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Showing content from https://pubmed.ncbi.nlm.nih.gov/24585433/ below:

Efficiency of weak brain connections support general cognitive functioning

. 2014 Sep;35(9):4566-82. doi: 10.1002/hbm.22495. Epub 2014 Mar 2. Efficiency of weak brain connections support general cognitive functioning

Affiliations

Affiliation

¹ Department of Medicine, Surgery and Neuroscience, Neurology and Neurophysiology Section, University of Siena, Siena, Italy.

PMID: 24585433
PMCID: PMC6869093
DOI: 10.1002/hbm.22495

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Efficiency of weak brain connections support general cognitive functioning

Emiliano Santarnecchi et al. Hum Brain Mapp. 2014 Sep.

. 2014 Sep;35(9):4566-82. doi: 10.1002/hbm.22495. Epub 2014 Mar 2. Affiliation

¹ Department of Medicine, Surgery and Neuroscience, Neurology and Neurophysiology Section, University of Siena, Siena, Italy.

PMID: 24585433
PMCID: PMC6869093
DOI: 10.1002/hbm.22495

Item in Clipboard

Abstract

Brain network topology provides valuable information on healthy and pathological brain functioning. Novel approaches for brain network analysis have shown an association between topological properties and cognitive functioning. Under the assumption that "stronger is better", the exploration of brain properties has generally focused on the connectivity patterns of the most strongly correlated regions, whereas the role of weaker brain connections has remained obscure for years. Here, we assessed whether the different strength of connections between brain regions may explain individual differences in intelligence. We analyzed-functional connectivity at rest in ninety-eight healthy individuals of different age, and correlated several connectivity measures with full scale, verbal, and performance Intelligent Quotients (IQs). Our results showed that the variance in IQ levels was mostly explained by the distributed communication efficiency of brain networks built using moderately weak, long-distance connections, with only a smaller contribution of stronger connections. The variability in individual IQs was associated with the global efficiency of a pool of regions in the prefrontal lobes, hippocampus, temporal pole, and postcentral gyrus. These findings challenge the traditional view of a prominent role of strong functional brain connections in brain topology, and highlight the importance of both strong and weak connections in determining the functional architecture responsible for human intelligence variability.

Keywords: brain connectivity; comparative psychology; fMRI; functional connectivity; graph theory; intelligence; resting state.

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Figures
Figure 1

Results of the clustering procedure.…

Figure 1

Results of the clustering procedure. Average values of Intelligence scores and brain morphometry…

Figure 1

Results of the clustering procedure. Average values of Intelligence scores and brain morphometry for the three IQ groups resulting from the two‐step clustering procedure (Akaike's Information Criterion, log‐likelihood). The results confirmed a good separation between High‐IQ, Average‐IQ, and Low‐IQ subjects for the main IQ scores (FSIQ, PIQ, VIQ) and subtests, whereas no significant difference emerged for total brain, gray matter, white matter, and CSF volumes. White boxes represent the 3th quintile (central line) as well the 2th and 4th ones (left and right edges) of the overall sample distribution. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com .]

Figure 2

Preprocessing, thresholding, and graph‐topological properties…

Figure 2

Preprocessing, thresholding, and graph‐topological properties computation workflow. Schematic representation of the major steps…

Figure 2

Preprocessing, thresholding, and graph‐topological properties computation workflow. Schematic representation of the major steps for network topology computation, involving images preprocessing, the thresholding procedure based on the connectivity strength and topology indices computation. From left to right, images underwent canonical preprocessing involving two different approaches for motion correction, removal of possible confounding factor related to breathing and cardiac signals, temporal band‐pass filtering, coregistration, and spatial normalization using the DARTEL module for SPM. Four different atlases (2 functional, 2 anatomical) were used for resting‐state parcellation into regions of interest and consequent BOLD signal time series extraction. A one‐sample t test was applied over resulting connectivity matrices to retain only significant connections, which were used to define several matrices based on connectivity strength and representing strong, intermediate, and weak brain connections (windowed thresholding, upper row). A few matrices obtained at identical sparsity values with the windowed and cumulative thresholding approaches are shown to highlight the different representation of brain connectivity resulting from the procedures. To normalize graph topology indices, an Hirschberger‐Qi‐Steuer algorithm was used to create transitivity preserved null networks based on the random correlation matrices matched for degree‐distribution. Considering our focus on connectivity strength distribution, all previous steps including matrix thresholding were performed at the single subject level. Additional details are provided in Methods. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com .]

