Diffusion tensor imaging (DTI) Analysis Based on Tract-based spatial statistics (TBSS) and Classification Using  Multi-Metric in Alzheimer's Disease

Zhang, Yingteng; Zhan, Feibiao

doi:10.31083/j.jin2204101

Cited by 2 publications

(4 citation statements)

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“…‣ AD (e.g., Nir et al, 2015 – multicenter (FSL, TBSS); Douaud et al, 2011 ; Bourbon-Teles et al, 2023 ; Zhang and Zhan, 2023 – monocenter (TBSS); Chen Y. et al, 2023 – review; Takahashi et al, 2024 – NODDI).…”

Section: Postprocessing-related Contributions To Results Of Dti Studi...mentioning

confidence: 99%

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Müller,

Kassubek

2024

Front. Hum. Neurosci.

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Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

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Section: Postprocessing-related Contributions To Results Of Dti Studi...mentioning

confidence: 99%

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Müller,

Kassubek

2024

Front. Hum. Neurosci.

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“…However, there are similar approaches reported in the literature, which we can compare to gain further insights into our proposed approach. Zhang and Zhan [18] , utilized DTI data from 37 AD patients and 36 healthy controls to identify white matter fiber tracts that are damaged in AD employing SVM-RFE. 78.8% accuracy was shown using FA+DA with 75 features selected by RFE.…”

Section: Discussionmentioning

confidence: 99%

“…al. [18], utilized DTI data from 37 AD patients and 36 healthy controls to identify white matter fiber tracts that are damaged in AD employing SVM-RFE. 78.8% accuracy was shown using FA+DA with 75 features selected by RFE.…”

Section: Discussionmentioning

confidence: 99%

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Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome

Singh,

Gupta,

Rajapakse

2024

IEEE J. Transl. Eng. Health Med.

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Brain state classification by applying deep learning techniques on neuroimaging data has become a recent topic of research. However, unlike domains where the data is low dimensional or there are large number of available training samples, neuroimaging data is high dimensional and has few training samples. To tackle these issues, we present a sparse feedforward deep neural architecture for encoding and decoding the structural connectome of the human brain. We use a sparsely connected element-wise multiplication as the first hidden layer and a fixed transform layer as the output layer. The number of trainable parameters and the training time is significantly reduced compared to feedforward networks. We demonstrate superior performance of this architecture in encoding the structural connectome implicated in Alzheimer’s disease (AD) and Parkinson’s disease (PD) from DTI brain scans. For decoding, we propose recursive feature elimination (RFE) algorithm based on DeepLIFT, layer-wise relevance propagation (LRP), and Integrated Gradients (IG) algorithms to remove irrelevant features and thereby identify key biomarkers associated with AD and PD. We show that the proposed architecture reduces 45.1% and 47.1% of the trainable parameters compared to a feedforward DNN with an increase in accuracy by 2.6 % and 3.1% for cognitively normal (CN) vs AD and CN vs PD classification, respectively. We also show that the proposed RFE method leads to a further increase in accuracy by 2.1% and 4% for CN vs AD and CN vs PD classification, while removing approximately 90% to 95% irrelevant features. Furthermore, we argue that the biomarkers (i.e., key brain regions and connections) identified are consistent with previous literature. We show that relevancy score-based methods can yield high discriminative power and are suitable for brain decoding. We also show that the proposed approach led to a reduction in the number of trainable network parameters, an increase in classification accuracy, and a detection of brain connections and regions that were consistent with earlier studies.

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Diffusion tensor imaging (DTI) Analysis Based on Tract-based spatial statistics (TBSS) and Classification Using Multi-Metric in Alzheimer's Disease

Cited by 2 publications

References 0 publications

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome

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