EDITORIAL
Network signatures of age-related cognitive decline
Robert D. Stevens, MD Yousef Hannawi, MD
Correspondence to Dr. Stevens: [email protected] Neurology® 2015;85:16–17
Amyloid pathology and cerebrovascular disease are the most frequent, and best characterized, mechanisms linked to age-related cognitive decline and dementia. The relationship between these mechanisms and phenotypes of normal aging, mild cognitive impairment, and dementia has recently been explored in vivo with neuroimaging markers of small vessel disease (SVD) and amyloid deposition (respectively, white matter hyperintensities [WMH] identified on T2-weighted or fluid-attenuated inversion recovery [FLAIR] MRI, and binding of the b-amyloid selective radiotracer Pittsburgh compound B [PiB] on PET). These techniques have accelerated understanding of disease burdens in preclinical and clinical states of impaired cognition. However, a plausible systems-level model that accounts for specific disease pathologies and downstream changes in cerebral integrative function and capacity is lacking. Both amyloid pathology and SVD are widely distributed processes that disrupt multiple neuronal systems, hence are poorly suited to conventional lesion-based analysis. An alternative approach is to consider the consequences of these diseases on the brain’s large-scale structural and functional network architecture. Diffusion tensor imaging (DTI) reveals multifocal microstructural defects affecting white matter tracts in patients with Alzheimer disease (AD),1,2 SVD,3 and in mixed syndromes.2 Studies using resting functional MRI indicate a breakdown in network functional connectivity in AD4 and SVD.5 But how best to analyze and represent brain network properties? This is generally accomplished with graph analysis, a statistical methodology that models real-world systems in terms of nodes (e.g., cortical sites) that are connected by edges (e.g., white matter tracts and bundles). Graph analysis is emerging as the preeminent paradigm in the neuroscience of networks.6 In this issue of Neurology®, Kim et al.7 hypothesize that disruptions in white matter connectivity are a fundamental mechanism linking b-amyloid deposition, or SVD, to cortical atrophy and impairments in cognition. The authors studied 232 patients with
cognitive impairment who were classified according to well-established criteria as amnestic mild cognitive impairment, subcortical vascular mild cognitive impairment, subcortical vascular dementia, and AD. Participants underwent structural brain MRI (including FLAIR, DTI, and cortical thickness measurements), PiB imaging, and neuropsychological testing. Nodes were defined by anatomical parcellation of the cerebral cortex into 78 regions of interest, while edges were identified as the mean DTI-derived fractional anisotropy along white matter tracts connecting 2 nodes. Network connectivity was expressed as “nodal efficiency,” a graph analytical descriptor that is the reciprocal of the shortest path length between a node and all other nodes in the network. The authors used path analysis, a model-driven multivariable technique, to characterize the relationships among WMH, PiB retention, nodal efficiency, cortical thickness, and cognitive performance. They found that WMH volume was linked to reduced nodal efficiency, decreased frontal and temporoparietal cortical thickness, and reduced performance on tests of executive and memory function. However, PiB-defined amyloid burden was associated with temporoparietal cortical thickness and with executive and memory scores, but these effects were completely independent of any changes in nodal efficiency. The authors infer, tentatively, that vascular cognitive impairment is akin to a “connectopathy,” while such a conclusion may not hold in diseases associated with cerebral amyloid pathology. The study by Kim et al. is an elegant investigation of putative relationships among markers of tissue damage, network disconnection, cortical atrophy, and cognitive impairment. Results of this multiscale, multiparametric characterization in a clinically defined population suggest limitations inherent to current phenotypically based diagnostic schemes, which are not congruent with underlying biological abnormalities. The data extend observations made in another report from the same group,8 and align with findings in other recent studies.1–3,9,10 Graph analysis of DTI-defined white matter tracts in patients with early AD indicates a
See page 63 From the Departments of Anesthesiology and Critical Care Medicine (R.D.S., Y.H.), Neurology (R.D.S., Y.H.), Neurosurgery (R.D.S.), and Radiology (R.D.S.), Johns Hopkins University School of Medicine, Baltimore, MD. Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the editorial. 16
© 2015 American Academy of Neurology
ª 2015 American Academy of Neurology. Unauthorized reproduction of this article is prohibited.