Figure 3

Inter‐regional connectivity distribution and regression…

Figure 3

Inter‐regional connectivity distribution and regression analysis results. (a) The five panels show the…

Figure 3

Inter‐regional connectivity distribution and regression analysis results. (a) The five panels show the distribution of pairwise connectivities over the whole sample (n = 98). For illustrative purposes, weighted adjacency matrices referring to each sparsity window were binarized (i − j = 1 if connection is present, 0 otherwise), and summed together across subjects. Thus, each i – j value in the resulting matrices represents the number of participants reporting that connection, leading to a graphical plot of the most representative connections for each sparsity window. Color bar represents the number of connections/edges. For example, the Q1 (sparsity 1–20%) shows an edge distribution ranging from not present (deep blue) to present in all participants (n = 98, dark red). It must be noticed that the original weighted matrices were composed of pairwise connections which survived a one‐sample t test analysis performed at the individual level (P < 0.05 FDR corrected). This test was performed to restrict the subsequent analyses on the most reliable connections. (b) Amount of intellectual functioning variance explained (adjusted R ²) by E values within each connectivity window. Regression equations built using E values led to the explanation of 37.5% of interindividual differences in the FSIQ. Results of the VIQ and the PIQ are not shown as they exhibit the same pattern with the function explaining 32 and 34% of interindividual variance in verbal and performance abilities, respectively. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com .]

Figure 4

Global efficiency differences within strong…

Figure 4

Global efficiency differences within strong and weak brain connections. The figure shows the…

Figure 4

Global efficiency differences within strong and weak brain connections. The figure shows the mean E values of participants included in the High, Average and Low‐IQ groups. For illustrative purposes, panels (a) and (b) report E values computed using the cumulative or windowed thresholding procedure, respectively. A statistically significant difference between High and Average‐Low IQ subjects in the Q4 window is evident (sparsity 61–80%; boundaries: lower r c ⇓ = 0.18 ± 0.12; upper r c ⇓ = 0.32 ± 0.11; *P = 0.003). Moreover, a weaker difference is present for E values representing the first 20% of stronger brain connections (Q1; boundaries: lower r c ⇓ = 0.53 ± 0.08; upper r c ⇓ = 0.75 ± 0.03). In this case, topological properties are identically captured by the cumulative and windowed solutions.

Figure 5

Topological organization of strong and…

Figure 5

Topological organization of strong and weak connections. Panel (a) reports the most represented…

Figure 5

Topological organization of strong and weak connections. Panel (a) reports the most represented pairwise connections (edges >95th percentile) across all subjects (n = 98) for the Q1 (1–20%, upper row) and the Q4 windows (61–80%, lower row), respectively. Shown from left to right are coronal, right and left sagittal, and axial views. Color‐coding corresponds to short (red, <50 mm) and long (green, >50 mm) connections. As evident from the figure, Q1 showed prevalent interhemispheric, short and long connections, while Q4 long distance, inter‐intrahemispheric balanced projections. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com .]

Figure 6

Most discriminative brain regions for…

Figure 6

Most discriminative brain regions for IQ groups separation. The figure shows the results…

Figure 6

Most discriminative brain regions for IQ groups separation. The figure shows the results of the multivariate pattern classification procedure performed between IQ groups (correct classification rate of 88.25%), using a support vector machine (SVM) algorithm on E values referring to the Q4 sparsity window. Acknowledged the multivariate nature of classification results, this graphic representation shows brain regions that carry most of the discriminative weight between IQ levels (>95th percentile), suggesting that these are the brain areas whose E values mostly contributed to the decision boundary. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com .]

Figure 7

Most discriminative functional connections. The…

Figure 7

Most discriminative functional connections. The figure shows the results of the multivariate pattern…

Figure 7

Most discriminative functional connections. The figure shows the results of the multivariate pattern classification procedure performed between IQ groups (correct classification rate of 72.19%), using a support vector machine (SVM) algorithm on correlation coefficient values referring to pairwise connections (i) within the Q4 sparsity window and (ii) encompassing those regions identified in the SVM classification based on regional E values (see Fig. 5). This graphic representation shows functional connections that carry most of the discriminative weight between High‐IQ and Low‐IQ subjects (>95th percentile). In order to ease visualization, connections have been shown separately for each of the eight regions. Legends: nodes' color refer to different brain lobes: Light Blue = frontal component of Default Mode Network; Red = Sensorimotor and Temporal lobe/auditory network; Green = visual network; Purple = basal ganglia network; Dark Blue = dorsal attention network.