A story that grew rapidly from flicker to flame in Alzheimer circles over the past few years has been what role an important brain network might play in the early stages of disease (see ARF related news story). The default mode network, identified by the synchronized activity of its component parts on functional MRI scans, is a target for amyloid deposition (see ARF related news story; ARF news story).

But what about other neurodegenerative diseases? Do they also spread through distributed networks? Could that explain the distinct patterns of pathology in different diseases? The answer appears to be yes, based on new data from William Seeley and colleagues at the University of California, San Francisco, and Michael Greicius at Stanford University. In a paper in the April 16 Neuron, the researchers report how they used MRI to compare the patterns of atrophy and network activity in AD and four other diseases. In each case, the patterns delineated a different functional network. The results paint a picture of the selective vulnerability of different networks to different pathological insults, and suggest that neurodegeneration may propagate via network connections.

To compare different diseases, the researchers analyzed a total of 102 patients with one of five distinct dementia syndromes, including AD, three types of frontotemporal dementia (behavioral variant frontotemporal dementia, semantic dementia, and progressive nonfluent aphasia) and corticobasal syndrome. To better age-match healthy controls, and to focus on the beginnings of disease, the scientists chose young patients who had early-onset disease, and excluded patients with moderate or severe dementia.

Using first structural MRI, the researchers found that the patterns of atrophy for each condition were distinct, as previously described, and corresponded to the different cognitive deficits noted in each patient group. To probe for possible network connections, Seeley and colleagues chose the brain region that showed the greatest atrophy for each condition, and looked for synchronized activity with that seed region during a task-free fMRI scan in healthy controls. They found that each of the five disease-associated regions anchored a different functionally connected network. Moreover, in each case the network activity mapped closely to the distribution of atrophy observed in the analysis of diseased brains.

The functional linkage of brain regions was mirrored in structural measures, as well. Analyses of gray matter intensity across the whole brain in both normal and disease subjects revealed that the functionally linked regions also showed a high correlation of gray matter volume. As Seeley explained to ARF, “This suggests that regions that fire together, scale together. They grow and shrink together, although we don’t know exactly how that works.” Adding a structural correlation to the functional measures may be a useful approach to define networks in general, Seeley says. This is similar to previous work that used correlations of cortical thickness to map the default network (He et al., 2008 and ARF related news story).

The results support the “network degeneration hypothesis,” which holds that disease starts in small neuron populations and progressively spreads to connected areas. “The point is that once a disease gets going at a given location in the brain, the whole network or regions that are interconnected with that region are at risk, and the disease is likely to propel itself down that set of pathways somehow or the other,” Seeley explained. Because the study looks at different clinical syndromes that can be caused by a number of pathogenic proteins, this ability to propagate through networks is likely to be relevant to numerous disease proteins, including β amyloid, tau, α-synuclein, and TDP43. How the proteins might spread their damage through networks is not clear, but there are several possibilities. Pathogenic proteins like Aβ are known to disrupt synaptic connections directly, and this could weaken networks. Some disease-causing proteins disrupt axonal transport, possibly leading to a severing of connections between regions. Some might even propagate misfolding by direct transfer from one neuron to another (e.g., see Frost et al., 2009 and ARF related news story). These potential scenarios are not mutually exclusive, the authors point out. And the extensive overlap between neurodegenerative diseases, for example, AD and dementia with Lewy bodies (DLB), both clinically and pathologically, is likely to complicate this new line of research as it digs deeper.

The network map for AD agrees with previous work showing involvement of the default-mode network, but the other diseases had their own distinct patterns. That suggests that the network signatures might eventually find use clinically to diagnose early stage or even presymptomatic disease, when there are still neurons to save. “The nice thing about network analyses is that they don’t rely on atrophy,” Seeley said. “We could potentially see the disease signature before brain regions start to shrink and wither.” The measure might also serve as an objective biological endpoint to monitor disease progression and treatment. Researchers including Greicius and Randy Buckner are already working on developing these methods for AD, and the new data should spur studies of similar approaches for other diseases.

An outstanding question remains as to why different diseases, and presumably different pathogenic proteins, have a favorite network to attack. “Each disease, defined at the protein level, has a set of neurons somewhere in the brain that are most vulnerable to that protein’s misfolding. That’s the concept of selective vulnerability, and understanding what brings these problems to these cells is critical to understanding neurodegenerative disease,” Seeley said.

To that end, Seeley says he is glad to see the concepts of network biology becoming more interwoven into the fabric of neurodegeneration research. “So much of the basic research on neurodegeneration focuses on single cells and molecules, but the tide is turning on that a little bit. We’re seeing a new wave of network-oriented basic and translational neuroscience to try to understand what it is about network physiology, or gene expression, or development that makes these systems disease vulnerable. I’m excited to see that pick up.” The importance of pushing forward with explorations of network structure and function in the human brain was echoed in an accompanying commentary by Marsel Mesulam of Northwestern University in Chicago, Illinois, who pioneered anatomical study of networks important for behavior and cognition.—Pat McCaffrey.

