AIMC Topic: Diffusion Tensor Imaging

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Associations among white matter microstructural changes and the development of emotional reactivity and regulation in infancy.

Molecular psychiatry
Deficits in emotional reactivity and regulation assessed in infancy, including high levels of negative emotionality (NE), low positive emotionality (PE) and low soothability, can predict future affective and behavioral disorders. White matter (WM) tr...

Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes.

Medical image analysis
Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI frame...

A multimodal MRI-based machine learning framework for classifying cognitive impairment in cerebral small vessel disease.

Scientific reports
The heterogeneity of cerebral small vessel disease (CSVD) with mild cognitive impairment (MCI) presents a challenge for diagnosis and classification. This study aims to propose a multimodal magnetic resonance imaging (MRI)-based machine learning fram...

Accelerated diffusion tensor imaging with self-supervision and fine-tuning.

Scientific reports
Diffusion tensor imaging (DTI) is essential for assessing brain microstructure but requires long acquisition times, limiting clinical use. Recent deep learning (DL) approaches, such as SuperDTI or deepDTI, improve DTI metrics but demand large, high-q...

Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks.

Medical image analysis
Multimodal neuroimaging data modeling has become a widely used approach but confronts considerable challenges due to their heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitate...

Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning.

Scientific reports
Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous diseases and processes affecting the central nervous system can be detected and monitored via diffusi...

Coupling analysis of diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) with abnormal cerebral blood flow in methamphetamine-dependent patients and its application in machine-learning-based classification.

Journal of affective disorders
BACKGROUND: Diffusion tensor imaging (DTI) analysis along the perivascular space (ALPS) index is currently widely employed to evaluate the neurophysiological activity in various neuropsychiatric disorders. However, there remains a scarcity of studies...

Fine-scale striatal parcellation using diffusion MRI tractography and graph neural networks.

Medical image analysis
The striatum, a crucial part of the basal ganglia, plays a key role in various brain functions through its interactions with the cortex. The complex structural and functional diversity across subdivisions within the striatum highlights the necessity ...

Deep learning based tractography with TractSeg in patients with hemispherotomy: Evaluation and refinement.

NeuroImage. Clinical
Deep learning-based tractography implicitly learns anatomical prior knowledge that is required to resolve ambiguities inherent in traditional streamline tractography. TractSeg is a particularly widely used example of such an approach. Even though it ...

Assessment of glymphatic function and white matter integrity in children with autism using multi-parametric MRI and machine learning.

European radiology
OBJECTIVES: To assess glymphatic function and white matter integrity in children with autism spectrum disorder (ASD) using multi-parametric MRI, combined with machine learning to evaluate ASD detection performance.