Pipeline Comparison for the Pre-Processing of Resting-State Data in Epilepsy

Noise removal is a critical step to recover the signal of interest from resting-state fMRI data. Several pre-processing pipelines have been developed mainly based on nuisance regression or independent component analysis. The aim of this work was to evaluate the ability in removing spurious non-BO LD signals of different cleaning pipelines when applied to a dataset of healthy controls and temporal lobe epilepsy patients. Increased tSNR and power spectral density in the resting-state frequency range (0.01-0.1 Hz) were found for all pre-processing pipelines with respect to the minimally pre-processed data, suggesting a positive gain in terms of temporal properties when optimal cleaning procedures are applied to the acquired fMRI data. All the pre-processing pipelines considered were able to recover the DMN through group ICA. By visually comparing this network across all the pipelines and groups, we found that AROMA, SPM12, FIX and FIXMC were able to better delineate the posterior cingulate cortex

What PLS can still do for Imaging Genetics in Alzheimer's disease

In this work we exploited Partial Least Squares (PLS) model for analyzing the genetic underpinning of grey matter atrophy in Alzheimer's Disease (AD). To this end, 42 features derived from T1-weighted Magnetic Resonance Imaging, including cortical thicknesses and subcortical volumes were considered to describe the imaging phenotype, while the genotype information consisted of 14 recently proposed AD related Polygenic Risk Scores (PRS), calculated by including Single Nucleotide Polymorphism passing different significance thresholds. The PLS model was applied on a large study cohort obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database including both healthy individuals and AD patients, and validated on an independent ADNI Mild Cognitive Impairment (MCI) cohort, including Early (EMCI) and Late MCI (LMCI). The experimental results confirm the existence of a joint dynamics between brain atrophy and genotype data in AD, while providing important generalization results when tested on a clinically heterogeneous cohort. In particular, less AD specific PRS scores were negatively correlated with cortical thicknesses, while highly AD specific PRSs showed a peculiar correlation pattern among specific subcortical volumes and cortical thicknesses. While the first outcome is in line with the well known neurodegeneration process in AD, the second could be revealing of different AD subtypes

Telomere length is causally connected to brain MRI image derived phenotypes: A mendelian randomization study.

Recent evidence suggests that shorter telomere length (TL) is associated with neuro degenerative diseases and aging related outcomes. The causal association between TL and brain characteristics represented by image derived phenotypes (IDPs) from different magnetic resonance imaging (MRI) modalities remains unclear. Here, we use two-sample Mendelian randomization (MR) to systematically assess the causal relationships between TL and 3,935 brain IDPs. Overall, the MR results suggested that TL was causally associated with 193 IDPs with majority representing diffusion metrics in white matter tracts. 68 IDPs were negatively associated with TL indicating that longer TL causes decreasing in these IDPs, while the other 125 were associated positively (longer TL leads to increased IDPs measures). Among them, ten IDPs have been previously reported as informative biomarkers to estimate brain age. However, the effect direction between TL and IDPs did not reflect the observed direction between aging and IDPs: longer TL was associated with decreases in fractional anisotropy and increases in axial, radial and mean diffusivity. For instance, TL was positively associated with radial diffusivity in the left perihippocampal cingulum tract and with mean diffusivity in right perihippocampal cingulum tract. Our results revealed a causal role of TL on white matter integrity which makes it a valuable factor to be considered when brain age is estimated and investigated

Multi-modal Brain Age Estimation: A Comparative Study Confirms the Importance of Microstructure

