Exploring Dementia in 3D: Techniques and Applications

By Samudrapom DamReviewed by Lily Ramsey, LLM

Dementia refers to progressive neurodegenerative conditions that cause a loss of cognitive functioning/decline in cognitive functions like remembering, thinking, reasoning, and decision-making.¹

Image Credit: Ws Studio1985/Shutterstock.com

Dementia progresses through varying degrees of severity, from mildest stages with subtle functional impacts to severe stages necessitating complete dependence on others for essential daily tasks, which significantly impact the quality of life of affected individuals.¹

Conventional two-dimensional (2D) approaches like microscopy and imaging cannot fully capture the brain's structural and functional complexity. They only provide 2D views, missing the complex three-dimensional (3D) relationships between neurons and regions.

This limits the ability to precisely model the brain's dynamic processes and interactions, hindering a thorough understanding of neurological conditions like dementia.²

3D techniques like 3D brain imaging and cell cultures provide a more holistic view of the brain, allowing researchers to study its intricate architecture and the spatial relationships between brain regions.^2,3

These methods enable the visualization of how Dementia affects brain structures in three dimensions, enhancing the understanding of disease progression from early stages. By incorporating spatial and functional data, 3D approaches offer a more accurate, dynamic perspective of dementia pathology.^2,3

The Future of 3D Cell Cultures in Biomedical Research

Key 3D Techniques Used in Dementia Research

Advanced 3D techniques have revolutionized dementia research, enabling more accurate models of brain changes and pathology. Techniques like 3D brain imaging, cell cultures, microscopy, and bioprinting offer innovative approaches to explore neurodegeneration and assess potential treatments.^4-10

3D Brain Imaging

Neuroimaging techniques like magnetic resonance imaging (MRI) have emerged as promising biomarkers in Alzheimer's disease’s preclinical stages. MRI generates high-quality 3D brain structure images without requiring radioactive tracers/X-rays. This technology enabled the development of a diagnostic model for Dementia by offering a non-invasive method to detect brain atrophy patterns indicative of the disease.⁴

Amyloid positron emission tomography (PET) imaging advancements provided additional insights into Alzheimer's disease pathophysiology. Amyloid PET scans offer a key biomarker to evaluate cognitive impairment by revealing amyloid plaques within the brain that were previously only identifiable through autopsy. These scans use radiotracers to visualize brain activity, with FDG-PET, AV45-PET, and PiB-PET being the primary variants used in diagnosis.⁴

The FDG-PET evaluates glucose metabolism in the brain, PiB-PET employs Pittsburgh compound B for amyloid binding, and AV45-PET utilizes florbetapir for amyloid. The PET scan classifications into glucose and amyloid PETs indicate their diagnostic utility, with amyloid PETs displaying higher sensitivity for diagnosis.⁴

In computed tomography (CT) scans, several X-rays obtained from various angles of the head are combined to generate brain images. CT scans are commonly used in dementia diagnosis. For instance, studies have validated perfusion CT as a reliable imaging modality for early dementia diagnosis and in differentiating vascular Dementia from Alzheimer's disease.⁵

3D Cell Cultures and Organoids

These techniques replicate the human brain's complex structure and function, providing a more accurate model for studying Dementia. These models allow researchers to better mimic disease pathology, enhancing the ability to test potential treatments and understand neurodegenerative processes.^6,7

For instance, the limitations of animal models and Alzheimer’s disease induced pluripotent stem cells (iPSCs)-derived neurons, including lack of robust extracellular Aβ plaques, could be addressed by developing a 3D human neuronal culture model of the disease.⁶

The model is developed by combining matrigel-based 3D culture technology and genetically engineered human neuronal progenitor cells. 3D neural cell culture systems are also suitable for recapitulating in vivo brain environments and could accelerate neural network formation and neuronal differentiation.⁶

Self-organizing structures like cerebral organoids represent another approach to viewing 3D structures that facilitate interstitial compartments for Aβ deposition. These neuro-spheroids/brain organoid models more closely mimic brain structures affected in Alzheimer’s disease patients’ brains.⁷

