Effective connectivity in individuals with Alzheimer's disease and mild cognitive impairment: A systematic review

Kazemi-Harikandei, Sayedeh-Zahra; Shobeiri, Parnian; Jelodar, Mohammad-Reza Salmani; Tavangar, Seyed Mohammad

doi:10.1016/j.neuri.2022.100104

Cited by 3 publications

(1 citation statement)

References 141 publications

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“…This work highlighted the involvement of additional brain regions for emotional materials, visual in nature, and offered a testable mechanism of action to explain why emotional material is typically better remembered than neutral. Current efforts are now finding that changes in connectivity (weakened ones) between these brain regions are notable in older adults with mild cognitive impairment ( Kazemi-Harikandei et al, 2022 ), and can be used as early markers of Alzheimer disease. In order to assess the need for intact connectivity in various memory functions, a recent major breakthrough in the field comes from the use of the Trans Magnetic Stimulation (TMS) technique.…”

Section: Modern Cognitive Neurosciencementioning

confidence: 99%

A case for using methods from natural science in advancing the field of cognitive neuroscience

Fernandes

2024

Theory & Psychology

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Cognitive neuroscience seeks to pinpoint the neural basis of cognitive function. Application of scientific methods can be credited for its advancement within the field of psychology. Past approaches such as phrenology, that linked bumps on the skull to mental capabilities, initially gained popularity, but the lack of experimental testing contributed to its demise. Research in neuropsychology and the use of the double dissociation experimental technique subsequently emerged. Objective measurements of behaviour following selective damage within the brain led to a paradigm shift. More recently, application of the subtraction technique, coupled with the emergence of cognitive neuroimaging tools, has allowed psychologists to isolate and measure specific functions such as language, vision, memory, and recognition of emotion. Importantly, these approaches enable reliable prediction of behaviours, given parameters of brain integrity, a key goal within the field of psychology.

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Section: Modern Cognitive Neurosciencementioning

confidence: 99%

A case for using methods from natural science in advancing the field of cognitive neuroscience

Fernandes

2024

Theory & Psychology

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Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer’s Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study

et al. 2023

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(1) Background: Alzheimer’s disease (AD) is a neurodegenerative disease with a high prevalence. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. The study of effective connectivity allows the evaluation of intensity flow and signaling pathways in functional regions, even in the early stage, known as amnestic mild cognitive impairment (aMCI). (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. (3) Results: The maps of effective connectivity in the brain networks of the three groups were different, and the direction and strength of the causal effect with the progression of the disease showed substantial changes. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. Effective connectivity can serve as a potential marker of Alzheimer’s pathophysiology, even in the early stages of the disease.

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fMRI-based Alzheimer’s disease detection via functional connectivity analysis: a systematic review

Alarjani,

Almarri

2024

PeerJ Computer Science

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Alzheimer’s disease is a common brain disorder affecting many people worldwide. It is the primary cause of dementia and memory loss. The early diagnosis of Alzheimer’s disease is essential to provide timely care to AD patients and prevent the development of symptoms of this disease. Various non-invasive techniques can be utilized to diagnose Alzheimer’s in its early stages. These techniques include functional magnetic resonance imaging, electroencephalography, positron emission tomography, and diffusion tensor imaging. They are mainly used to explore functional and structural connectivity of human brains. Functional connectivity is essential for understanding the co-activation of certain brain regions co-activation. This systematic review scrutinizes various works of Alzheimer’s disease detection by analyzing the learning from functional connectivity of fMRI datasets that were published between 2018 and 2024. This work investigates the whole learning pipeline including data analysis, standard preprocessing phases of fMRI, feature computation, extraction and selection, and the various machine learning and deep learning algorithms that are used to predict the occurrence of Alzheimer’s disease. Ultimately, the paper analyzed results on AD and highlighted future research directions in medical imaging. There is a need for an efficient and accurate way to detect AD to overcome the problems faced by patients in the early stages.

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Effective connectivity in individuals with Alzheimer's disease and mild cognitive impairment: A systematic review

Cited by 3 publications

References 141 publications

A case for using methods from natural science in advancing the field of cognitive neuroscience

A case for using methods from natural science in advancing the field of cognitive neuroscience

Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer’s Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study

fMRI-based Alzheimer’s disease detection via functional connectivity analysis: a systematic review

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