Brain age inferred from neuroimaging data could reveal important information about the evolution of structural and functional cerebral features across the life span. This has important implications for understanding healthy aging and for identifying Imaging-Derived Phenotypes (IDPs) that characterise age-related neurodegenerative illnesses, such as Alzheimer’s and Parkinson’s disease. The so-called brain age delta refers to the difference between image-derived brain age and chronological age. Accelerated aging (positive delta) or resilience to aging (negative delta) have been found to be useful correlates of factors such as disease and cognitive decline. Multiple studies have proposed prediction models using brain IDPs as predictor variables, mostly relying on simple linear regression. However, methodological and population heterogeneity in these studies precludes definitive conclusions regarding the most informative modelling methodologies or predictor IDPs. To provide first hints in this respect, in this paper we propose to address three questions. First, four different state-of-the-art models are ranked based on well-known performance indices (e.g., mean absolute error) using the UK Biobank brain MRI data in different single/multi-modal settings. Second, for the best model, the association with individual IDPs are calculated to identify those that could play a prominent role in the aging process. Third, associations with non-brain variables are assessed as a first step towards a holistic approach. Our findings demonstrate a prominent role for dMRI IDPs in reducing the mean absolute error and rank high in the association study, dominating the first ten positions and being preceded only by three structural measures that are known to be related to the aging process. This provides evidence of the potential of dMRI IDPs as biomarkers of aging in health and disease

INVESTIGATING EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR MRI-BASED CLASSIFICATION OF DEMENTIA: A NEW STABILITY CRITERION FOR EXPLAINABLE METHODS

Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer's Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans

A new scheme for the assessment of the robustness of explainable methods applied to brain age estimation

Deep learning methods show great promise in a range of settings including the biomedical field. Explainability of these models is important in these fields for building end-user trust and to facilitate their confident deployment. Although several Machine Learning Interpretability tools have been proposed so far, there is currently no recognized evaluation standard to transfer the explainability results into a quantitative score. Several measures have been proposed as proxies for quantitative assessment of explainability methods. However, the robustness of the list of significant features provided by the explainability methods has not been addressed. In this work, we propose a new proxy for assessing the robustness of the list of significant features provided by two explainability methods. Our validation is defined at functionality-grounded level based on the ranked correlation statistical index and demonstrates its successful application in the framework of brain aging estimation. We assessed our proxy to estimate brain age using neuroscience data. Our results indicate small variability and high robustness in the considered explainability methods using this new proxy

Convergent and discriminant validity of default mode network and limbic network perfusion in amnestic mild cognitive impairment patients

International audienceBackground: Previous studies reported default mode network (DMN) and limbic network (LIN) brain perfusion deficits in patients with amnestic mild cognitive impairment (aMCI), frequently a prodromal stage of Alzheimer’s disease (AD). However, the validity of these measures as AD markers has not yet been tested using MRI arterial spin labeling (ASL). Objective: To investigate the convergent and discriminant validity of DMN and LIN perfusion in aMCI. Methods: We collected core AD markers (amyloid-β 42 [Aβ42], phosphorylated tau 181 levels in cerebrospinal fluid [CSF]), neurodegenerative (hippocampal volumes and CSF total tau), vascular (white matter hyperintensities), genetic (apolipoprotein E [APOE] status), and cognitive features (memory functioning on Paired Associate Learning test [PAL]) in 14 aMCI patients. Cerebral blood flow (CBF) was extracted from DMN and LIN using ASL and correlated with AD features to assess convergent validity. Discriminant validity was assessed carrying out the same analysis with AD-unrelated features, i.e., somatomotor and visual networks’ perfusion, cerebellar volume, and processing speed. Results: Perfusion was reduced in the DMN (F = 5.486, p = 0.039) and LIN (F = 12.678, p = 0.004) in APOE ɛ4 carriers compared to non-carriers. LIN perfusion correlated with CSF Aβ42 levels (r = 0.678, p = 0.022) and memory impairment (PAL, number of errors, r = –0.779, p = 0.002). No significant correlation was detected with tau, neurodegeneration, and vascular features, nor with AD-unrelated features. Conclusion: Our results support the validity of DMN and LIN ASL perfusion as AD markers in aMCI, indicating a significant correlation between CBF and amyloidosis, APOE ɛ4, and memory impairment