reduction in local network efficiency that is linked to worse performance on cognitive testing.1 Similarly, a correlation has been found among WMH burden, DTI-based local and global measures of network efficiency, and cognitive scores in patients with SVD.3 In a study of elderly patients with varying levels of cognitive impairment, WMH burden was associated with damage in projection and association white matter tracts, whereas PiB retention correlated with loss of integrity in the fornix and splenium of the corpus callosum.10 Another study found no relation between PiB-defined amyloid burdens and DTI evidence of white matter degeneration.9 Regarding the study by Kim et al., several caveats need to be considered, most conspicuous of which is disentangling causation vs correlation. As the authors acknowledge, their results cannot attest to a causative sequence of neurobiological events; they highlight a set of associations whose true magnitude and directionality require further investigation. The study lacks an age-matched control group to establish the importance of abnormalities detected in the diseased populations. Additional analysis could have explored the potentially synergistic interaction between vascular and amyloid-associated etiologies of cognitive decline. The anatomical parcellation did not incorporate subcortical gray matter nodes (e.g., thalamus, basal ganglia), suggesting that the network analysis may have been truncated; inclusion of thalamic nuclei, which are critical relays in memory and in executive circuits, may have yielded different results. Finally, the use of nodal efficiency as the sole topological network descriptor is reductive; confirmation of nodal efficiency findings with other graph analytical variables (e.g., centrality, modularity, clustering coefficient) could increase the robustness of the reported results. Overall, this work evokes fundamental questions that deserve further exploration. Is the cortical atrophy in vascular cognitive impairment and amyloid disease the cause, or the result, of degeneration observed in white matter tracts? What is the cellular basis for these imaging abnormalities? How do amyloid deposition and vascular mechanisms synergize to alter network connectivity? What is the relationship between connectopathies identified using anatomical vs functional techniques? Can therapeutic interventions be designed to reverse abnormalities
in node organization or between-node connectivity? Answering these questions could substantially advance the recognition and treatment of agerelated disorders of cognition. AUTHOR CONTRIBUTIONS Dr. Stevens: data interpretation and analysis, drafting the manuscript and editing it for intellectual content. Dr. Hannawi: data interpretation and analysis, critically reviewing the manuscript for its intellectual content.
STUDY FUNDING No targeted funding reported.
DISCLOSURE The authors report no disclosures relevant to the manuscript. Go to Neurology.org for full disclosures.
REFERENCES 1. Reijmer YD, Leemans A, Caeyenberghs K, Heringa SM, Koek HL, Biessels GJ. Disruption of cerebral networks and cognitive impairment in Alzheimer disease. Neurology 2013;80:1370–1377. 2. Lee DY, Fletcher E, Martinez O, et al. Regional pattern of white matter microstructural changes in normal aging, MCI, and AD. Neurology 2009;73:1722–1728. 3. Lawrence AJ, Chung AW, Morris RG, Markus HS, Barrick TR. Structural network efficiency is associated with cognitive impairment in small-vessel disease. Neurology 2014;83:304–311. 4. Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target largescale human brain networks. Neuron 2009;62:42–52. 5. Schaefer A, Quinque EM, Kipping JA, et al. Early small vessel disease affects frontoparietal and cerebellar hubs in close correlation with clinical symptoms: a resting-state fMRI study. J Cereb Blood Flow Metab 2014;34:1091–1095. 6. Deco G, Kringelbach ML. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 2014;84:892–905. 7. Kim HJ, Im K, Kwon H, et al. Clinical effect of white matter network disruption related to amyloid and small vessel disease. Neurology 2015;85:63–70. 8. Kim HJ, Im K, Kwon H, et al. Effects of amyloid and small vessel disease on white matter network disruption. J Alzheimers Dis 2015;44:963–975. 9. Kantarci K, Schwarz CG, Reid RI, et al. White matter integrity determined with diffusion tensor imaging in older adults without dementia: influence of amyloid load and neurodegeneration. JAMA Neurol 2014;71:1547–1554. 10. Chao LL, Decarli C, Kriger S, et al. Associations between white matter hyperintensities and beta amyloid on integrity of projection, association, and limbic fiber tracts measured with diffusion tensor MRI. PLoS One 2013;8: e65175.
