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Written by Wang Rong, edited by Wang Sizhen The brain is a highly nonlinear complex network system
.
With the growth and development of the nervous system, the brain gradually forms different functional systems to support relatively independent neural activities and specific local functions within the system, such as motor, language, and visual areas [1-3]
.
The separation of the brain system, on the one hand, enables the brain to activate specific functional areas when performing simple cognitive tasks and releases other brain areas to perform more general cognitive processes; on the other hand, it provides the brain with resistance to focal damage The ability to cause dysfunction of the whole brain [1]
.
At the same time, structurally, the functional system is connected to each other through white matter fiber bundles for information transmission; the nervous system integrates the information of different functional systems to provide a physiological basis for the brain to continuously adapt to external changes [1]
.
Therefore, functional separation and integration are the two basic processes for the brain to produce cognitive behaviors, which are closely related to the complexity of cognitive tasks and brain diseases [1, 3-6]
.
However, how does the brain effectively organize neural information for effective processing in the local and whole brain, so as to support a variety of complex cognitive tasks from simple to advanced? The relevant knowledge is not very clear
.
From the perspective of dynamic systems, one possible explanation is: "The nervous system is in a dynamic critical state at rest and can support the balance of separation and integration.
" Thus, when cognitive tasks require higher separation or integration , The brain can flexibly switch to a separate or integrated state to meet the needs of different cognitive functions [1, 3, 5-7]
.
This hypothesis has also received great attention in the field of cognitive psychology in recent years
.
In 2018, the famous American neuroscientist Aron K.
Barbey proposed the modern network neuroscience theory (NNT) of human cognition, which believed that the brain can switch between local information processing (separation) and global processing (integration).
It promotes the development of general intelligence, that is, separation-integration balance corresponds to higher general intelligence [8]
.
However, since the concept of separation-integration balance was proposed in the 1990s, whether the resting brain is in the balance of separation and integration at the whole brain scale has not been finalized, and the NNT theory urgently needs further verification
.
In response to the above common problems in the fields of dynamics, network science, neuroscience and psychology, on June 8, 2021, Zhou Changsong's group from Hong Kong Baptist University and Andrea Hildebrandt's group from Oldenburg University in Germany collaborated in Proceedings of the The National Academy of Sciences (PNAS) published the latest paper titled Segregation, integration and balance of large-scale resting brain networks configure different cognitive abilities, and found that the brains of healthy young people are in a state of separation and integration at rest.
, And this state predicts the best memory ability
.
Wang Rong and Liu Mianxin are the first authors of this article
.
In this article, the author first proposes a hierarchical module division method for functional networks based on eigenmodes, and obtains the brain's global integrated component Hin and separate Hse components
.
When the integrated and separated components are equal, the brain network is in a balance of separation and integration
.
Define the competition between separation and integration HB = Hin-Hse, HB> 0 indicates that the brain is biased toward integration, HB <0 indicates that the brain is biased toward separation, and HB = 0 indicates a balance between separation and integration
.
In order to verify the effectiveness of separation and integration based on the eigenmode theory, the researchers built a network dynamics model on the whole brain scale based on the Gaussian linear model, obtained a stable brain function matrix in a sufficiently long time range, and passed The coupling parameter c in the adjustment model can be compared with the real function matrix, so that the balance of separation and integration can be studied from the perspective of the dynamic model
.
It is found that at the critical coupling strength c=70, the simulated functional network is most similar to the real network, which is represented by the same average correlation coefficient, the smallest distance between the real and simulated matrices, and the smallest difference in the degree of brain nodes.
The characteristic path length, clustering coefficient and global efficiency of (Figure 1) indicate that the resting brain corresponds to the dynamic behavior of the critical coupling strength
.
Figure 1.
At the coupling strength c=70, the analog function matrix is most similar to the real function matrix
.
(Picture source: Zhou Changsong laboratory) At the same time, as the coupling strength increases, the network transitions from an asynchronous state to a synchronous state
.