References:
Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative Diseases Target Large-Scale Human Brain Networks. Neuron. 2009 April 16; 62:42-52. Abstract

Mesulam M. Defining Neurocognitive Networks in the BOLD New World of Computed Connectivity. Neuron. 2009 April 16; 62:1-3. Abstract

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  1. This can be regarded as a milestone in the search for conceptual patterns underlying different types of neurodegenerative disorders. In a single study, this group addressed a whole number of questions. The authors were able to demonstrate that the patterns of cerebral atrophy typically found in different neurodegenerative disorders indeed follow the pathways of pre-existing functional intrinsic connectivity networks (ICNs), which can be identified in healthy subjects. This work is all the more impressive as the authors actually used the foci of maximum atrophy detected in the different neurodegenerative disorders as a seed region for identification of the functional ICNs in healthy subjects (i.e., they identified regions in the brain which are functionally interrelated with this seed region). The similarity of the ICNs as identified in healthy subjects by this approach with the pattern of atrophy as detected in patients is striking.

    These results do indeed strongly support the so-called network degeneration hypothesis, which implies that different types of neurodegeneration follow distinct patterns of functionally associated neuronal populations in the brain. This notion has existed for a long time and is even implicitly found in terms describing neurodegenerative disorders (e.g., “system/multisystem degeneration”). However, in-vivo proof for this hypothesis so far has been sparse.

    Furthermore, it is highly remarkable that the authors were able to detect network-associated atrophy patterns in different groups of neurodegeneration including Alzheimer disease and syndromes belonging to the frontotemporal lobar degenerative disorders (such as semantic dementia or frontotemporal dementia), because these disorders are typically based on different types of underlying causal pathologies (i.e., β amyloid, tau- or TDP-43 aggregation pathology). Another important finding is the detected interrelation between structure and function in healthy subjects, as demonstrated by the observed overlap between the ICNs and structural covariance networks (SCNs).

    Although a number of questions are answered by the current work, it immediately raises new questions: For most neurodegenerative disorders (except for Alzheimer’s), it is not known yet if changes of functional connectivity do actually occur and, if so, if the atrophy in a specific network results in a change of functional connectivity within this network or vice versa. Possibly, it will also remain difficult to rule that measurements of functional connectivity are affected by regional atrophy (i.e., no activity can be measured where no tissue is present). Other issues to be addressed are the relation of white matter changes to the observed phenomena and the influence of developmental factors.

    Furthermore, it remains to be clarified why the mentioned networks show specific susceptibility to basic underlying pathologies, also, why identical causal pathologies may result in different patterns of atrophy/neuronal dysfunction in different people, or why different causal pathologies may result in similar patterns of atrophy/dysfunction. In conclusion, this study will definitely stimulate further research in this direction and will serve as an important basis for subsequent experiments.

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References

News Citations

  1. Network Diagnostics: "Default-Mode" Brain Areas Identify Early AD
  2. Tracing Alzheimer Disease Back to Source
  3. Cortical Hubs Found Capped With Amyloid
  4. Changes In Cortical Thickness Mirror Loss of Network Connectivity in AD
  5. Keystone: Tau, Huntingtin—Do Prion-like Properties Play a Role in Disease?

Paper Citations

  1. . Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J Neurosci. 2008 Apr 30;28(18):4756-66. PubMed.
  2. . Conformational diversity of wild-type Tau fibrils specified by templated conformation change. J Biol Chem. 2009 Feb 6;284(6):3546-51. PubMed.
  3. . Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009 Apr 16;62(1):42-52. PubMed.
  4. . Defining neurocognitive networks in the BOLD new world of computed connectivity. Neuron. 2009 Apr 16;62(1):1-3. PubMed.

Further Reading

Papers

  1. . Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009 Apr 16;62(1):42-52. PubMed.
  2. . Defining neurocognitive networks in the BOLD new world of computed connectivity. Neuron. 2009 Apr 16;62(1):1-3. PubMed.

News

  1. ApoE4 Linked to Default Network Differences in Young Adults
  2. Network Diagnostics: "Default-Mode" Brain Areas Identify Early AD
  3. Keystone: Tau, Huntingtin—Do Prion-like Properties Play a Role in Disease?
  4. Cortical Hubs Found Capped With Amyloid
  5. Changes In Cortical Thickness Mirror Loss of Network Connectivity in AD
  6. Tracing Alzheimer Disease Back to Source

Primary Papers

  1. . Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009 Apr 16;62(1):42-52. PubMed.
  2. . Defining neurocognitive networks in the BOLD new world of computed connectivity. Neuron. 2009 Apr 16;62(1):1-3. PubMed.