Neurology 85
July 7, 2015
17
ª 2015 American Academy of Neurology. Unauthorized reproduction of this article is prohibited.
Network signatures of age-related cognitive decline Robert D. Stevens and Yousef Hannawi Neurology 2015;85;16-17 Published Online before print June 10, 2015 DOI 10.1212/WNL.0000000000001724 This information is current as of June 10, 2015 Updated Information & Services
including high resolution figures, can be found at: http://www.neurology.org/content/85/1/16.full.html
References
This article cites 10 articles, 3 of which you can access for free at: http://www.neurology.org/content/85/1/16.full.html##ref-list-1
Subspecialty Collections
This article, along with others on similar topics, appears in the following collection(s): Alzheimer's disease http://www.neurology.org//cgi/collection/alzheimers_disease MCI (mild cognitive impairment) http://www.neurology.org//cgi/collection/mci_mild_cognitive_impairm ent MRI http://www.neurology.org//cgi/collection/mri PET http://www.neurology.org//cgi/collection/pet Vascular dementia http://www.neurology.org//cgi/collection/vascular_dementia
Permissions & Licensing
Information about reproducing this article in parts (figures,tables) or in its entirety can be found online at: http://www.neurology.org/misc/about.xhtml#permissions
Reprints
Information about ordering reprints can be found online: http://www.neurology.org/misc/addir.xhtml#reprintsus
Neurology ® is the official journal of the American Academy of Neurology. Published continuously since 1951, it is now a weekly with 48 issues per year. Copyright © 2015 American Academy of Neurology. All rights reserved. Print ISSN: 0028-3878. Online ISSN: 1526-632X.
Network signatures of age-related cognitive decline
Robert D. Stevens, MD Yousef Hannawi, MD
Correspondence to Dr. Stevens: [email protected] Neurology® 2015;85:16–17
Amyloid pathology and cerebrovascular disease are the most frequent, and best characterized, mechanisms linked to age-related cognitive decline and dementia. The relationship between these mechanisms and phenotypes of normal aging, mild cognitive impairment, and dementia has recently been explored in vivo with neuroimaging markers of small vessel disease (SVD) and amyloid deposition (respectively, white matter hyperintensities [WMH] identified on T2-weighted or fluid-attenuated inversion recovery [FLAIR] MRI, and binding of the b-amyloid selective radiotracer Pittsburgh compound B [PiB] on PET). These techniques have accelerated understanding of disease burdens in preclinical and clinical states of impaired cognition. However, a plausible systems-level model that accounts for specific disease pathologies and downstream changes in cerebral integrative function and capacity is lacking. Both amyloid pathology and SVD are widely distributed processes that disrupt multiple neuronal systems, hence are poorly suited to conventional lesion-based analysis. An alternative approach is to consider the consequences of these diseases on the brain’s large-scale structural and functional network architecture. Diffusion tensor imaging (DTI) reveals multifocal microstructural defects affecting white matter tracts in patients with Alzheimer disease (AD),1,2 SVD,3 and in mixed syndromes.2 Studies using resting functional MRI indicate a breakdown in network functional connectivity in AD4 and SVD.5 But how best to analyze and represent brain network properties? This is generally accomplished with graph analysis, a statistical methodology that models real-world systems in terms of nodes (e.g., cortical sites) that are connected by edges (e.g., white matter tracts and bundles). Graph analysis is emerging as the preeminent paradigm in the neuroscience of networks.6 In this issue of Neurology®, Kim et al.7 hypothesize that disruptions in white matter connectivity are a fundamental mechanism linking b-amyloid deposition, or SVD, to cortical atrophy and impairments in cognition. The authors studied 232 patients with
cognitive impairment who were classified according to well-established criteria as amnestic mild cognitive impairment, subcortical vascular mild cognitive impairment, subcortical vascular dementia, and AD. Participants underwent structural brain MRI (including FLAIR, DTI, and cortical thickness measurements), PiB imaging, and neuropsychological testing. Nodes were defined by anatomical parcellation of the cerebral cortex into 78 regions of interest, while edges were identified as the mean DTI-derived fractional anisotropy along white matter tracts connecting 2 nodes. Network connectivity was expressed as “nodal efficiency,” a graph analytical descriptor that is the reciprocal of the shortest path length between a node and all other