1,720,982 research outputs found
Diffusion MRI Allows Capturing the Amyloid-β and τ Proteins Status in Alzheimer’s Disease Continuum
No full textAlzheimer's Disease (AD) is a neurodegenerative process characterized by the accumulation of amyloid-β (Aβ) and tau (τ) proteins leading to neurodegeneration. It has been hypothesizes that the aggregation of these proteins could spread through specific white matter (WM) pathways. To investigate such hypothesis, convolutional neural networks were employed to analyze microstructural alterations induced by the accumulation of Aβ and τ proteins via two classification tasks, relying on mean diffusivity (MD) and fractional anisotropy (FA), achieving competitive performance. A post-hoc analysis via integrated gradients revealed that the splenium of the corpus callosum played a prominent role for both indices, while index-specific were the ventricles and subcortical regions for MD and the corticospinal tract for FA. This supports the assumption that WM pathways might play a key role in misfolded protein distribution and highlights the potential of diffusion imaging for the identification of Aβ and τ proteins spread and accumulation
Cortico-Subcortical motor network integrity relates to functional recovery after stroke
No full textIn this work, we investigated whether the structural properties of cortico-subcortical (CS) motor circuits are related to motor outcome after stroke. To do this, we acquired Diffusion Spectrum Imaging data in 10 stroke patients at 3 time points after stroke. We then performed tractographic reconstruction and estimated a number of microstructural indices, derived from the 3D Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) model, in the cortico-subcortical motor fiber tracts. Linear regression analysis showed that SHORE metrics of thalamo-cortical and intrastriatal connections in the first week after stroke are strongly related to stroke recovery at 6 months follow-up
Objective Assessment of the Bias Introduced by Baseline Signals in XAI Attribution Methods
No full textThis work represents a first step towards a systematic analysis of the impact of the choice of the baseline signals to be used in explainable baseline-dependent methods for multi-modal and multi-dimensional data relying on single-input deep networks, in view of the generalization to multi -channel architectures. This point is critical for ensuring the soundness of the attribution values and enabling their subsequent validation through association studies. In this work, two different CNNs were implemented to predict Alzheimer's disease patients from control subjects using structural Magnetic Resonance Imaging volumes and genetics data. The Integrated Gradients method was applied to both models for post-hoc attribution visualization relying on different baselines. Differences in the attribution maps were found with respect to the attributions of the reference baseline in both modalities highlighting the importance of finding and using the 'optimal' baseline. We believe this work is highly relevant for the community in the framework of the validation of XAI post-hoc methods, as it provides evidence of the impact of the choice of the baselines for deriving feature attribution values with the Integrated Gradients method which determines the reliability of the outcomes, improving both the awareness of the users and their trust in the methods
Coordinate-based meta-analysis of resting fMRI studies for the identification of potential targets for brain stimulation in AD and bvFTD
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Test-Retest Reliability of Graph Metrics in Functional Brain Network
No full textThe statistical link between spontaneous fluctuations occurring in different parts of the brain can provide insights into its functional organization. Here, we used high-quality resting-state fMRI (rs-fMRI) data acquired with a test-retest (TRT) paradigm to assess the reliability of graph metrics. After applying time/frequency methods to generate FC matrices, we restricted our focus on global, local and central graph measure through different statistical measures, including but not limited to the intraclass correlation coefficient (ICC). We found that full correlation and magnitude square coherence yielded more reproducible measurements than the other metrics, as revealed by ICC values. These results have important implications when choosing metrics for quantifying FC in rs-fMRI studies, adding novel information to the current panorama of information on TRT reliability topic
Can Diffusion MRI Reveal Stroke-Induced Microstructural Changes in GM?
No full textThe development of noninvasive techniques to image the human brain has enabled the demonstration of structural plasticity in response to motor learning. In the last years evidence has emerged on the potential of some measures derived from diffusion Magnetic Resonance Imaging (DMRI) as numerical biomarkers of tissue changes in regions involved in the motor network. In these works, the descriptors were extensively analysed in contralateral white matter (WM) along both single connections and networks relying on tract-based analyses and statistical evaluation. Though, their ability to detect changes in gray matter (GM) has been scarcely investigated. This work aims at the assessment of propagator-based microstructural indices in capturing GM changes and the relation of such changes to functional recovery at six months from the injury focusing on the Diffusion Tensor Imaging (DTI) and the three dimensional Simple Harmonics Oscillator based Reconstruction and Estimation (3D-SHORE) models
Telomere length is causally connected to brain MRI image derived phenotypes: A mendelian randomization study
Full text linkRecent 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
Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum
No full textAmyloid-β (Aβ) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer’s disease. It is well-known that the identification of individuals with Aβ positivity could enable early diagnosis. In this work, we aim at capturing the Aβ positivity status in an unbalanced cohort enclosing subjects at different disease stages, exploiting the underlying structural and connectivity disease-induced modulations as revealed by structural, functional, and diffusion MRI. Of note, due to the unbalanced cohort, the outcomes may be guided by those factors rather than amyloid accumulation. The partial views provided by each modality are integrated in the model, allowing to take full advantage of their complementarity in encoding the effects of the Aβ accumulation, leading to an accuracy of 0.762 ± 0.04. The specificity of the information brought by each modality is assessed by post hoc explainability analysis (guided backpropagation), highlighting the underlying structural and functional changes. Noteworthy, well-established biomarker key regions related to Aβ deposition could be identified by all modalities, including the hippocampus, thalamus, precuneus, and cingulate gyrus, witnessing in favor of the reliability of the method as well as its potential in shedding light on modality-specific possibly unknown Aβ deposition signatures
Assessment of Mean Apparent Propagator-based indices as biomarkers of axonal remodeling after stroke
No full textRecently, a robust mathematical formulation has been introduced for the closed-form analytical reconstruction of the signal and the %Ensemble Average Propagator (EAP) Mean Apparent Propagator (MAP) in diffusion MRI. This is referred to as MAP-MRI or 3D-SHORE depending on the chosen reference frame. % that is Cartesian in the first case and polar in the second. From the MAP, microstructural properties can be inferred by the derivation of indices that under certain circumstances allow the estimation of of pores' geometry and local diffusivity, holding the potential of becoming the next generation of microstructural numerical biomarkers.In this work, we propose the assessment and validation of a subset of such indices that is %Return to Axis Probability (RTAP), axon diameter (D) and Propagator Anisotropy (PA) RTAP, D, and PA for the quantitative analysis of axonal remodeling in the uninjured motor network after stroke. Diffusion Spectrum Imaging (DSI) was performed on %a cohort of 20 subjects (ten patients and ten controls at different time points and the indices were derived and exploited for tract-based quantitative analysis. Our results provide quantitative evidence on the eligibility of the derived indices as microstructural biomarkers
Resting-state cerebral blood flow and functional connectivity in focal epilepsy as assessed by arterial spin labeling
No full textArterial spin labeling can be very useful for the detection of perfusion changes in drug-resistant focal epilepsy. In order to identify regions related to the epileptic focus, quantification of CBF and a statistical analysis were computed in each patient and compared with a template of normal perfusion. A seed-driven connectivity was also used to identify networks regions that are differently organized in epileptic patients compared to healthy subjects. The investigation allowed us to correctly identify the epileptogenic zone in patients, in whom the results were confirmed by surgical resection and subsequent seizure freedom
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