Clinical Neurophysiology 125 (2014) 653–654
Contents lists available at ScienceDirect
Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph
Editorial
Global complexity and cognitive reserve in MCI See Article, pages 694–702
Mild Cognitive Impairment (MCI) is defined as a transitional state between normal aging and the early stages of dementia, particularly Alzheimer’s disease (AD) (Petersen et al., 1999). Some epidemiological studies reported that the prevalence of MCI in the elderly population older than 65 years is up to 19% and that more than half of the patients with this disease progress to dementia within 5 years (Gauthier et al., 2006). It is an urgent issue to establish diagnostic methods to identify MCI and early stages of AD easily, objectively and non-invasively. In addition, because of recent failures of several pharmacological and immunological therapies in clinical trials for AD (Corbett et al., 2012), the development of some alternative strategies based on non-pharmacological cognitive intervention are also needed for treatment of MCI and AD patients. MCI subjects are now recommended to engage in cognitive activities and to participate in social activities, which have proven to be beneficial for patient improvement without any risk (Petersen, 2011). In this issue of Clinical Neurophysiology, Ahmadlou et al. (2014) aimed at identifying possible neurophysiological biomarker for diagnosis of MCI. For that purpose, the authors recorded magnetoencephalography (MEG) in MCI and normal subjects during the Sternberg task and looked at the global complexity of the functional brain network. Neurophysiological techniques like electroencephalography (EEG) and MEG share several advantages over other techniques that measure brain function, including direct measurement of neural activity and high temporal resolution. MEG, however, has some more advantages over EEG, such as: (1) better spatial and frequency resolution because the magnetic field is much less affected by tissues (e.g., the scalp, skull and cerebrospinal fluid) than the electric field; (2) reference-free measurements of the magnetic field while in EEG recordings one specific channel is needed as reference for the others; and (3) a larger number of sensors of recent MEG systems (Stam, 2010). Especially, for the evaluation of functional connectivity between brain regions, the reference-free measurement of the magnetic field may be a great value for accurate and reliable results (Knake et al., 2006). Complexity in the brain network is a vital characteristic for feasible synchronization and efficient information transmission. For instance, if all brain regions connect to each other with equally strong connections during a task, the global network may not be efficient for transmission of the information from one region to another region (since it is paying a lot of extra cost/connections). Indeed some areas are hubs (usually high level processing regions) and need more connectivity, and other areas do not need high connectivity. It is the distribution of these connectivities among the
brain regions that determines the efficiency of the brain network. In the study of Ahmadlou et al. (2014), the brain networks’ complexities were measured by Graph Index Complexity and Efficiency Complexity. They focused on alpha and theta bands based mainly on their essential involvement in memory functions in healthy humans. It was found that MCI subjects, compared with normal subjects, showed reduced global complexity of functional networks in the two frequency bands. However, the reduction in complexity of functional networks was more pronounced in the theta band in the whole brain and intra left hemisphere. The left hemisphere involvement was attributed to the fact that a verbal task was used in this study. These findings suggested that the global complexity of functional brain networks in MCI subjects might be less efficient with slower information transmission during verbal working memory. Overall, by applying the Graph Complexity Index and Efficiency Complexity to MEG data, the authors succeeded to depict the global complexity of the brain functional connectivity network after quantifying the constructed network (Ahmadlou et al., 2014). Since MCI subjects have more global complexity deficits (Patalong-Ogiewa et al., 2009), the combination of MEG and these two complexity analysis methods appears to be quite promising to visualize connectome abnormalities, which may potentially represent good biomarkers for diagnosis of MCI and early AD. Although MEG connectivity analyses at source level and complexity analyses of functional networks in MCI and AD have been largely unexplored, previous studies have assessed differences in brain activity related to working memory in patients with early Alzheimer’s disease (AD), mild cognitive impairment (MCI), and age-matched healthy controls. For instance we also used MEG and multiple-source beamformer for localization of source-power changes across the brain cortex while the subjects performed a modified version of the Sternberg’s memory recognition task (Kurimoto et al., 2012). We found significant differences in oscillatory response during the task, specifically in the beta and gamma frequency bands: patients with AD showed reduced beta event-related desynchronization (ERD) in the right central area compared to controls, and reduced gamma ERD in the left prefrontal and medial parietal cortex compared to patients with MCI. Our findings suggest that reduced oscillatory responses over certain brain regions in high frequency bands (i.e., beta, gamma), and especially in the beta band that was significantly different between AD patients and healthy subjects, may represent brain electromagnetic changes underlying visual working memory dysfunction in early AD, and a neurophysiological indicator of cognitive decline.
