Limits...
Dynamically Allocated Hub in Task-Evoked Network Predicts the Vulnerable Prefrontal Locus for Contextual Memory Retrieval in Macaques.

Osada T, Adachi Y, Miyamoto K, Jimura K, Setsuie R, Miyashita Y - PLoS Biol. (2015)

Bottom Line: We found that the activated areas formed a hierarchical hub-centric network based on task-evoked directed connectivity, differently from the anatomical network reflecting axonal projection patterns.Our results suggest that PFC areas dynamically and cooperatively shape a functional hub-centric network to reallocate the lesion-effective site depending on the cognitive processes, apart from static anatomical hubs.These findings will be a foundation for precise prediction of behavioral impacts of damage or surgical intervention in human brains.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, The University of Tokyo School of Medicine, Hongo, Bunkyo-ku, Tokyo, Japan; Department of Physiology, Juntendo University School of Medicine, Hongo, Bunkyo-ku, Tokyo, Japan.

ABSTRACT
Neuroimaging and neurophysiology have revealed that multiple areas in the prefrontal cortex (PFC) are activated in a specific memory task, but severity of impairment after PFC lesions is largely different depending on which activated area is damaged. The critical relationship between lesion sites and impairments has not yet been given a clear mechanistic explanation. Although recent works proposed that a whole-brain network contains hubs that play integrative roles in cortical information processing, this framework relying on an anatomy-based structural network cannot account for the vulnerable locus for a specific task, lesioning of which would bring impairment. Here, we hypothesized that (i) activated PFC areas dynamically form an ordered network centered at a task-specific "functional hub" and (ii) the lesion-effective site corresponds to the "functional hub," but not to a task-invariant "structural hub." To test these hypotheses, we conducted functional magnetic resonance imaging experiments in macaques performing a temporal contextual memory task. We found that the activated areas formed a hierarchical hub-centric network based on task-evoked directed connectivity, differently from the anatomical network reflecting axonal projection patterns. Using a novel simulated-lesion method based on support vector machine, we estimated severity of impairment after lesioning of each area, which accorded well with a known dissociation in contextual memory impairment in macaques (impairment after lesioning in area 9/46d, but not in area 8Ad). The predicted severity of impairment was proportional to the network "hubness" of the virtually lesioned area in the task-evoked directed connectivity network, rather than in the anatomical network known from tracer studies. Our results suggest that PFC areas dynamically and cooperatively shape a functional hub-centric network to reallocate the lesion-effective site depending on the cognitive processes, apart from static anatomical hubs. These findings will be a foundation for precise prediction of behavioral impacts of damage or surgical intervention in human brains.

No MeSH data available.


Related in: MedlinePlus

Relationship of betweenness centrality and predicted behavioral impairment after lesioning.(A) Betweenness centrality calculated based on task-evoked connectivity (horizontal axis) and predicted impact on performance (vertical axis) for each area for each monkey are plotted as a scattergram. The green line was fitted (r = 0.53, p = 0.008). (B) Betweenness centrality calculated based on anatomical connectivity (horizontal axis) and predicted impact on performance (vertical axis) for each area for each monkey are plotted as a scattergram. The purple line was fitted (r = -0.26, p = 0.14). (C) Areas where lesions induced impairment in temporal-order judgment; data were compiled from Petrides (1991) [18]. Red-yellow color code and gray code indicate the overlap of the lesion area among six hemispheres for mid-dorsolateral prefrontal area (effective lesion area) and periarcuate area (noneffective, control lesion area), respectively. (D) Schematic illustration of interareal connections. PPIs with p < 0.01 (FDR correction) are displayed as directed edges for display purpose. Node color indicates predicted impact on performance. Node diameter represents betweenness centrality. Note that causality cannot be inferred from PPI directionality.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4488377&req=5

pbio.1002177.g006: Relationship of betweenness centrality and predicted behavioral impairment after lesioning.(A) Betweenness centrality calculated based on task-evoked connectivity (horizontal axis) and predicted impact on performance (vertical axis) for each area for each monkey are plotted as a scattergram. The green line was fitted (r = 0.53, p = 0.008). (B) Betweenness centrality calculated based on anatomical connectivity (horizontal axis) and predicted impact on performance (vertical axis) for each area for each monkey are plotted as a scattergram. The purple line was fitted (r = -0.26, p = 0.14). (C) Areas where lesions induced impairment in temporal-order judgment; data were compiled from Petrides (1991) [18]. Red-yellow color code and gray code indicate the overlap of the lesion area among six hemispheres for mid-dorsolateral prefrontal area (effective lesion area) and periarcuate area (noneffective, control lesion area), respectively. (D) Schematic illustration of interareal connections. PPIs with p < 0.01 (FDR correction) are displayed as directed edges for display purpose. Node color indicates predicted impact on performance. Node diameter represents betweenness centrality. Note that causality cannot be inferred from PPI directionality.

