Schematic depiction of differences between low-IQ and high-IQ individuals with regard to brain volume, neurite density, and arborization of dendritic trees within the cortex. High-IQ individuals are likely to possess more cortical volume than low-IQ individuals, which is indicated by differently sized brains (left side) and differently sized panels showing exemplary magnifications of neuron and neurite microstructure (right side). The difference in cortical volume is highlighted by the shadow around the upper brain. Due to their larger cortices, it is conceivable that high-IQ individuals benefit from the processing power of additional neurons, which are marked by the dotted line in the lower panel. The cerebral cortex of high-IQ individuals is characterized by a low degree of neurite density and orientation dispersion, which is indicated by smaller and less ramified dendritic trees in the respective panel. Intellectual performance is likely to benefit from this kind of microstructural architecture since restricting synaptic connections to an efficient minimum facilitates the differentiation of signals from noise while saving network and energy resources. Neurons and neurites are depicted in black and gray to create a sense of depth. Please note, this depiction does not correspond to the actual magnitude of effect sizes reported in the study. For the purpose of an easier visual understanding, differences in both macrostructural and microstructural brain properties are highly accentuated 

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... data as well as data provided by the Human Connectome Project 25 revealed an expected positive association between cor- tical volume and intelligence, corrected for age, sex, and colli- nearity. It is a well-established and consistent observation that cognitive abilities are related to brain volume, especially the volume of the cerebral cortex 1,4,5 . The biological explanation for this structure-function relationship is usually derived from the fact that individuals with more cortical volume possess a higher number of neurons 7,8 and thus more computational power to engage in logic reasoning (Fig. 4). However, the major aim of our study was to investigate the microstructural architecture of the cortex by closely analyzing the diffusion characteristics of den- drites and ...

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... of synaptic and dendritic growth and pruning have grave consequences with regard to cognitive performance 46 For example, reduced synaptic pruning results in an excess of synapses, which is associated with pathologies characterized by low intelligence including Down's syndrome 47,48 . An increase in synapses may also cause failure in differentiating signals from noise, reducing network efficiency 49 . Indeed, computational stu- dies show that synaptic pruning increases learning and processing speed, and saves network and energy resources 50 , by requiring less computation to learn relations between data sets 51 . These observations are in line with the results obtained from both our experimental data and validation data from the Human Con- nectome Project 25 . We found that both INVF Cortex and ODI Cortex , representing neurite density and orientation dispersion in the cerebral cortex, were negatively associated with intelligence. Since both markers are closely related to the amount of synaptic con- nections, our findings provide the first evidence of specific microstructural brain correlates facilitating efficient information processing as measured by intelligence (Fig. 4). This supports the neural efficiency hypothesis of intelligence [10][11][12] . In the original PET study of neural efficiency 52 , researchers examined two samples of low-IQ individuals, including patients suffering from Down's syndrome and another form of mental retardation, as well as a control group of individuals with average intelligence. They found that both low-IQ groups exhibited higher rates of cortical glucose metabolism compared to the healthy control participants while working on Raven's Advanced Progressive Matrices 9,53 . They attributed their observations to a failure of neural pruning in the brains of low-IQ individuals 13,52 . It is very important to note that these researchers were restricted to a pathological sample when proposing a biological foundation for the neural efficiency hypothesis of intelligence. Given the lack of suitable post mortem data or practical in vivo methods to obtain information about cortical microstructure, they examined indi- viduals that were known to have dendritic trees with a very dis- tinct microstructure, i.e., patients with Down's syndrome. However, evidence from a clinical sample is prone to influence by various confounding factors. Therefore, one should proceed with utmost care when generalizing these findings to our results, which were obtained from healthy individuals in the range of average intelligence. Nevertheless, there is some evidence from healthy subjects to support the idea that interindividual differences in intelligence are associated with different levels of cortical activation during rea- soning. For example, early EEG studies showed that high-IQ individuals, when working on an elementary cognitive task, dis- play an event-related desynchronization (ERD) limited to cortical areas required for the task 54 . In contrast, low-IQ individuals were characterized by an ERD that was spread across a wide range of cortical areas. We hypothesize that this evidence of unfocused cortical activity was associated with redundant neuronal circuits in the form of expendable dendrites in the cortex. In another EEG Schematic depiction of differences between low-IQ and high-IQ individuals with regard to brain volume, neurite density, and arborization of dendritic trees within the cortex. High-IQ individuals are likely to possess more cortical volume than low-IQ individuals, which is indicated by differently sized brains (left side) and differently sized panels showing exemplary magnifications of neuron and neurite microstructure (right side). The difference in cortical volume is highlighted by the shadow around the upper brain. Due to their larger cortices, it is conceivable that high-IQ individuals benefit from the processing power of additional neurons, which are marked by the dotted line in the lower panel. The cerebral cortex of high-IQ individuals is characterized by a low degree of neurite density and orientation dispersion, which is indicated by smaller and less ramified dendritic trees in the respective panel. Intellectual performance is likely to benefit from this kind of microstructural architecture since restricting synaptic connections to an efficient minimum facilitates the differentiation of signals from noise while saving network and energy resources. Neurons and neurites are depicted in black and gray to create a sense of depth. Please note, this depiction does not correspond to the actual magnitude of effect sizes reported in the study. For the purpose of an easier visual understanding, differences in both macrostructural and microstructural brain properties are highly accentuated study by Walhovd et al. 30 the authors demonstrated that the latency of the ERP component P3a, as a measure of speed-of- processing, was negatively correlated with intelligence. Again, these findings can be interpreted in terms of neural efficiency and correspond to the results presented in our study. Future studies utilizing both structural and functional techniques will be critical in determining whether a higher degree of neurite density and orientation dispersion could slow cortical speed-of-processing due to inefficient circuitry, thus having a negative effect on ...