1388-2457/$36.00 Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.clinph.2013.10.015
654
Editorial / Clinical Neurophysiology 125 (2014) 653–654
There were no significant differences, however, between MCI patients and healthy controls. Unlike the study of Ahmadlou et al. (2014), we did not find significant differences in working memory-related activity between MCI, early AD and healthy subjects in the theta and alpha frequency bands. The difference in the results of these studies is likely due to the characteristics of the analysis methods (source-power changes vs. functional connectivity/ source localization vs. global complexity/ verbal working memory vs. visual working memory). In particular, the complexity measures they adopted this time allowed them to visualize global abnormalities in lower frequency activities, specifically alpha and theta bands, while our analyses allowed for visualization of regional functional deficits. It is well-known that some elderly people can tolerate better physiological or pathological brain changes than others and maintain cognitive and social function. In an attempt to explain these differences in cognitive decline between individuals, Stern (2003) proposed the concept of ‘‘cognitive reserve’’. Cognitive reserve is closely associated with the brain capacity of tolerance for neuropathology which could minimize clinical manifestations of normal aging and dementia. The cognitive reserve hypothesis predicts that older adults with good cognitive ability will have a lower risk of dementia than individuals with less cognitive ability. It was suggested in some epidemiological studies that educational and occupational experiences can increase cognitive reserve, and the risk of developing Alzheimer’s disease is reduced in individuals with higher educational or occupational attainment. The efficiency of cortical circuits serving specific cognitive tasks, which is well known to be enhanced by repeated use, is classified as an active component of cognitive reserve (Stern, 2003). Previous studies have demonstrated that a patient’s cognitive reserve may moderate the age-related and pathological neural changes in task performance in both normal aging and dementia. By measuring fMRI during a language processing task, Bosch et al. (2010) reported that MCI patients showed positive correlations between CR measures and BOLD activation (in areas directly processing speech) and deactivation (in regions of the Default Mode Network), whereas the healthy elderly showed inverted correlations. They suggested that cognitive reserve might modulate brain areas showing both task-induced activation and deactivation in an opposite manner when considering the healthy elderly versus MCI patients. We can assume that a cognitive reserve might facilitate brain reorganizations reflecting behavioral compensatory mechanisms. Although not tested in the current study by Ahmadlou et al. (2014) in this issue of Clinical Neurophysiology, cognitive reserve has already been suggested to be closely related to the strength of functional connectivity between brain regions (Steffener et al., 2012). Greater cognitive reserve may result in increased functional connectivity, or at least maintained connectivity with advancing
age. Better understanding of the relationships between cognitive reserve and functional connectivity could lead to more efficient cognitive interventions to improve patients with MCI or ageing-related cognitive deficits, and reduce the risk of AD (Bullmore and Sporns, 2009). References Ahmadlou M, Adeli A, Bajo R, Adeli H. Complexity of Functional Connectivity Networks in Mild Cognitive Impairment Subjects during a Working Memory Task. Clin Neurophysiol 2014;125:694–702. Bosch B, Bartrés-Faz D, Rami L, Arenaza-Urquijo EM, Fernández-Espejo D, Junqué C, et al. Cognitive reserve modulates task-induced activations and deactivations in healthy elders, amnestic mild cognitive impairment and mild Alzheimer’s disease. Cortex 2010;46:451–61. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009;10:186–98. Corbett A, Pickett J, Burns A, Corcoran J, Dunnett SB, Edison P, et al. Drug repositioning for Alzheimer’s disease. Nat Rev Drug Discov 2012;11:833–46. Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, et al. Mild cognitive impairment. Lancet 2006;367:1262–70. Knake S, Halgren E, Shiraishi H, Hara K, Hamer HM, Grant PE, et al. The value of multichannel MEG and EEG in the presurgical evaluation of 70 epilepsy patients. Epilepsy Res 2006;69:80–6. Kurimoto R, Ishii R, Canuet L, Ikezawa K, Iwase M, Azechi M, et al. Induced oscillatory responses during the Sternberg’s visual memory task in patients with Alzheimer’s disease and mild cognitive impairment. Neuroimage 2012;59:4132–40. Patalong-Ogiewa MB, Siuda JS, Opala GM. Working and episodic memory in the MCI group. J Neurol Sci 2009;283:291. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 1999;56:303–8. Petersen RC. Clinical practice. Mild Cognitive Impairment. New Engl J Med 2011;364:2227–34. Stam CJ. Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders. J Neurol Sci 2010;289:128–34. Steffener J, Habeck CG, Stern Y. Age-related changes in task related functional network connectivity. PLoS One 2012;7:e44421. Stern Y. The concept of cognitive reserve: a catalyst for research. J Clin Exp Neuropsychol 2003;25:589–93.
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Ryouhei Ishii Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan ⇑ Address: Department of Psychiatry, Osaka University Graduate School of Medicine, 2-2 D3, Yamada-oka, Suita, Osaka, 565-0871 Japan. Tel.: +81 6 6879 3051; fax: +81 6 6879 3059. E-mail address: [email protected] Leonides Canuet Centre for Biomedical Technology, Department of Cognitive and Computational Neuroscience, Complutense University of Madrid, UPM, Madrid, Spain Available online 5 November 2013
Contents lists available at ScienceDirect
Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph
Editorial
Global complexity and cognitive reserve in MCI See Article, pages 694–702
Mild Cognitive Impairment (MCI) is defined as a transitional state between normal aging and the early stages of dementia, particularly Alzheimer’s disease (AD) (Petersen et al., 1999). Some epidemiological studies reported that the prevalence of MCI in the elderly population older than 65 years is up to 19% and that more than half of the patients with this disease progress to dementia within 5 years (Gauthier et al., 2006). It is an urgent issue to establish diagnostic methods to identify MCI and early stages of AD easily, objectively and non-invasively. In addition, because of recent failures of several pharmacological and immunological therapies in clinical trials for AD (Corbett et al., 2012), the development of some alternative strategies based on non-pharmacological cognitive intervention are also needed for treatment of MCI and AD patients. MCI subjects are now recommended to engage in cognitive activities and to participate in social activities, which have proven to be beneficial for patient improvement without any risk (Petersen, 2011). In this issue of Clinical Neurophysiology, Ahmadlou et al. (2014) aimed at identifying possible neurophysiological biomarker for diagnosis of MCI. For that purpose, the authors recorded magnetoencephalography (MEG) in MCI and normal subjects during the Sternberg task and looked at the global complexity of the functional brain network. Neurophysiological techniques like electroencephalography (EEG) and MEG share several advantages over other techniques that measure brain function, including direct measurement of neural activity and high temporal resolution. MEG, however, has some more advantages over EEG, such as: (1) better spatial and frequency resolution because the magnetic field is much less affected by tissues (e.g., the scalp, skull and cerebrospinal fluid) than the electric field; (2) reference-free measurements of the magnetic field while in EEG recordings one specific channel is needed as reference for the others; and (3) a larger number of sensors of recent MEG systems (Stam, 2010). Especially, for the evaluation of functional connectivity between brain regions, the reference-free measurement of the magnetic field may be a great value for accurate and reliable results (Knake et al., 2006). Complexity in the brain network is a vital characteristic for feasible synchronization and efficient information transmission. For instance, if all brain regions connect to each other with equally strong connections during a task, the global network may not be efficient for transmission of the information from one region to another region (since it is paying a lot of extra cost/connections). Indeed some areas are hubs (usually high level processing regions) and need more connectivity, and other areas do not need high connectivity. It is the distribution of these connectivities among the