3D Microscopy and Clearing Techniques

3D microscopy and clearing techniques, such as Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging compatible Tissue hYdrogel (CLARITY) and light-sheet microscopy, offer groundbreaking methods for visualizing brain tissues in high resolution. These innovations enable researchers to explore complex brain structures in 3D, enhancing our understanding of dementia pathology.^8,9

A recent work published in NeuroImage developed a histological method based on optical 3D imaging/microscopy combined with a tissue clearing method CLARITY on human brain tissue, adapted for histology using MRI (hMRI) validation.

Researchers showed the feasibility of using this approach for neuron density quantification, cell type classification, and fiber orientation distribution within an MRI voxel-sized volume.⁸

Light sheet fluorescence microscopy (LSFM) is an optical sectioning technique allowing volumetric imaging over several length scales while minimizing phototoxicity and photobleaching and retaining high spatial resolution. Thus, morphological images of entire cleared mammalian brains can be acquired with sub-cellular resolution using LSFM. Additionally, the use of propagation invariant beams allows imaging in turbid neural tissues.⁹

3D Bioprinting

This approach enables the creation of brain-like structures that closely mimic the human brain for studying neurodegeneration. Bioprinting combines biomaterials and cells to generate 3D structures that replicate the native tissue environment and are utilized as a disease modeling/drug screening tool. Thus, 3D bioprinting technology allows researchers to develop precise, customizable models to test therapies and gain deeper insights into dementia pathology.¹⁰

A work published in Bioelectronic Medicine differentiated both diseased and healthy patient-derived human iPSCs (hiPSCs) into neural progenitor cells that were bioprinted into dome-shaped constructs using the Aspect RX1 microfluidic printer.

Bioprinted tissue models incorporating patient-derived hiPSCs were developed successfully. The models could screen promising drug candidates for dementia/Alzheimer’s disease treatment.¹⁰

Creating Molecules Using 3D Printing

Applications of 3D Techniques in Dementia Research

In dementia research, 3D approaches like 3D bioprinting and brain imaging enhance brain structure modeling. They improve drug testing and therapy development by closely mimicking the brain environment. Additionally, 3D techniques enable higher-resolution imaging and deeper insights into cellular interactions in neurodegenerative conditions.^4-10

3D techniques like perfusion CT imaging and 3D culture models enhance the understanding of the disease mechanisms, improving drug discovery and enhancing diagnostic capabilities. By providing a more accurate representation of brain structure and function, these methods help researchers uncover the underlying processes of neurodegeneration.^4-10

In drug discovery and testing, 3D models allow for better predictive models of drug efficacy and disease progression, enabling more reliable testing of potential treatments. For instance, 3D human neural cell culture models of Alzheimer's disease are used as a drug screening platform to accelerate drug discovery.^4-10

Techniques like 3D MRI aid in early detection and diagnostics as they could reveal subtle brain changes preceding clinical symptoms. The integration of advanced imaging like 3D microscopy with clearing techniques allows for a comprehensive approach to studying Dementia, while 3D bioprinting and culture models provide deeper insights into the disease and facilitate the development of personalized therapies and early intervention strategies.^4-10

Challenges and Future Directions

3D technologies face challenges related to complexity, cost, and scalability, limiting their widespread use. However, ongoing efforts aim to combine 3D imaging methods with artificial intelligence (AI) and machine learning, specifically deep learning, to enhance data analysis and improve predictions.^4,5,11

For instance, AI techniques like convolutional neural networks (CNN), deep CNN (DCNN), 3D CNN (3D-CNN), support vector machines (SVM), transfer learning, and 3D VGG16 have been used with MRI and PET. The future potential of these advancements includes the creation of personalized 3D brain models for precision medicine, offering more accurate and individualized approaches to diagnosis, treatment, and disease management in Dementia.^4,5,11

Conclusion

In conclusion, 3D techniques are transforming dementia research by providing more accurate models of brain structure and function, improving diagnostics, and enabling more reliable drug testing.

