fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

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

doi:10.1007/s11760-020-01789-y

Cited by 2 publications

(3 citation statements)

References 38 publications

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“…This is identical to nonlinear relationships in the primary space. In this study based on (Ahmadi et al, 2020a), the polynomial kernel function has opted.…”

Section: -Kernel Trick and Pccmentioning

confidence: 99%

“…Recently, a study has been conducted on a non-linear alternative for PCC based on fMRI time-series of Alzheimer's Disease (AD). In this study (Ahmadi, Fatemizadeh, & Motie-Nasrabadi, 2020a), they used the kernel trick which a polynomial kernel to increase the dimension of the input space and perform the PCC calculation in a new space. The PCC in the new space is equivalent to non-linear relations in the primary space.…”

Section: Introductionmentioning

confidence: 99%

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FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear Approaches

Ahmadi

Fatemizadeh

Nasrabadi

2021

Preprint

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Neuroimaging data analysis reveals the underlying interactions in the brain. It is essential, yet controversial, to choose a proper tool to manifest brain functional connectivity. In this regard, researchers have not reached a definitive conclusion between the linear and non-linear approaches, as both have pros and cons. In this study, to evaluate this concern, the functional Magnetic Resonance Imaging (fMRI) data of different stages of Alzheimer’s disease are investigated. In the linear approach, the Pearson Correlation Coefficient (PCC) is employed as a common technique to generate brain functional graphs. On the other hand, for non-linear approaches, two methods including Distance Correlation (DC) and the kernel trick are utilized. By the use of the three mentioned routines and graph theory, functional brain networks of all stages of Alzheimer’s disease (AD) are constructed and then sparsed. Afterwards, graph global measures are calculated over the networks and a non-parametric permutation test is conducted. Results reveal that the non-linear approaches have more potential to discriminate groups in all stages of AD. Moreover, the kernel trick method is more powerful in comparison to the DC technique. Nevertheless, AD degenerates the brain functional graphs more at the beginning stages of the disease. At the first phase, both functional integration and segregation of the brain degrades, and as AD progressed brain functional segregation further declines. The most distinguishable feature in all stages is the clustering coefficient that reflects brain functional segregation.

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“…This is identical to nonlinear relationships in the primary space. In this study based on (Ahmadi et al, 2020a), the polynomial kernel function has opted.…”

Section: -Kernel Trick and Pccmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear Approaches

Ahmadi

Fatemizadeh

Nasrabadi

2021

Preprint

Self Cite

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“…The machine-learning approaches are efficient in identifying and classifying the resting state fMRI images utilizing predetermined and labeled categories. The automatic diagnosis of AD plays a crucial role in healthcare systems because timely treatment significantly decreases the mortality rate [ 13 ]. Whereas, investigating complex brain structures in fMRI images is a time-consuming and complex task [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

Chelladurai,

Narayan,

Divakarachari

et al. 2023

Brain Sciences

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In the present scenario, Alzheimer’s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.

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fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

Cited by 2 publications

References 38 publications

FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear Approaches

FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear Approaches

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

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