A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI

Tau pathology mediated the plasma biomarkers and cognitive function in patients with mild cognitive impairment

Glial fibrillary acidic protein (GFAP) and neurofilament light (NfL) are putative non-amyloid biomarkers indicative of ongoing inflammatory and neurodegenerative disease processes. Hence, this study aimed to demonstrate the relationship between plasma biomarkers (GFAP and NfL) and 18F-AV-1451 tau PET images, and to explore their effects on cognitive function. Ninety-one participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and 20 participants from the Shanghai Action of Prevention Dementia for the Elderly (SHAPE) cohort underwent plasma biomarker testing, 18F-AV-1451 tau PET scans and cognitive function assessments. Within the ADNI, there were 42 cognitively normal (CN) individuals and 49 with mild cognitive impairment (MCI). Similarly, in the SHAPE, we had 10 CN and 10 MCI participants. We calculated the standardized uptake value ratios (SUVRs) for the regions of interest (ROIs) in the 18F-AV-1451 PET scans. Using plasma biomarkers and regional SUVRs, we trained machine learning models to differentiate between MCI and CN subjects with ADNI database and validated in SHAPE. Results showed that eight selected variables (including left amygdala SUVR, right amygdala SUVR, left entorhinal cortex SUVR, age, education, plasma NfL, plasma GFAP, plasma GFAP/ NfL) identified by LASSO could differentiate between the MCI and CN individuals, with AUC ranging from 0.783 to 0.926. Additionally, cognitive function was negatively associated with the plasma biomarkers and tau deposition in amygdala and left entorhinal cortex. Increased tau deposition in amygdala and left entorhinal cortex were related to increased plasma biomarkers. Moreover, tau pathology mediated the effect of plasma biomarkers level on the cognitive decline. The present study provides valuable insights into the association among plasma markers (GFAP and NfL), regional tau deposition and cognitive function. This study reports the mediation effect of brain regions tau deposition on the plasma biomarkers level and cognitive function, indicating the significance of tau pathology in the MCI patients.

Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?

The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques-for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer's disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.

A hybrid multimodal machine learning model for Detecting Alzheimer's disease

Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.

Challenges and Perspectives of Neurological Disorders

Neurological disorders pose significant challenges to healthcare systems worldwide [...].