brain regions that determines the efficiency of the brain network. In the study of Ahmadlou et al. (2014), the brain networks’ complexities were measured by Graph Index Complexity and Efficiency Complexity. They focused on alpha and theta bands based mainly on their essential involvement in memory functions in healthy humans. It was found that MCI subjects, compared with normal subjects, showed reduced global complexity of functional networks in the two frequency bands. However, the reduction in complexity of functional networks was more pronounced in the theta band in the whole brain and intra left hemisphere. The left hemisphere involvement was attributed to the fact that a verbal task was used in this study. These findings suggested that the global complexity of functional brain networks in MCI subjects might be less efficient with slower information transmission during verbal working memory. Overall, by applying the Graph Complexity Index and Efficiency Complexity to MEG data, the authors succeeded to depict the global complexity of the brain functional connectivity network after quantifying the constructed network (Ahmadlou et al., 2014). Since MCI subjects have more global complexity deficits (Patalong-Ogiewa et al., 2009), the combination of MEG and these two complexity analysis methods appears to be quite promising to visualize connectome abnormalities, which may potentially represent good biomarkers for diagnosis of MCI and early AD. Although MEG connectivity analyses at source level and complexity analyses of functional networks in MCI and AD have been largely unexplored, previous studies have assessed differences in brain activity related to working memory in patients with early Alzheimer’s disease (AD), mild cognitive impairment (MCI), and age-matched healthy controls. For instance we also used MEG and multiple-source beamformer for localization of source-power changes across the brain cortex while the subjects performed a modified version of the Sternberg’s memory recognition task (Kurimoto et al., 2012). We found significant differences in oscillatory response during the task, specifically in the beta and gamma frequency bands: patients with AD showed reduced beta event-related desynchronization (ERD) in the right central area compared to controls, and reduced gamma ERD in the left prefrontal and medial parietal cortex compared to patients with MCI. Our findings suggest that reduced oscillatory responses over certain brain regions in high frequency bands (i.e., beta, gamma), and especially in the beta band that was significantly different between AD patients and healthy subjects, may represent brain electromagnetic changes underlying visual working memory dysfunction in early AD, and a neurophysiological indicator of cognitive decline.
1388-2457/$36.00 Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.clinph.2013.10.015
654
Editorial / Clinical Neurophysiology 125 (2014) 653–654
There were no significant differences, however, between MCI patients and healthy controls. Unlike the study of Ahmadlou et al. (2014), we did not find significant differences in working memory-related activity between MCI, early AD and healthy subjects in the theta and alpha frequency bands. The difference in the results of these studies is likely due to the characteristics of the analysis methods (source-power changes vs. functional connectivity/ source localization vs. global complexity/ verbal working memory vs. visual working memory). In particular, the complexity measures they adopted this time allowed them to visualize global abnormalities in lower frequency activities, specifically alpha and theta bands, while our analyses allowed for visualization of regional functional deficits. It is well-known that some elderly people can tolerate better physiological or pathological brain changes than others and maintain cognitive and social function. In an attempt to explain these differences in cognitive decline between individuals, Stern (2003) proposed the concept of ‘‘cognitive reserve’’. Cognitive reserve is closely associated with the brain capacity of tolerance for neuropathology which could minimize clinical manifestations of normal aging and dementia. The cognitive reserve