In this process, global integration increases and separation decreases
.
This dynamic process can be well described by increasing the separation component and decreasing the integration component (Figure 2), showing the effectiveness of characterizing the separation and integration process based on the characteristic mode
.
Interestingly, the separation and integration curves in the Gaussian model intersect at the critical coupling strength (Figure 2)
.
Therefore, as the coupling strength increases, the degree of competition between integration and separation increases from negative to positive, and passes through the zero point at c=70, indicating that there is a balance between separation and integration in the kinetic model, and this The equilibrium state cannot be revealed by the monotonous changes of modularity and participation coefficient
.
Most importantly, in the real brain function network, HB = -0.
106 is close to zero, indicating that the brains of healthy young people maintain a balance between separation and integration in the resting state
.
Figure 2.
The brain of a healthy young man is in a state of separation and integration at rest
.
(Picture source: Zhou Changsong laboratory) Further, the author analyzed the dynamic function mode from the perspective of dynamic analysis, and found that the dynamic switching mode between the separated and integrated state is significantly different between individuals; the balanced brain resides in The time of the integration and separation states is almost equal (Figure 3), indicating the coexistence of static and dynamic equilibrium between the separation and integration states
.
At the same time, it was also found that individual brains with a high degree of separation or integration are not easy to switch between the separation and integration states, while the individual brains in equilibrium show more obvious switching between the separation and integration states, with the highest switching frequency (Figure 3) , Which shows that the balanced brain has the highest flexibility
.
Figure 3.
The effect of separation-integration balance on dynamic behavior (Source: Zhou Changsong laboratory) Finally, in order to study whether separation, integration and balance can predict different cognitive abilities, the research team adopted structural equation modeling (structural equation modeling) , SEM)
.
Based on nine specific cognitive task measurements, the model can estimate four general cognitive ability factors through factor analysis: general intelligence (g), crystal intelligence (cry), processing speed (spd) and memory ability (mem) (Figure 4a) )
.
It is found that higher general intelligence is related to stronger integration, higher crystal intelligence and processing speed depend on stronger separation, and the balance between separation and integration supports the highest memory capacity (Figure 4b)
.
Figure 4.
(a) SEM structure diagram for testing the linear relationship between cognitive ability and HB (CFI: comparative fitting index; SRMR: approximate root mean square error; RMSEA: standardized root mean square residual
.
(b) The division of separation group (SG), balance group (BG) and integration group (IG) group
.
(d) Estimated values of the four cognitive abilities in different groups
.
(Picture source: Zhou Changsong laboratory) Article conclusion and discussion This study clarified the balance between separation and integration by proposing a layered module method of brain function network
.
And from the two aspects of dynamic model and experimental data, it is proved that the healthy young brain is in a balanced state at rest.
This balanced state allows the brain to switch flexibly between separation and integration
.
And this analysis method more effectively reveals the complex role of separation, integration and balance in different cognitive abilities
.
Higher global integration promotes general cognitive ability; better crystal intelligence and processing speed are related to higher separation; balance predicts the highest memory ability
.
Although Aron K.
Barbey’s NNT theory predicts that general intelligence is related to separation-integration balance, this study found that higher network integration corresponds to better general intelligence.
This is because in the field of cognitive psychology, the current understanding of how accurate The measurement of general intelligence is still controversial
.
In this work, the two tasks of Penn Progressive Matrices and Variable Short Penn Line Orientation Test are only included in the general intelligence measurement, so general intelligence can be interpreted as fluid intelligence, that is, higher integration is related to better fluid intelligence, in line with NNT Predictions
.
At the same time, memory itself is a complex ability, including working memory, primary (short-term) and secondary (long-term) memory
.
The memory task in this research involves the representational relationship between complex mental blocks, which can be regarded as the basic cognitive mechanism of general intelligence
.
Therefore, the separation-integration balance predicts the highest memory ability and validates the NNT theory
.