These advancements allow for a deeper understanding of disease progression, offering insights into the underlying mechanisms of neurodegeneration. The future impact on healthcare and neuroscience includes the development of more effective early intervention strategies, paving the way for precision medicine in dementia care.

Applications of 3D printing in Drug Delivery

References

1. What Is Dementia? Symptoms, Types, and Diagnosis [Online] Available at https://www.nia.nih.gov/health/alzheimers-and-dementia/what-dementia-symptoms-types-and-diagnosis (Accessed on 30 December 2024)
2. Avesta, A., Hossain, S., Lin, M., Aboian, M., Krumholz, H. M., Aneja, S. (2023). Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation. Bioengineering, 10(2), 181. DOI: 10.3390/bioengineering10020181, https://www.mdpi.com/2306-5354/10/2/181
3. 3D imaging helps to better understand the early stages of Alzheimer’s disease [Online] Available at https://news.ki.se/3d-imaging-helps-to-better-understand-the-early-stages-of-alzheimers-disease (Accessed on 30 December 2024)
4. Castellano, G., Esposito, A., Lella, E., Montanaro, G., & Vessio, G. (2024). Automated detection of Alzheimer’s disease: A multi-modal approach with 3D MRI and amyloid PET. Scientific Reports, 14(1), 1-10. DOI: 10.1038/s41598-024-56001-9, https://www.nature.com/articles/s41598-024-56001-9
5. Dash, S., Agarwal, Y., Jain, S., Sharma, A., & Chaudhry, N. (2023). Perfusion CT imaging as a diagnostic and prognostic tool for Dementia: prospective case–control study. Postgraduate Medical Journal, 99(1170), 318-325. DOI: 10.1136/postgradmedj-2021-141264, https://academic.oup.com/pmj/article/99/1170/318/7177417
6. Choi, S. H., Kim, Y. H., Quinti, L., Tanzi, R. E., Kim, D. Y. (2016). 3D culture models of Alzheimer’s disease: a road map to a “cure-in-a-dish”. Molecular Neurodegeneration, 11, 1-11. DOI: 10.1186/s13024-016-0139-7, https://link.springer.com/article/10.1186/s13024-016-0139-7
7. Sreenivasamurthy, S., Laul, M., Zhao, N., Kim, T., & Zhu, D. (2023). Current progress of cerebral organoids for modeling Alzheimer's disease origins and mechanisms. Bioengineering & Translational Medicine, 8(2), e10378. DOI: 10.1002/btm2.10378, https://aiche.onlinelibrary.wiley.com/doi/full/10.1002/btm2.10378
8. Morawski, M. et al. (2018). Developing 3D microscopy with CLARITY on human brain tissue: Towards a tool for informing and validating MRI-based histology. NeuroImage, 182, 417-428. DOI: 10.1016/j.neuroimage.2017.11.060, https://www.sciencedirect.com/science/article/pii/S1053811917310066
9. Corsetti, S., Gunn-Moore, F., & Dholakia, K. (2019). Light sheet fluorescence microscopy for neuroscience. Journal of Neuroscience Methods, 319, 16-27. DOI: 10.1016/j.jneumeth.2018.07.011, https://www.sciencedirect.com/science/article/abs/pii/S0165027018302188
10. Benwood, C., Walters-Shumka, J., Scheck, K., Willerth, S. M. (2023). 3D bioprinting patient-derived induced pluripotent stem cell models of Alzheimer’s disease using a smart bioink. Bioelectronic Medicine, 9(1), 10. DOI: 10.1186/s42234-023-00112-7, https://link.springer.com/article/10.1186/s42234-023-00112-7
11. Awang, M. K., Ali, G., & Faheem, M. (2024). Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review. Health Science Reports, 7(9), e70025. DOI: 10.1002/hsr2.70025, https://onlinelibrary.wiley.com/doi/full/10.1002/hsr2.70025

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