Mentions: We compared the predicted impact on performance after lesioning (Fig 5C) and the betweenness centrality based on task-evoked connectivity network (Fig 3E) for each area in each monkey. An analysis of covariance (ANCOVA) on predicted impact on performance after removal of each area (monkey × betweenness centrality) revealed a significant main effect of betweenness centrality of the removed area (F(1, 16) = 6.65, p = 0.02), but no significant main effect of monkey (F(1, 16) = 0.22, p = 0.64) or interaction between monkey and betweenness centrality (F(1, 16) = 0.85, p = 0.36). Moreover, we found a significant positive correlation between the betweenness centrality and the predicted impact on performance (r = 0.53, p = 0.008) (Fig 6A). These observations indicate that removal of an area with higher betweenness centrality in the task-evoked connectivity network causes a larger reduction in prediction accuracy. Contrarily, no significant correlation was observed between the betweenness centrality based on anatomical connectivity network (Fig 3I) and the predicted impact on performance (r = -0.26, p = 0.14) (Fig 6B). Even in a network containing the larger set of areas that included nonhomotopic areas (total of 39 areas; see Table 1), the predicted impact on performance correlated more highly with betweenness centrality based on task-evoked connectivity than with betweenness centrality based on anatomical connectivity (p = 8.8 × 10−17, paired t-test) (S14 Fig; see “Prediction with the Network with a Larger Set of Areas” in S1 Text). These observations suggest that severity of behavioral impairment induced by a focal lesion is predicted from the task-evoked connectivity network, but not from the anatomical connectivity network.


Dynamically Allocated Hub in Task-Evoked Network Predicts the Vulnerable Prefrontal Locus for Contextual Memory Retrieval in Macaques.

Osada T, Adachi Y, Miyamoto K, Jimura K, Setsuie R, Miyashita Y - PLoS Biol. (2015)

Relationship of betweenness centrality and predicted behavioral impairment after lesioning.(A) Betweenness centrality calculated based on task-evoked connectivity (horizontal axis) and predicted impact on performance (vertical axis) for each area for each monkey are plotted as a scattergram. The green line was fitted (r = 0.53, p = 0.008). (B) Betweenness centrality calculated based on anatomical connectivity (horizontal axis) and predicted impact on performance (vertical axis) for each area for each monkey are plotted as a scattergram. The purple line was fitted (r = -0.26, p = 0.14). (C) Areas where lesions induced impairment in temporal-order judgment; data were compiled from Petrides (1991) [18]. Red-yellow color code and gray code indicate the overlap of the lesion area among six hemispheres for mid-dorsolateral prefrontal area (effective lesion area) and periarcuate area (noneffective, control lesion area), respectively. (D) Schematic illustration of interareal connections. PPIs with p < 0.01 (FDR correction) are displayed as directed edges for display purpose. Node color indicates predicted impact on performance. Node diameter represents betweenness centrality. Note that causality cannot be inferred from PPI directionality.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4488377&req=5