In general, this research provides a comprehensive and in-depth understanding of the functional principles of the brain that support various functional needs and cognitive abilities, and promotes the development of modern network neuroscience theories of human cognition
.
Associate Professor Wang Rong, Xiangjiang Scholar, Xi’an University of Science and Technology (left); Dr.
Liu Mianxin, Shanghai University of Science and Technology (middle); Professor Zhou Changsong, Director of Nonlinear Research, Hong Kong Baptist University (right) (Source: Zhou Changsong Laboratory) Original link: https://doi .
org/10.
1073/pnas.
2022288118 Selected articles from previous issues [1] Cell︱ breakthrough! The new mechanism of human brain spatial navigation and memory: phase precession of hippocampus and entorhinal cortex [2] PNAS︱ new hope for vision restoration? CXCR4/CXCL12 signal-mediated new mechanism of damaged optic nerve regeneration [3] Sci Transl Med︱ a new mechanism to improve the pathology of Alzheimer’s disease [4] Alzheimer's Dementia︱ the latest scientific hypothesis! JNK is a therapeutic target for neurodegenerative diseases [5] Nat Neurosci ︱ for the first time! Neuron ApoE is a new mechanism that affects immune response genes to cause AD pathology.
Recommended high-quality scientific research training courses [1] "Scientific Research Image Processing and Mapping" offline : June 26-27, Shanghai; July 10-11, Beijing [2] Patch clamp and optogenetic and calcium imaging technology seminar (June 26-27, two days and one night) [3] Single cell sequencing Data Analysis and Project Design Network Practice Class (July 24-25) References (slide up and down to view) [1] Wig, GS, Segregated systems of human brain networks.
Trends Cogn.
Sci.
, 2017.
21(12) : p.
981-996.
【2】Sporns, O.
, Networks of the brain.
2010: MIT press.
【3】Shine, JM, Neuromodulatory influences on integration and segregation in the brain.
Trends Cogn.
Sci.
, 2019.
23(7): p.
572-583.
[4] Cabral, J.
, ML Kringelbach, and G.
Deco,
.
With the growth and development of the nervous system, the brain gradually forms different functional systems to support relatively independent neural activities and specific local functions within the system, such as motor, language, and visual areas [1-3]
.
The separation of the brain system, on the one hand, enables the brain to activate specific functional areas when performing simple cognitive tasks and releases other brain areas to perform more general cognitive processes; on the other hand, it provides the brain with resistance to focal damage The ability to cause dysfunction of the whole brain [1]
.
At the same time, structurally, the functional system is connected to each other through white matter fiber bundles for information transmission; the nervous system integrates the information of different functional systems to provide a physiological basis for the brain to continuously adapt to external changes [1]
.
Therefore, functional separation and integration are the two basic processes for the brain to produce cognitive behaviors, which are closely related to the complexity of cognitive tasks and brain diseases [1, 3-6]
.
However, how does the brain effectively organize neural information for effective processing in the local and whole brain, so as to support a variety of complex cognitive tasks from simple to advanced? The relevant knowledge is not very clear
.
From the perspective of dynamic systems, one possible explanation is: "The nervous system is in a dynamic critical state at rest and can support the balance of separation and integration.
" Thus, when cognitive tasks require higher separation or integration , The brain can flexibly switch to a separate or integrated state to meet the needs of different cognitive functions [1, 3, 5-7]
.
This hypothesis has also received great attention in the field of cognitive psychology in recent years
.
In 2018, the famous American neuroscientist Aron K.
Barbey proposed the modern network neuroscience theory (NNT) of human cognition, which believed that the brain can switch between local information processing (separation) and global processing (integration).
It promotes the development of general intelligence, that is, separation-integration balance corresponds to higher general intelligence [8]
.
However, since the concept of separation-integration balance was proposed in the 1990s, whether the resting brain is in the balance of separation and integration at the whole brain scale has not been finalized, and the NNT theory urgently needs further verification
.
