Background:
Alzheimer’s disease (AD) is a debilitating condition that gradually robs patients of control over their memory, body and life.
The entorhinal cortex and hippocampus are often some of the first regions that suffer damage from onset of Alzheimer’s disease, characterized by short-term memory loss and disorientation.
However, changes in the brain may begin a decade or more before symptoms appear. During this very early stage of Alzheimer’s, abnormal buildups of proteins that form amyloid plaques and tau tangles gradually accumulate and spread. Massive efforts from scientists and doctors all over the world have been mounted to understand the earliest stages of disease onset, with the hope that understanding the mechanisms that start this gradual process will yield to potential interventions or future treatments.
Patterns of amyloid-β and tau accumulation correlate with cognitive decline and altered structural and functional network organization. Synapse loss is among the best predictors of cognitive decline in Alzheimer’s disease. However, the cellular mechanisms by which the neurotoxic protein aggregates initiate synapse loss at the earliest stages of AD pathology onset have not been elucidated. Recent literature suggest that amyloid-β induces neuronal hyperactivity that triggers synaptic failure. (Hector & Brouillette, Front Mol Neurosci, 2020)
This functional hallmark of early disease opens up an interesting question:
Can the study of functional connectomics provide novel insights into the neurobiological mechanisms of Alzheimer’s disease?
Network failure in Alzheimer’s disease
Functional connectomics describes a field of inquiry that studies the neuronal activity network of the brain, for example in health and disease conditionm, during experimental perturbation, or when performing behavioral or cognitive task.
More generally, connectomics tries to map meaningful relationships between different regions of the brain. These relationships can be structural, functional or based on effect:
In MRI studies, nodes are defined by brain parcellations that are based on cytoarchitectonics, anatomical landmarks or connectivity patterns. Links can be defined by four types of brain connectivity: structural connectivity, structural co-variance, functional connectivity and effective connectivity (i.e directed information flow) (Meichen Y. et al., Nat Rev Neurol., 2021)
Many neurodegenerative conditions can alter the functional connectome of the brain when observed at this macroscale.
Patients with Alzheimer’s disease exhibit distinct alterations in network connectivity when compared to control group patients. For example, AD networks are characterized by a loss of small-world features (Stam CJ., Nat. Rev. Neurosci, 2014) and a disturbed modular structure (Yu M et al., Neurobiol. Aging, 2016). These changes often happen before visible structural changes to the brain occur, such as shrinking.
The neuropathology of Aβ plaques and tau neurofibrillary tangles causes progressive neuronal loss and shrinkage together with synaptic impairment in specific cortical and subcortical brain regions, suggesting that AD can be described as a ‘disconnection syndrome’ — Delbeuck X. et al., Neurpsychol Rev. 2003
But how does this disconnection come about? Macroscopic MRI connectomic studies, like many top-down approaches, have a limited resolution and can not help infer neuronal activity alterations and network connectivity at the micro- and mesoscale (up to thousands of neurons).
Yet to drill down at the biological mechanism of Alzheimer’s disease onset, the problem has to be understood from the ground up.
Untangling the “disconnection syndrome”
A syndrome is a set of medical signs and symptoms which are correlated with each other and often associated with a particular disease or disorder. — The British Medical Association Illustrated Medical Dictionary. 2002
Alzheimer’s disease is incredibly difficult to study at the molecular level because it is a slow, progressive disease that develops into complex physiological phenotypes over decades through the gradual reshaping of neurobiology. Mechanistic insights of functional connectivity patterns in AD would rely heavily computational modeling and inferences, not biological model sytems (van Nifterick AM. et al., Alzheimer’s Research & Therapy, 2022)
This is because the decline of connectivity of functional networks in Alzheimer disease models can only be assessed when recording neuronal activity of thousands of neurons over time, which was technically unfeasible in vitro until very recently.
Attempts to find cures for AD and neurodegenerative disorders are hampered both by the elusive causes of disease hallmarks and by the relative paucity of robust and sensitive assay systems for testing therapeutic strategies — Amin H. et al., Scientific reports, 2017
Some encouraging early work has been performed a few years ago by the Berdondini lab at the Italian Institute of Technology, who used multi-electrode arrays to assess functional rescue for a stem-cell based treatment against amyloid-β neurotoxicity (Amin H. et al., Scientific reports, 2017)
However, 2D models of AD are inherently limited by a lack of physiological relevance. We have previously written about the complexity barrier in brain research and how there is a need for novel 3D in vitro model systems and measurement technologies to tackle some of that complexity underlying neurodegenerative diseases.
With the advance of patient-derived organoid systems to models Alzheimer’s disease, new animal models and organotypic tissue slices, as well as new organ-on-chip technologies to probe them, researchers are currently worked hard to close that gap. (often with the additional benefit of using less animal experimentation)
Model systems for Alzheimer’s disease. (a) Animal models. Genetically engineered animal models that express AD-related genes. Two most popular animal models for AD are rodents and nonhuman primates. (b) 2D model systems. Mono-culture on a petri dish, co-culture, or tri-culture of brain cells or a trans-well insert using primary cells from human patients or induced pluripotent stem cells (iPSCs) derived brain cells. (c) Brain organoid models and brain organ on a chip model. (d) Comparison of animal models, 2D models, and organoids models. −, none; +, low; ++, medium; +++, high (Sreenivasamurthy S. et al., Bioengineering & Translational Medicine, 2022)
As research to unravel AD mechanisms continues to surge, cerebral organoids have been gaining popularity as a valuable platform to model this disease. […] They are highly sought after in modern research and are likely to mimic human brain pathology and disease pathways more accurately and ethically. — Sreenivasamurthy S. et al., Bioengineering & Translational Medicine, 2022
One remaining major bottleneck to tackle the disconnection syndrome of Alzheimer’s disease has been the limitation to perform functional connectomics in these new 3D model systems. (Disclaimer: 3Brain is currently producing a first-in-class 3D microchip able to contribute to this specific issue, but more on that another time)
Progress is often not linear, but follows a s-curve of growth, innovation and maturity. Current developments in engineering, disease modeling and computational modeling provide a bright outlook for producing more conclusive, in-depth studies for AD and to finally uncover it’s neurobiological roots.
Conclusion
Alzheimer’s disease is a sickness that starts silently, by losing connections. It is an almost uniquely isolating condition on every level, a disconnection syndrome.
Alzheimer patient’s symptoms are currently treated by strengthening connections, through facilitating cognitive, behavioral and social stimulation.
Researchers and doctors currently believe that understanding the functional activity patterns at the earliest stages of disease initiation and protein aggregation might offer a window of therapeutic opportunity.
A chance to counter losing connections in the first place.
References:
Delbeuck X. et al., Neurpsychol Rev., 2003
Smith SM et al., Trends Cogn Sci., 2013
Stam CJ., Nat. Rev. Neurosci, 2014
Yu M et al., Neurobiol. Aging, 2016
Amin H. et al., Scientific reports, 2017
Hector & Brouillette, Front Mol Neurosci, 2020
Meichen Y. et al., Nat Rev Neurol., 2021
van Nifterick AM. et al., Alzheimer’s Research & Therapy, 2022
Sreenivasamurthy S. et al., Bioengineering & Translational Medicine, 2022
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Declaration of interest:
The author is an employee at 3Brain.
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