nodes in the network. The authors used path analysis, a model-driven multivariable technique, to characterize the relationships among WMH, PiB retention, nodal efficiency, cortical thickness, and cognitive performance. They found that WMH volume was linked to reduced nodal efficiency, decreased frontal and temporoparietal cortical thickness, and reduced performance on tests of executive and memory function. However, PiB-defined amyloid burden was associated with temporoparietal cortical thickness and with executive and memory scores, but these effects were completely independent of any changes in nodal efficiency. The authors infer, tentatively, that vascular cognitive impairment is akin to a “connectopathy,” while such a conclusion may not hold in diseases associated with cerebral amyloid pathology. The study by Kim et al. is an elegant investigation of putative relationships among markers of tissue damage, network disconnection, cortical atrophy, and cognitive impairment. Results of this multiscale, multiparametric characterization in a clinically defined population suggest limitations inherent to current phenotypically based diagnostic schemes, which are not congruent with underlying biological abnormalities. The data extend observations made in another report from the same group,8 and align with findings in other recent studies.1–3,9,10 Graph analysis of DTI-defined white matter tracts in patients with early AD indicates a
See page 63 From the Departments of Anesthesiology and Critical Care Medicine (R.D.S., Y.H.), Neurology (R.D.S., Y.H.), Neurosurgery (R.D.S.), and Radiology (R.D.S.), Johns Hopkins University School of Medicine, Baltimore, MD. Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the editorial. 16
© 2015 American Academy of Neurology
ª 2015 American Academy of Neurology. Unauthorized reproduction of this article is prohibited.
reduction in local network efficiency that is linked to worse performance on cognitive testing.1 Similarly, a correlation has been found among WMH burden, DTI-based local and global measures of network efficiency, and cognitive scores in patients with SVD.3 In a study of elderly patients with varying levels of cognitive impairment, WMH burden was associated with damage in projection and association white matter tracts, whereas PiB retention correlated with loss of integrity in the fornix and splenium of the corpus callosum.10 Another study found no relation between PiB-defined amyloid burdens and DTI evidence of white matter degeneration.9 Regarding the study by Kim et al., several caveats need to be considered, most conspicuous of which is disentangling causation vs correlation. As the authors acknowledge, their results cannot attest to a causative sequence of neurobiological events; they highlight a set of associations whose true magnitude and directionality require further investigation. The study lacks an age-matched control group to establish the importance of abnormalities detected in the diseased populations. Additional analysis could have explored the potentially synergistic interaction between vascular and amyloid-associated etiologies of cognitive decline. The anatomical parcellation did not incorporate subcortical gray matter nodes (e.g., thalamus, basal ganglia), suggesting that the network analysis may have been truncated; inclusion of thalamic nuclei, which are critical relays in memory and in executive circuits, may have yielded different results. Finally, the use of nodal efficiency as the sole topological network descriptor is reductive; confirmation of nodal efficiency findings with other graph analytical variables (e.g., centrality, modularity, clustering coefficient) could increase the robustness of the reported results. Overall, this work evokes fundamental questions that deserve further exploration. Is the cortical atrophy in vascular cognitive impairment and amyloid disease the cause, or the result, of degeneration observed in white matter tracts? What is the cellular basis for these imaging abnormalities? How do amyloid deposition and vascular mechanisms synergize to alter network connectivity? What is the relationship between connectopathies identified using anatomical vs functional techniques? Can therapeutic interventions be designed to reverse abnormalities
in node organization or between-node connectivity? Answering these questions could substantially advance the recognition and treatment of agerelated disorders of cognition. AUTHOR CONTRIBUTIONS Dr. Stevens: data interpretation and analysis, drafting the manuscript and editing it for intellectual content. Dr. Hannawi: data interpretation and analysis, critically reviewing the manuscript for its intellectual content.
