Deep sparse graph functional connectivity analysis in AD patients using fMRI data

Ahmadi, Hessam; Fatemizadeh, Emad; Nasrabadi, Ali Motie

doi:10.1016/j.cmpb.2021.105954

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…Our findings highlight the activation of the frontal and temporal regions in response to non-painful thermal stimuli in the theta and alpha frequency bands, and the interconnection between these regions in cognitive and sensory processes. The networks emerging from these regions are crucial in emotional assessments, sensory perceptions, and cognitive processes related to non-painful thermal stimuli [46][47][48][49][50][51][52][53]. The opposite direction of information flow between warmth and cold, especially in the frontal and temporal regions, is consistent with the existence of specialized neural pathways for each type of stimulus.…”

Section: Discussionmentioning

confidence: 90%

“…Brain connectivity analysis is a prominent approach that is being used to gain a deeper understanding of the involvement of different regions of the brain in various applications, such as neuropsychiatric disorders [46][47][48], sensory processing [49,50], and cognitive task evaluations [51,52]. Effective connectivity, which is the focus of our study, involves the analysis of direct and indirect causal influences among brain regions, providing insights into the directional effects and potential mechanisms underlying these interactions.…”

Section: Introductionmentioning

confidence: 99%

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Effective brain connectivity related to non-painful thermal stimuli using EEG

Santos Cuevas,

Campos Ruiz,

Collina

et al. 2024

Biomed. Phys. Eng. Express

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Understanding the brain response to thermal stimuli is crucial in the sensory experience. This study focuses on non-painful thermal stimuli, which are sensations induced by temperature changes without causing discomfort. These stimuli are transmitted to the central nervous system through specific nerve fibers and are processed in various regions of the brain, including the insular cortex, the prefrontal cortex, and anterior cingulate cortex. Despite the prevalence of studies on painful stimuli, non-painful thermal stimuli have been less explored. This research aims to bridge this gap by investigating brain functional connectivity during the perception of non-painful warm and cold stimuli using electroencephalography (EEG) and the partial directed coherence technique (PDC). Our results demonstrate a clear contrast in the direction of information flow between warm and cold stimuli, particularly in the theta and alpha frequency bands, mainly in frontal and temporal regions. The use of PDC highlights the complexity of brain connectivity during these stimuli and reinforces the existence of different pathways in the brain to process different types of non-painful warm and cold stimuli.

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Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Effective brain connectivity related to non-painful thermal stimuli using EEG

Santos Cuevas,

Campos Ruiz,

Collina

et al. 2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

show abstract

“…Brain neurons are interconnected to form vast and complex networks, and fMRI contains a lot of data. Some researchers carry out feature extraction on the obtained data and apply the data to automatic diagnosis of brain diseases and other fields [2] [3]. However, there are relatively few researches on the extraction of time series features, and no time attribute is added to the data for research.…”

Section: Introductionmentioning

confidence: 99%

Interactive Visualization of Temporal Brain Connectivity Data based-on Frequent Feature Mining (S)

Zhang,

Jing,

Liu

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

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Medical data visualization is instrumental in assisting disease diagnosis and exploring brain function and structure. In this paper, we constructed a brain connectivity network using changes in BOLD signals at different time intervals and identified frequent characteristics to help doctors quickly pinpoint areas of interest. To study the changes in connectivity between brain regions, we visualize frequent sequences and compare them, highlighting important temporal features of patient brain areas. This makes the study and analysis of fMRI data more convenient and assists doctors in investigating abnormalities in the connections between brain functional areas.

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“…Analysis has shown that metabolic alterations occur before atrophy in people at risk for AD and a functional biomarker can be identified before the specific protein profiles connected to advanced AD using FDG-PET (Ou et al 2019 ; Veitch et al 2022 ; Kim et al 2022 ). Considering the progression of AD and cognitive impairment, fMRI techniques can track AD-related brain damage (Hojjati et al 2017 ; Ahmadi et al 2021 ; Li et al 2020 ). DTI gives information on the structure of the brain in the form of Mean Diffusivity (MD), Fractional Anisotropy (FA), and Echo Planar Imaging (EPI) intensities (De and Chowdhury 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Odusami,

Maskeliūnas,

Damaševičius

et al. 2023

Cogn Neurodyn

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In recent years, Alzheimer’s disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87–87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.

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Deep sparse graph functional connectivity analysis in AD patients using fMRI data

Cited by 7 publications

References 32 publications

Effective brain connectivity related to non-painful thermal stimuli using EEG

Effective brain connectivity related to non-painful thermal stimuli using EEG

Interactive Visualization of Temporal Brain Connectivity Data based-on Frequent Feature Mining (S)

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

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