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Machine Learning-based Biological Ageing Estimation Technologies: A Survey
Published in Richard Jiang, Li Zhang, Hua-Liang Wei, Danny Crookes, Paul Chazot, Recent Advances in AI-enabled Automated Medical Diagnosis, 2022
Zhaonian Zhang, Richard Jiang, Danny Crookes, Paul Chazot
Brain Age (BrA) is also a very important kind of BA. The aging brain functions decline and neurodegenerative diseases bring increasingly serious economic, old-age, medical, and other social problems to our society. Therefore, it is an important task for researchers to accurately and quickly predict the BrA of subjects. Although brain aging is a natural process, there are significant individual differences in changes in brain volume, cortical thickness and white matter microstructure during this process. In addition, the deviation degree between the individual brain aging trajectory and the average trajectory of healthy brain aging can reflect the individual’s future risk of neurodegenerative diseases [7, 15]. Therefore, building models based on the characteristic patterns of brain aging contained in neuroimaging data and detecting the aging trajectories of individual brains can provide a new perspective for studying individual differences in brain aging.
MR diffusion tensor imaging
Published in João Manuel, R. S. Tavares, R. M. Natal Jorge, Computational Modelling of Objects Represented in Images, 2018
MR Diffusion Tensor Imaging is a very promising and powerful technique to estimate white matter fiber trajectories in vivo. It is a technique that is highly dependent on complex algorithms and heavy computing power. It has many applications in medicine, both in the fundamental and clinical aspects, like establishing the fiber connections between different eloquent parts of the brain, determining whether a tumour has displaced the fibers within a certain area of the brain or is encapsulating them, or determining if particular fiber connections have been destroyed.
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Published in Mara Cercignani, Nicholas G. Dowell, Paul S. Tofts, Quantitative MRI of the Brain: Principles of Physical Measurement, 2018
Yeatman et al. (Yeatman et al., 2014) modelled white matter development and ageing over an 80-year period of the lifespan (N=102, ages 7–85). The 1/T1 and MTV (1-WC) growth curves were well fitted by a symmetric curve such as a second-order polynomial over the measured period of the lifespan. This implies that the rate of growth and decline are symmetric. MTV values reach their peak between 30 and 50 years of age and then decline, returning to their 8-year-old levels between age 70 and 80. Interestingly different white matter pathways show different rates of change as a function of age.
Comparison analysis of local angular interpolation methods in diffusion MRI
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Ines Ben Alaya, Majdi Jribi, Faouzi Ghorbel, Tarek Kraiem
Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is the only non-invasive imaging modality for studying the neuronal architecture of brain white matter, development and diseases. It is sensitive to the Brownian motion of water molecules in living tissues. In fact, in cerebral white matter, the water will preferentially diffuse more rapidly in the direction of fibres than in perpendicular directions (Beaulieu 2002). DW-MRI is widely applied in neurological applications (Abhinav et al. 2014a, 2014b; Wang et al. 2015). It’s well known that in diffusion MRI, fibre crossing is an important problem for the most existing Diffusion Tensor Imaging (DTI). To overcome these limitations, we have to increase the angular or spatial resolution for capturing more information about the brain structural connectivity (Tuch et al. 2003).
Effect of axonal fiber architecture on mechanical heterogeneity of the white matter—a statistical micromechanical model
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Hesam Hoursan, Farzam Farahmand, Mohammad Taghi Ahmadian
Human brain white matter consists of axonal bundles which connect nerve cell bodies mostly located in the grey matter. A sudden inertial loading on the head can cause Diffuse Axonal Injury (DAI) of white matter, which involves axonal damage in a variety of modes. Among the failure modes of axons, rapid stretching of neural tracts, leading to the impairment of axoplasmic transport and subsequent swelling and neuropathologic problems, has been reported to be the prevailing failure mode (McKenzie et al. 1996; Smith and Meaney 2000; Di Pietro 2013). DAI tends to occur in three anatomical regions of white matter, known as the “injury triad”: the lobar white matter (including corona radiata), the corpus callosum, and the dorsolateral quadrant of the rostal brainstem, adjacent to the superior cerebellar peduncle (Tsao 2012).
A Functional BCI Model by the P2731 working group: Physiology
Published in Brain-Computer Interfaces, 2021
Ali Hossaini, Davide Valeriani, Chang S. Nam, Raffaele Ferrante, Mufti Mahmud
Neurons do not form simple circuits. Instead axons and dendrites branch, which connects a single neuron to many others. The general term for a neural extension is neurite, and neurites range from a few microns to a meter in length. The complexity of neural networks [31] accounts for the brain’s astonishing computational power and the difficulty of describing it in unambiguous terms. A human brain contains approximately 86 billion neurons (20% of which are in the cerebral cortex), and each one participates in multiple processes [38]. Whether in the brain or body, the presence of a myelin sheath offers a way of recognizing neurons. Because cortical neurons are unmyelinated, the outer surface of the brain is gray. Immediately below the gray matter of the cerebral cortex lies white matter, a mass of neurons with myelinated axons that efficiently connect the cerebral cortex with distant regions throughout the brain. We should note that neurons are not the only actively communicating cells in the brain. Glial cells were once considered mere scaffolds, but in recent years researchers have discovered cross-talk between astrocytes and oligodendrocytes [39], and, somewhat surprisingly, oligodendrocytes sometimes form synapses with neurons [34]. Much remains to be discovered about the functional roles of glial cells.