseases for clinical intervention and treatment. However, most existing methods have ignored the issue of sample imbalance, which often makes it difficult to train an effective model due to lack of a large number of negative samples. To address this problem, we propose a two-stage method, which is used to learn the compression and recover rules of normal subjects so that potential negative samples can be detected. The experimental results show that the proposed method can not only obtain a superb recognition result, but also give an explanation that conforms to the physiological mechanism. Most importantly, the deep learning model does not need to be retrained for each type of disease, which can be widely applied to the diagnosis of various brain diseases. Furthermore, this research could have great potential in understanding regional dysfunction of various brain diseases.

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Section: F Feature Extraction and Evaluation Criteriamentioning

confidence: 99%

Recognition of Dementia Biomarkers With Deep Finer-DBN

Xia

Zhou

Mamoon

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

“…Persons with AD exhibit a multitude of cognitive, affective, and physical manifestations to variable degrees at different stages. Aphasia can manifest as an early feature in some patients, particularly word-finding difficulty and circumlocution; apraxia and visuospatial deficits then typically follow but vary considerably in time of onset ( Bird et al, 2010 ; Ota et al, 2020 ). Higher brain functions, including executive functions, continue to decline as the disease progresses, such as apraxia, aphasia, and agnosia; and reading impairment manifests as atrophy reaches the posterior cortex ( Ota et al, 2020 ).…”

Section: Clinical Manifestations Of Alzheimer’s Diseasementioning

confidence: 99%

Preserved Consciousness in Alzheimer’s Disease and Other Dementias: Caregiver Awareness and Communication Strategies

Warren

2021

Front. Psychol.

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Alzheimer’s disease is an insidious onset neurodegenerative syndrome without effective treatment or cure. It is rapidly becoming a global health crisis that is overwhelming healthcare, society, and individuals. The clinical nature of neurocognitive decline creates significant challenges in bidirectional communication between caregivers and persons with Alzheimer’s disease (AD) that can negatively impact quality-of-life. This paper sought to understand how and to what extent would awareness training about the levels of consciousness in AD influence the quality-of-life interactions in the caregiver-patient dyad. A literature review of multiple databases was conducted utilizing a transdisciplinary approach. The sum of findings indicates a positive relationship between enhanced caregiver awareness and training, positive interactions, and improved QOL measures among patients and caregivers. A multidirectional relationship was found among healthcare policies, training and education resources, caregivers, and persons with AD. Specifically, the current lack of policy and inadequate training and educational resources has various detrimental effects on patients and caregivers, while improvements in training and education of caregivers yields positive outcomes in communication and QOL. Furthermore, evidence of preserved consciousness in persons with AD was demonstrated from multiple disciplines, including neurobiological, psychological, and biopsychosocial models. The literature further revealed several methods to access the preserved consciousness in persons with AD and related dementias, including sensory, emotional, and cognitive stimulations. The evidence from the literature suggests a reframed approach to our understanding and treatment of persons with AD is not only warranted, but crucial to address the needs of those affected by AD.

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“…Network analysis has been used to investigate the network dynamics of neurological diseases such as AD and mild cognitive impairment [ 39 , 40 , 41 ]. Resting state connectivity and network topology are increasingly being studied to understand the effect of aging on specific brain regions connected in a resting state.…”

Section: Introductionmentioning

confidence: 99%

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

2022

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Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.

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Structural Brain Network Correlated with Reading Impairment in Alzheimer’s Disease

Cited by 5 publications

References 34 publications

Recognition of Dementia Biomarkers With Deep Finer-DBN

Recognition of Dementia Biomarkers With Deep Finer-DBN

Preserved Consciousness in Alzheimer’s Disease and Other Dementias: Caregiver Awareness and Communication Strategies

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

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