Defining neurocognitive networks in the BOLD new world of computed connectivity.
Neuron. 2009 Apr 16;62(1):1-3.
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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.