In response to the above common problems in the fields of dynamics, network science, neuroscience and psychology, on June 8, 2021, Zhou Changsong's group from Hong Kong Baptist University and Andrea Hildebrandt's group from Oldenburg University in Germany collaborated in Proceedings of the The National Academy of Sciences (PNAS) published the latest paper titled Segregation, integration and balance of large-scale resting brain networks configure different cognitive abilities, and found that the brains of healthy young people are in a state of separation and integration at rest.
, And this state predicts the best memory ability
.
Wang Rong and Liu Mianxin are the first authors of this article
.
In this article, the author first proposes a hierarchical module division method for functional networks based on eigenmodes, and obtains the brain's global integrated component Hin and separate Hse components
.
When the integrated and separated components are equal, the brain network is in a balance of separation and integration
.
Define the competition between separation and integration HB = Hin-Hse, HB> 0 indicates that the brain is biased toward integration, HB <0 indicates that the brain is biased toward separation, and HB = 0 indicates a balance between separation and integration
.
In order to verify the effectiveness of separation and integration based on the eigenmode theory, the researchers built a network dynamics model on the whole brain scale based on the Gaussian linear model, obtained a stable brain function matrix in a sufficiently long time range, and passed The coupling parameter c in the adjustment model can be compared with the real function matrix, so that the balance of separation and integration can be studied from the perspective of the dynamic model
.
It is found that at the critical coupling strength c=70, the simulated functional network is most similar to the real network, which is represented by the same average correlation coefficient, the smallest distance between the real and simulated matrices, and the smallest difference in the degree of brain nodes.
The characteristic path length, clustering coefficient and global efficiency of (Figure 1) indicate that the resting brain corresponds to the dynamic behavior of the critical coupling strength
.
Figure 1.
At the coupling strength c=70, the analog function matrix is most similar to the real function matrix
.
(Picture source: Zhou Changsong laboratory) At the same time, as the coupling strength increases, the network transitions from an asynchronous state to a synchronous state
.
In this process, global integration increases and separation decreases
.
This dynamic process can be well described by increasing the separation component and decreasing the integration component (Figure 2), showing the effectiveness of characterizing the separation and integration process based on the characteristic mode
.
Interestingly, the separation and integration curves in the Gaussian model intersect at the critical coupling strength (Figure 2)
.
Therefore, as the coupling strength increases, the degree of competition between integration and separation increases from negative to positive, and passes through the zero point at c=70, indicating that there is a balance between separation and integration in the kinetic model, and this The equilibrium state cannot be revealed by the monotonous changes of modularity and participation coefficient
.
Most importantly, in the real brain function network, HB = -0.
106 is close to zero, indicating that the brains of healthy young people maintain a balance between separation and integration in the resting state
.
Figure 2.
The brain of a healthy young man is in a state of separation and integration at rest
.
(Picture source: Zhou Changsong laboratory) Further, the author analyzed the dynamic function mode from the perspective of dynamic analysis, and found that the dynamic switching mode between the separated and integrated state is significantly different between individuals; the balanced brain resides in The time of the integration and separation states is almost equal (Figure 3), indicating the coexistence of static and dynamic equilibrium between the separation and integration states
.
At the same time, it was also found that individual brains with a high degree of separation or integration are not easy to switch between the separation and integration states, while the individual brains in equilibrium show more obvious switching between the separation and integration states, with the highest switching frequency (Figure 3) , Which shows that the balanced brain has the highest flexibility
.
Figure 3.
The effect of separation-integration balance on dynamic behavior (Source: Zhou Changsong laboratory) Finally, in order to study whether separation, integration and balance can predict different cognitive abilities, the research team adopted structural equation modeling (structural equation modeling) , SEM)
.
Based on nine specific cognitive task measurements, the model can estimate four general cognitive ability factors through factor analysis: general intelligence (g), crystal intelligence (cry), processing speed (spd) and memory ability (mem) (Figure 4a) )
.