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... this model, intelligence was regressed on age, sex, and all brain properties included in the partial correlation analysis. The regression model for sample S259 was significant (R² = 0.14, F (10, 248) = 3.86, p < 0.01) and yielded significant regression coefficients for INVF Cortex (β = −0.22, p < 0.05) and ODI Cortex (β = −0.19, p < 0.05). The regression coefficient for VOL Cortex was INVFCortex = intra-neurite volume fraction representing neurite density in the cortex, INVFWM = intra-neurite volume fraction representing neurite density in the white matter, ODICortex = orientation dispersion index of neurites in the cortex, ODIWM = orientation dispersion index of neurites in the white matter, ISOCortex = isotropic diffusion in the cortex, ISOWM = isotropic diffusion in the white matter, VOL Cortex = cortical volume, VOL WM = white matter volume; Sex was represented as a dummy variable with males being labeled 0 and females 1; *p < 0.05 Partial correlation analyses with data from sample S259 quantifying structure-function associations at the whole-brain level. Scatter plots illustrating the relationship between neurite density and intelligence are depicted in the left column. Scatter plots illustrating the relationship between neurite orientation dispersion and intelligence are depicted in the right column. In all cases, microstructural measures were computed as mean values derived from the overall cortex (upper row) or white matter (lower row), respectively. Results are based on partial correlation analyses with age and sex being used as controlling variables. Statistically significant partial correlation coefficients (N = 259, p < 0.05) are highlighted with black boxes of comparable magnitude but failed to reach statistical signifi- cance (β = 0.22, p = 0.08) ( Table 1 and Supplementary Fig. 4). Nevertheless, these results generally confirmed the pattern revealed by the partial correlation analysis and indicate that the two microstructural brain properties, INVF Cortex and ODI Cortex , contribute to the prediction of intelligence independently. Furthermore, we observed no significant associations between intelligence and the remaining predictors ISO Cortex , INVF WM , ODI WM , ISO WM , VOL WM , age, and sex. It is conceivable that intelligence might be associated with study compliance in such a way that low-IQ individuals show more unwanted head move- ments during the MRI examination. This in turn might distort the estimated magnitude of certain brain properties and hence affect the outcome of the aforementioned multiple regression analysis. However, in the S259 sample, intelligence was not significantly correlated with head motion measured during the diffusion-weighted scan (r = −0.03, p = 0.69). Consequentially, adding head motion as a covariate to the multiple regression analysis did not alter the respective results in any substantial way (Supplementary Table ...

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... are based on partial correlation analyses with age and sex being used as controlling variables. Statistically significant partial correlation coefficients (N = 259, p < 0.05) are highlighted with black boxes of comparable magnitude but failed to reach statistical signifi- cance (β = 0.22, p = 0.08) ( Table 1 and Supplementary Fig. 4). Nevertheless, these results generally confirmed the pattern revealed by the partial correlation analysis and indicate that the two microstructural brain properties, INVF Cortex and ODI Cortex , contribute to the prediction of intelligence independently. ...

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... cognitive abilities are related to brain volume, especially the volume of the cerebral cortex 1,4,5 . The biological explanation for this structure-function relationship is usually derived from the fact that individuals with more cortical volume possess a higher number of neurons 7,8 and thus more computational power to engage in logic reasoning (Fig. 4). However, the major aim of our study was to investigate the microstructural architecture of the cortex by closely analyzing the diffusion characteristics of den- drites and ...

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... density and orientation dispersion in the cerebral cortex, were negatively associated with intelligence. Since both markers are closely related to the amount of synaptic con- nections, our findings provide the first evidence of specific microstructural brain correlates facilitating efficient information processing as measured by intelligence (Fig. 4). This supports the neural efficiency hypothesis of intelligence [10][11][12] . In the original PET study of neural efficiency 52 , researchers examined two samples of low-IQ individuals, including patients suffering from Down's syndrome and another form of mental retardation, as well as a control group of individuals with average ...