In the main text, we present our findings using the region parcellation and include the region results in the Supporting Information, as the findings were largely preserved across scales. In Fig 1b , we present the structural anatomical network of an individual as a weighted connectivity matrix whose entries represent the strength of the connection between two brain regions. The dynamics of each brain region are modeled using nonlinear Wilson-Cowan oscillators WCOs , coupled through a subject-specific anatomical brain network Materials and methods.
A WCO is a biologically motivated model of local brain activity, developed to describe the mean behavior of small neuronal populations [ 47 ]. As this parameter crosses a threshold, a sudden transition to an excited state is observed as marked by the orange arrow. By holding the mathematical modeling of regional brain dynamics constant across individuals but allowing the underlying subject-specific structural connectivity matrix, A , to vary, the model provides a causal link between differences in the structural organization of the brain and differences in the resulting simulated brain dynamics.
The global strength of the coupling between different brain regions via A is controlled by a global coupling parameter c 5 ; Materials and methods. The dynamical state of the brain can be tuned by varying this parameter as shown in Fig 1c , which depicts the average excitatory dynamics as a function of global coupling. When the global coupling parameter exceeds a threshold value , the brain dynamics abruptly transition to an active state that is characterized by the oscillatory activity of Fig 1d.
Mathematically, this equates to oscillators switching from hovering near a fixed point to jumping to a limit cycle. See [ 45 ] for more details about this transition. Previous work has shown that the computational model is particularly sensitive to the point at which model dynamics undergo this transition: the value of at which the transition takes places is subject-specific, and the inter-subject variability in is greater than that seen using anatomical networks derived from different scans of the same individual [ 45 ].
The transition value can be thought of as a proxy for the global excitability of the brain. Brains with a lower transition value require less external input e.
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Here, we use this as a parameter to measure differences in global network dynamics between individuals. Because we are interested in linking variability in brain structure and behavior, we first measured the extent of individual variability in anatomical connectivity, simulated brain activity, and task performance in our data set. Across our cohort of subjects, we observed measurable variability in anatomical network structure as seen in Fig 2a , which shows the standard deviation in edge weights between two brain regions, normalized by the mean edge weight.
This structural variability is also manifested in the simulated brain activity depicted in Fig 2b and 2c. We observe variability across individuals in the specific patterns of brain activity in the active state Fig 2b as well as in the transition values Fig 2c. Since the nonlinear WCOs are all identical in the model, these observed differences in simulated brain activity are a direct result of variation in the underlying anatomical connectivity.
In Fig 2d we show the spatial map of the average regional variability in structural connectivity and functional activity across individuals.
A Data-Driven Approach
Interestingly, we observe a diverse range of regional variation in functional dynamics that does not necessarily match the pattern or level of structural variation. Although there are few regions of low variability blue , there are a significant number of regions with moderate to high variability green to yellow.
Each column represents the temporal average of the excitatory activity across brain regions for a given individual in the excited state and is unique in terms of the overall activity pattern. Finally, we assessed the extent of variability in the cognitive performances of individuals across three language-demanding tasks: i verb-generation VG ; ii sentence-completion SC ; and iii number-reading NR. It is therefore expected that external stimulation to this region should affect task performance. However, it is important to note that while subject performance on a task did often change after TMS, we did not consistently observe an improvement or degradation in task performance across individuals, nor was the effect consistent between tasks within a given subject.
This suggests that although the L-IFG plays an important role in the context of language comprehension, the actual cognitive response reflects contributions from a larger part of the brain network.senjouin-renshu.com/wp-content/79/3228-bajar-programa.php
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Given the observed variability in the structural, dynamical, and behavioral aspects of our data, we next focused on assessing how this variability was related across these three domains. We therefore examined network features measured at both the global network level using the entire brain network and within task-specfic subnetworks that were selected to represent the specific circuitry involved in task completion and asked how these measures related to task performance.
We first examined the relationship between global network properties and task performance by estimating the correlation between global brain activation and task performance. Thus for the SC task, individuals with a lower value of more easily excitable brain are likely to perform better as measured through a short response time. However, we did not observe a significant correlation in the verb generation VG or number reading NR tasks, indicating that the performance of these tasks cannot be predicted by a global network property.
To ensure that these results were dependent upon the organization of the subject-specific anatomical connectivity used as a basis for the computational model, for each individual, we created randomized brain networks by preserving the distribution of edge weights but randomly reassigning connection strengths between brain regions Materials and methods.
We recalculated the values for simulations using these randomized connectivity matrices, but did not observe any significant correlations between transition values and task performance in this case. Task performance versus model transition values, , for three tasks: a verb generation VG , b sentence completion SC , and c number reading NR. The red lines represent a linear fit to the data for visual guidance.
