Study: fMRI suggests Alzheimers disease spreads through linked nerve cells
Alzheimer’s disease and other forms of dementia may spread within nerve networks in the brain by moving directly between connected neurons, according to a study published in the March issue of Neuron.
A nerve region’s connectedness to a disease hot spot trumps overall connectedness, spatial proximity and loss of growth-factor support in predicting its vulnerability to the spread of disease in some of the most common forms of dementia, including Alzheimer’s disease, according to William Seeley, MD, of the University of California, San Francisco (UCSF) Memory and Aging Center, and colleagues.
In the study, healthy participants underwent fMRI on a 3T system, and researchers used the data to relate each brain region’s health connectivity profile to that region’s disease-specific vulnerability.
The researchers modeled the normal nerve network that can be affected by Alzheimer’s disease and networks affected by frontotemporal dementia and related disorders.
Seeley and colleagues mapped brain connectedness in 12 healthy people. Then they used data from patients with the five different diseases to map and compare specific regions within the networks that are damaged by the different dementias.
According to Seeley et al, predictions from the transneuronal spread mechanism model best fit the network connectivity maps constructed by the researchers.
“The transneuronal spread model predicts that the more closely connected a region is to the node of disease onset—which we call the epicenter—then the more vulnerable that region will be once the disease begins to spread,” Seeley said in a statement. “It’s as if the disease is emanating from a point of origin, but it can reach any given target faster if there is a stronger connection.”
The scientists tracked and analyzed linkages within nerve networks that the dementias target. They used functional connectivity MRI to measure and spatially represent activity in specific regions of key networks. The MRI readout allowed the researchers to model each region within the network as a distinct but interconnected node. They ranked the nodes that most consistently fired together as being the most closely connected.
The findings of the UCSF researchers are reinforced by a study conducted independently and published in the same edition of the journal by a research team led by Ashish Raj, PhD, from Weill Medical College of Cornell University. The Cornell scientists used diffusion tensor MRI to examine connectedness in affected nerve networks, obtaining similar results.
The finding raises hopes that physicians may be able to use MRI to predict the course of dementias—depending on where within an affected network degenerative damage is first discovered—and that researchers may use these predicted outcomes to determine whether a new treatment is working, according to UCSF.
Network modeling combined with functional MRI might serve as an intermediate biomarker to gauge drug efficacy in clinical trials before behavioral changes become measurable, Seeley said.
“Our next goal is to further develop methods to predict disease progression, using these models to create a template for how disease will progress in the brain of an affected individual,” Seeley said. “Already this work suggests that if we know the wiring diagram in a healthy brain, we can predict where the disease is going to go next. Once we can predict how the network will change over time we can predict how the patient’s behavior will change over time and we can monitor whether a potential therapy is working.”
A nerve region’s connectedness to a disease hot spot trumps overall connectedness, spatial proximity and loss of growth-factor support in predicting its vulnerability to the spread of disease in some of the most common forms of dementia, including Alzheimer’s disease, according to William Seeley, MD, of the University of California, San Francisco (UCSF) Memory and Aging Center, and colleagues.
In the study, healthy participants underwent fMRI on a 3T system, and researchers used the data to relate each brain region’s health connectivity profile to that region’s disease-specific vulnerability.
The researchers modeled the normal nerve network that can be affected by Alzheimer’s disease and networks affected by frontotemporal dementia and related disorders.
Seeley and colleagues mapped brain connectedness in 12 healthy people. Then they used data from patients with the five different diseases to map and compare specific regions within the networks that are damaged by the different dementias.
According to Seeley et al, predictions from the transneuronal spread mechanism model best fit the network connectivity maps constructed by the researchers.
“The transneuronal spread model predicts that the more closely connected a region is to the node of disease onset—which we call the epicenter—then the more vulnerable that region will be once the disease begins to spread,” Seeley said in a statement. “It’s as if the disease is emanating from a point of origin, but it can reach any given target faster if there is a stronger connection.”
The scientists tracked and analyzed linkages within nerve networks that the dementias target. They used functional connectivity MRI to measure and spatially represent activity in specific regions of key networks. The MRI readout allowed the researchers to model each region within the network as a distinct but interconnected node. They ranked the nodes that most consistently fired together as being the most closely connected.
The findings of the UCSF researchers are reinforced by a study conducted independently and published in the same edition of the journal by a research team led by Ashish Raj, PhD, from Weill Medical College of Cornell University. The Cornell scientists used diffusion tensor MRI to examine connectedness in affected nerve networks, obtaining similar results.
The finding raises hopes that physicians may be able to use MRI to predict the course of dementias—depending on where within an affected network degenerative damage is first discovered—and that researchers may use these predicted outcomes to determine whether a new treatment is working, according to UCSF.
Network modeling combined with functional MRI might serve as an intermediate biomarker to gauge drug efficacy in clinical trials before behavioral changes become measurable, Seeley said.
“Our next goal is to further develop methods to predict disease progression, using these models to create a template for how disease will progress in the brain of an affected individual,” Seeley said. “Already this work suggests that if we know the wiring diagram in a healthy brain, we can predict where the disease is going to go next. Once we can predict how the network will change over time we can predict how the patient’s behavior will change over time and we can monitor whether a potential therapy is working.”