It is found that higher general intelligence is related to stronger integration, higher crystal intelligence and processing speed depend on stronger separation, and the balance between separation and integration supports the highest memory capacity (Figure 4b)
.
Figure 4.
(a) SEM structure diagram for testing the linear relationship between cognitive ability and HB (CFI: comparative fitting index; SRMR: approximate root mean square error; RMSEA: standardized root mean square residual
.
(b) The division of separation group (SG), balance group (BG) and integration group (IG) group
.
(d) Estimated values of the four cognitive abilities in different groups
.
(Picture source: Zhou Changsong laboratory) Article conclusion and discussion This study clarified the balance between separation and integration by proposing a layered module method of brain function network
.
And from the two aspects of dynamic model and experimental data, it is proved that the healthy young brain is in a balanced state at rest.
This balanced state allows the brain to switch flexibly between separation and integration
.
And this analysis method more effectively reveals the complex role of separation, integration and balance in different cognitive abilities
.
Higher global integration promotes general cognitive ability; better crystal intelligence and processing speed are related to higher separation; balance predicts the highest memory ability
.
Although Aron K.
Barbey’s NNT theory predicts that general intelligence is related to separation-integration balance, this study found that higher network integration corresponds to better general intelligence.
This is because in the field of cognitive psychology, the current understanding of how accurate The measurement of general intelligence is still controversial
.
In this work, the two tasks of Penn Progressive Matrices and Variable Short Penn Line Orientation Test are only included in the general intelligence measurement, so general intelligence can be interpreted as fluid intelligence, that is, higher integration is related to better fluid intelligence, in line with NNT Predictions
.
At the same time, memory itself is a complex ability, including working memory, primary (short-term) and secondary (long-term) memory
.
The memory task in this research involves the representational relationship between complex mental blocks, which can be regarded as the basic cognitive mechanism of general intelligence
.
Therefore, the separation-integration balance predicts the highest memory ability and validates the NNT theory
.
In general, this research provides a comprehensive and in-depth understanding of the functional principles of the brain that support various functional needs and cognitive abilities, and promotes the development of modern network neuroscience theories of human cognition
.
Associate Professor Wang Rong, Xiangjiang Scholar, Xi’an University of Science and Technology (left); Dr.
Liu Mianxin, Shanghai University of Science and Technology (middle); Professor Zhou Changsong, Director of Nonlinear Research, Hong Kong Baptist University (right) (Source: Zhou Changsong Laboratory) Original link: https://doi .
org/10.
1073/pnas.
2022288118 Selected articles from previous issues [1] Cell︱ breakthrough! The new mechanism of human brain spatial navigation and memory: phase precession of hippocampus and entorhinal cortex [2] PNAS︱ new hope for vision restoration? CXCR4/CXCL12 signal-mediated new mechanism of damaged optic nerve regeneration [3] Sci Transl Med︱ a new mechanism to improve the pathology of Alzheimer’s disease [4] Alzheimer's Dementia︱ the latest scientific hypothesis! JNK is a therapeutic target for neurodegenerative diseases [5] Nat Neurosci ︱ for the first time! Neuron ApoE is a new mechanism that affects immune response genes to cause AD pathology.
Recommended high-quality scientific research training courses [1] "Scientific Research Image Processing and Mapping" offline : June 26-27, Shanghai; July 10-11, Beijing [2] Patch clamp and optogenetic and calcium imaging technology seminar (June 26-27, two days and one night) [3] Single cell sequencing Data Analysis and Project Design Network Practice Class (July 24-25) References (slide up and down to view) [1] Wig, GS, Segregated systems of human brain networks.
Trends Cogn.
Sci.
, 2017.
21(12) : p.
981-996.
【2】Sporns, O.
, Networks of the brain.
2010: MIT press.
【3】Shine, JM, Neuromodulatory influences on integration and segregation in the brain.
Trends Cogn.
Sci.
, 2019.
23(7): p.
572-583.
[4] Cabral, J.
, ML Kringelbach, and G.
Deco,