pbio.1002177.g006: Relationship of betweenness centrality and predicted behavioral impairment after lesioning.(A) Betweenness centrality calculated based on task-evoked connectivity (horizontal axis) and predicted impact on performance (vertical axis) for each area for each monkey are plotted as a scattergram. The green line was fitted (r = 0.53, p = 0.008). (B) Betweenness centrality calculated based on anatomical connectivity (horizontal axis) and predicted impact on performance (vertical axis) for each area for each monkey are plotted as a scattergram. The purple line was fitted (r = -0.26, p = 0.14). (C) Areas where lesions induced impairment in temporal-order judgment; data were compiled from Petrides (1991) [18]. Red-yellow color code and gray code indicate the overlap of the lesion area among six hemispheres for mid-dorsolateral prefrontal area (effective lesion area) and periarcuate area (noneffective, control lesion area), respectively. (D) Schematic illustration of interareal connections. PPIs with p < 0.01 (FDR correction) are displayed as directed edges for display purpose. Node color indicates predicted impact on performance. Node diameter represents betweenness centrality. Note that causality cannot be inferred from PPI directionality.
Mentions: We compared the predicted impact on performance after lesioning (Fig 5C) and the betweenness centrality based on task-evoked connectivity network (Fig 3E) for each area in each monkey. An analysis of covariance (ANCOVA) on predicted impact on performance after removal of each area (monkey × betweenness centrality) revealed a significant main effect of betweenness centrality of the removed area (F(1, 16) = 6.65, p = 0.02), but no significant main effect of monkey (F(1, 16) = 0.22, p = 0.64) or interaction between monkey and betweenness centrality (F(1, 16) = 0.85, p = 0.36). Moreover, we found a significant positive correlation between the betweenness centrality and the predicted impact on performance (r = 0.53, p = 0.008) (Fig 6A). These observations indicate that removal of an area with higher betweenness centrality in the task-evoked connectivity network causes a larger reduction in prediction accuracy. Contrarily, no significant correlation was observed between the betweenness centrality based on anatomical connectivity network (Fig 3I) and the predicted impact on performance (r = -0.26, p = 0.14) (Fig 6B). Even in a network containing the larger set of areas that included nonhomotopic areas (total of 39 areas; see Table 1), the predicted impact on performance correlated more highly with betweenness centrality based on task-evoked connectivity than with betweenness centrality based on anatomical connectivity (p = 8.8 × 10−17, paired t-test) (S14 Fig; see “Prediction with the Network with a Larger Set of Areas” in S1 Text). These observations suggest that severity of behavioral impairment induced by a focal lesion is predicted from the task-evoked connectivity network, but not from the anatomical connectivity network.

Bottom Line: We found that the activated areas formed a hierarchical hub-centric network based on task-evoked directed connectivity, differently from the anatomical network reflecting axonal projection patterns.Our results suggest that PFC areas dynamically and cooperatively shape a functional hub-centric network to reallocate the lesion-effective site depending on the cognitive processes, apart from static anatomical hubs.These findings will be a foundation for precise prediction of behavioral impacts of damage or surgical intervention in human brains.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, The University of Tokyo School of Medicine, Hongo, Bunkyo-ku, Tokyo, Japan; Department of Physiology, Juntendo University School of Medicine, Hongo, Bunkyo-ku, Tokyo, Japan.

ABSTRACT
Neuroimaging and neurophysiology have revealed that multiple areas in the prefrontal cortex (PFC) are activated in a specific memory task, but severity of impairment after PFC lesions is largely different depending on which activated area is damaged. The critical relationship between lesion sites and impairments has not yet been given a clear mechanistic explanation. Although recent works proposed that a whole-brain network contains hubs that play integrative roles in cortical information processing, this framework relying on an anatomy-based structural network cannot account for the vulnerable locus for a specific task, lesioning of which would bring impairment. Here, we hypothesized that (i) activated PFC areas dynamically form an ordered network centered at a task-specific "functional hub" and (ii) the lesion-effective site corresponds to the "functional hub," but not to a task-invariant "structural hub." To test these hypotheses, we conducted functional magnetic resonance imaging experiments in macaques performing a temporal contextual memory task. We found that the activated areas formed a hierarchical hub-centric network based on task-evoked directed connectivity, differently from the anatomical network reflecting axonal projection patterns. Using a novel simulated-lesion method based on support vector machine, we estimated severity of impairment after lesioning of each area, which accorded well with a known dissociation in contextual memory impairment in macaques (impairment after lesioning in area 9/46d, but not in area 8Ad). The predicted severity of impairment was proportional to the network "hubness" of the virtually lesioned area in the task-evoked directed connectivity network, rather than in the anatomical network known from tracer studies. Our results suggest that PFC areas dynamically and cooperatively shape a functional hub-centric network to reallocate the lesion-effective site depending on the cognitive processes, apart from static anatomical hubs. These findings will be a foundation for precise prediction of behavioral impacts of damage or surgical intervention in human brains.

No MeSH data available.


Related in: MedlinePlus