hypothesis predicts that older adults with good cognitive ability will have a lower risk of dementia than individuals with less cognitive ability. It was suggested in some epidemiological studies that educational and occupational experiences can increase cognitive reserve, and the risk of developing Alzheimer’s disease is reduced in individuals with higher educational or occupational attainment. The efficiency of cortical circuits serving specific cognitive tasks, which is well known to be enhanced by repeated use, is classified as an active component of cognitive reserve (Stern, 2003). Previous studies have demonstrated that a patient’s cognitive reserve may moderate the age-related and pathological neural changes in task performance in both normal aging and dementia. By measuring fMRI during a language processing task, Bosch et al. (2010) reported that MCI patients showed positive correlations between CR measures and BOLD activation (in areas directly processing speech) and deactivation (in regions of the Default Mode Network), whereas the healthy elderly showed inverted correlations. They suggested that cognitive reserve might modulate brain areas showing both task-induced activation and deactivation in an opposite manner when considering the healthy elderly versus MCI patients. We can assume that a cognitive reserve might facilitate brain reorganizations reflecting behavioral compensatory mechanisms. Although not tested in the current study by Ahmadlou et al. (2014) in this issue of Clinical Neurophysiology, cognitive reserve has already been suggested to be closely related to the strength of functional connectivity between brain regions (Steffener et al., 2012). Greater cognitive reserve may result in increased functional connectivity, or at least maintained connectivity with advancing
age. Better understanding of the relationships between cognitive reserve and functional connectivity could lead to more efficient cognitive interventions to improve patients with MCI or ageing-related cognitive deficits, and reduce the risk of AD (Bullmore and Sporns, 2009). References Ahmadlou M, Adeli A, Bajo R, Adeli H. Complexity of Functional Connectivity Networks in Mild Cognitive Impairment Subjects during a Working Memory Task. Clin Neurophysiol 2014;125:694–702. Bosch B, Bartrés-Faz D, Rami L, Arenaza-Urquijo EM, Fernández-Espejo D, Junqué C, et al. Cognitive reserve modulates task-induced activations and deactivations in healthy elders, amnestic mild cognitive impairment and mild Alzheimer’s disease. Cortex 2010;46:451–61. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009;10:186–98. Corbett A, Pickett J, Burns A, Corcoran J, Dunnett SB, Edison P, et al. Drug repositioning for Alzheimer’s disease. Nat Rev Drug Discov 2012;11:833–46. Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, et al. Mild cognitive impairment. Lancet 2006;367:1262–70. Knake S, Halgren E, Shiraishi H, Hara K, Hamer HM, Grant PE, et al. The value of multichannel MEG and EEG in the presurgical evaluation of 70 epilepsy patients. Epilepsy Res 2006;69:80–6. Kurimoto R, Ishii R, Canuet L, Ikezawa K, Iwase M, Azechi M, et al. Induced oscillatory responses during the Sternberg’s visual memory task in patients with Alzheimer’s disease and mild cognitive impairment. Neuroimage 2012;59:4132–40. Patalong-Ogiewa MB, Siuda JS, Opala GM. Working and episodic memory in the MCI group. J Neurol Sci 2009;283:291. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 1999;56:303–8. Petersen RC. Clinical practice. Mild Cognitive Impairment. New Engl J Med 2011;364:2227–34. Stam CJ. Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders. J Neurol Sci 2010;289:128–34. Steffener J, Habeck CG, Stern Y. Age-related changes in task related functional network connectivity. PLoS One 2012;7:e44421. Stern Y. The concept of cognitive reserve: a catalyst for research. J Clin Exp Neuropsychol 2003;25:589–93.
⇑
Ryouhei Ishii Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan ⇑ Address: Department of Psychiatry, Osaka University Graduate School of Medicine, 2-2 D3, Yamada-oka, Suita, Osaka, 565-0871 Japan. Tel.: +81 6 6879 3051; fax: +81 6 6879 3059. E-mail address: [email protected] Leonides Canuet Centre for Biomedical Technology, Department of Cognitive and Computational Neuroscience, Complutense University of Madrid, UPM, Madrid, Spain Available online 5 November 2013