STUDY FUNDING No targeted funding reported.
DISCLOSURE The authors report no disclosures relevant to the manuscript. Go to Neurology.org for full disclosures.
REFERENCES 1. Reijmer YD, Leemans A, Caeyenberghs K, Heringa SM, Koek HL, Biessels GJ. Disruption of cerebral networks and cognitive impairment in Alzheimer disease. Neurology 2013;80:1370–1377. 2. Lee DY, Fletcher E, Martinez O, et al. Regional pattern of white matter microstructural changes in normal aging, MCI, and AD. Neurology 2009;73:1722–1728. 3. Lawrence AJ, Chung AW, Morris RG, Markus HS, Barrick TR. Structural network efficiency is associated with cognitive impairment in small-vessel disease. Neurology 2014;83:304–311. 4. Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target largescale human brain networks. Neuron 2009;62:42–52. 5. Schaefer A, Quinque EM, Kipping JA, et al. Early small vessel disease affects frontoparietal and cerebellar hubs in close correlation with clinical symptoms: a resting-state fMRI study. J Cereb Blood Flow Metab 2014;34:1091–1095. 6. Deco G, Kringelbach ML. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 2014;84:892–905. 7. Kim HJ, Im K, Kwon H, et al. Clinical effect of white matter network disruption related to amyloid and small vessel disease. Neurology 2015;85:63–70. 8. Kim HJ, Im K, Kwon H, et al. Effects of amyloid and small vessel disease on white matter network disruption. J Alzheimers Dis 2015;44:963–975. 9. Kantarci K, Schwarz CG, Reid RI, et al. White matter integrity determined with diffusion tensor imaging in older adults without dementia: influence of amyloid load and neurodegeneration. JAMA Neurol 2014;71:1547–1554. 10. Chao LL, Decarli C, Kriger S, et al. Associations between white matter hyperintensities and beta amyloid on integrity of projection, association, and limbic fiber tracts measured with diffusion tensor MRI. PLoS One 2013;8: e65175.
Neurology 85
July 7, 2015
17
ª 2015 American Academy of Neurology. Unauthorized reproduction of this article is prohibited.
Network signatures of age-related cognitive decline Robert D. Stevens and Yousef Hannawi Neurology 2015;85;16-17 Published Online before print June 10, 2015 DOI 10.1212/WNL.0000000000001724 This information is current as of June 10, 2015 Updated Information & Services
including high resolution figures, can be found at: http://www.neurology.org/content/85/1/16.full.html
References
This article cites 10 articles, 3 of which you can access for free at: http://www.neurology.org/content/85/1/16.full.html##ref-list-1
Subspecialty Collections
This article, along with others on similar topics, appears in the following collection(s): Alzheimer's disease http://www.neurology.org//cgi/collection/alzheimers_disease MCI (mild cognitive impairment) http://www.neurology.org//cgi/collection/mci_mild_cognitive_impairm ent MRI http://www.neurology.org//cgi/collection/mri PET http://www.neurology.org//cgi/collection/pet Vascular dementia http://www.neurology.org//cgi/collection/vascular_dementia
Permissions & Licensing
Information about reproducing this article in parts (figures,tables) or in its entirety can be found online at: http://www.neurology.org/misc/about.xhtml#permissions
Reprints
Information about ordering reprints can be found online: http://www.neurology.org/misc/addir.xhtml#reprintsus
Neurology ® is the official journal of the American Academy of Neurology. Published continuously since 1951, it is now a weekly with 48 issues per year. Copyright © 2015 American Academy of Neurology. All rights reserved. Print ISSN: 0028-3878. Online ISSN: 1526-632X.