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The corresponding Pearson correlation coefficients are given in Table 1. To further explore the link between global brain dynamics and behavior, we additionally assessed the relationship between specific patterns of brain activity and task performance. Since the L-IFG is involved in controlled language processing [ 54 — 56 ], one can argue that the pattern of brain synchronization as a result of targeted stimulation to the region might also be predictive of task performance.
We therefore computationally stimulated the brain regions comprising the L-IFG Fig 4a , and quantified how the stimulation spread throughout the global brain network see Materials and methods for details. As shown in Fig 4a , computational stimulation pushes the dynamics of the region into oscillatory activity which then drives the functional dynamics of other brain regions through the underlying structural connections.
2. Theoretical Framework
We measure the resulting pattern of brain activity by calculating the pairwise functional connectivity using the maximum normalized correlation between brain regions [ 45 , 57 , 58 ] Materials and methods. In Fig 4b , we show the spatial mapping of the variability in average functional connectivity across subjects measured as the standard deviation divided by the mean resulting from computational stimulation of the L-IFG, and in Fig 4c , we show the subject-specific patterns of functional connectivity for three subjects.
Due to the variability in the underlying anatomical connectivity matrices, the resulting patterns of functional connectivity differ between individuals. We measure the extent of the global spread of synchronization by calculating the functional effect [ 45 ] which measures the average value of synchronization across the entire brain Materials and methods.
Interestingly, unlike our observation with the global coupling parameter, the global functional effect does not show a significant correlation with task performance for any of the cognitive tasks Table 1. Computational stimulation of a single brain region pushes its dynamics into a limit cycle oscillations. Variability is measured as the standard deviation in regional functional connectivity across subjects divided by mean. Note the variation in the observed connectivity patterns across subjects. While we did observe a significant correlation between the global threshold value and task performance for the SC task, we saw no correlations between global brain dynamics and task performance in the remaining two tasks, and the global functional effect was not correlated with performance in any of the tasks.
However, the three language tasks performed in this study differ in semantic demands, and the absence of a significant correlation for the VG and NR tasks could be due to either a drastically different cognitive mechanism for performing these tasks, or the dependence of these tasks on a more localized brain circuit. To investigate the latter possibility, we assessed the role of task-specific subnetworks in task performance.
To construct a task dependent, spatially localized measure, we follow the work of Roux et al. Roux et al.
This approach provides a much more direct and causal form of evidence that implicates the role of specific brain regions in the behaviors of interest as compared to anatomical or functional networks based on task-related activity measured using fMRI, which are indirect measures of cognitive circuits. We mapped these regions to the Lausanne atlas and constructed two task circuits: one involved in VG and SC alphabets-related, Fig 5a , and one involved in NR number-related, Fig 5b. We found that these circuits are also consistent with other studies mapping brain regions involved in language processing [ 53 , 60 — 62 ].
Note that both of these sub-networks are contained entirely in the left hemispheric language network. Brain regions in the circuits are 1: pars orbitalis , 2: pars triangularis , 3: pars opercularis-1 , 4: pars opercularis-2 , 5: superiofrontal-9 , 6: caudal middle frontal-1 , 7: postcentral-7 , supramarginal , inferioperietal-3 , fusiform-2 and 3 , inferio temporal-2 and 3 , temporal pole , middle temporal , superior temporal. We then calculated the functional effect within these task circuits averaging the functional connectivity values only within the subnetwork as opposed to the entire brain network as done previously and correlated this local measure with task performances Fig 5c—5e and Table 1.
Our results indicate that individuals with a lower functional effect less synchronization within the task circuit also have a lower response time better performance. When synchronization within the task circuit is increased a high functional effect , task performance degrades, suggesting that high levels of synchronized activity within the task circuit could potentially impede the ability of the circuit to perform localized computations necessary for task completion.
If we compare the functional effect measured only within brain regions outside of the task circuit, the significant correlation with the NR task is lost Table 1 , indicating the specificity of the task circuit in the model. Although we also observed a significant correlation between the task-specific functional effect and task performance for the SC task, this result is driven by a single subject and does not hold if this subject is removed from the data set.
Performing the analysis on a larger data set would therefore be necessary to confirm this finding for the SC task. No significant correlations were observed for the VG task. To validate the specificity of our selected task circuits, we constructed 10, random sub-networks by randomly selecting the same number of brain regions as in each task circuit and then calculated the functional effect within these random circuits after stimulating the L-IFG Materials and methods. We observed that only 1. This low error rate signifies that the observed significant correlation in the NR task is due to the selection of brain regions in the task-specific circuit.
We also verified that the observed effects were related to our choice of stimulating the L-IFG as opposed to some other brain structure. We chose different sets of brain regions equal in size to the number of brain regions that compose the L-IFG that were randomly distributed within the task circuit and applied targeted computational stimulation to these randomly selected regions.