This title appears in the Scientific Report :
2019
Please use the identifier:
http://hdl.handle.net/2128/22415 in citations.
Regional brain connectivity patterns distinguish males from females
Regional brain connectivity patterns distinguish males from females
A large amount of research has suggested sex differences in functional brain organization (Cahill, 2006). The present study aimed to elucidate the brain basis of these differences by showing that the connectivity patterns of specific brain regions during resting state are sufficiently distinct to fa...
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Personal Name(s): | Weis, Susanne (Corresponding author) |
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Patil, Kaustubh / Hoffstaedter, Felix / Eickhoff, Simon | |
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Imprint: |
2019
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Conference: | 2019 Annual Meeting of the Organization of Human Brain Mapping, Rom (Italy), 2019-06-09 - 2019-06-13 |
Document Type: |
Poster |
Research Program: |
Connectivity and Activity |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
A large amount of research has suggested sex differences in functional brain organization (Cahill, 2006). The present study aimed to elucidate the brain basis of these differences by showing that the connectivity patterns of specific brain regions during resting state are sufficiently distinct to facilitate sex classification with high accuracy. When assessing classification accuracies separately for males and females, relatively lower accuracies in one sex as opposed to the other imply more varied connectivity patterns across that sex group, which makes classification difficult for that sex. Similarly, higher accuracy for one sex can be taken to imply a more typical connectivity pattern within that sex. Thus, by comparing sex-specific accuracies in classification based on regionally specific connectivity patterns, we aimed to identify key brain regions that underlie sex differences in functional brain organization. A sample comprising 744 subjects (372 male, age range: 22-37, mean age: 28.5 years) was constructed from the data provided by the Human Connectome Project (HCP S1200 release, (Van Essen, 2012)). Males and females were matched for age, twin-status and education. The FIX-Denoised fMRI dataset comprised 1200 functional volumes in MNI space per subject in a resting state (Siemens Skyra 3T scanner, TR=720ms) (Salimi-Khorshidi et al., 2014)). Individual resting state connectomes were created based on 400 ROIs from the Schaefer whole-cortex parcellation (Schaefer, 2017) in combination with 36 subcortical parcels from the Brainnetome atlas (Fan et al., 2016). For each of the 436 parcels individually, connectivity patterns with all other brain parcels were employed as features for non-linear SVM analyses (Chang & Lin, 2011) to train individual models for classification of the subject’s sex from their spatially specific connectome. Classification accuracies, determined using 10-fold cross-validation, were computed individually for each parcel. Then, for each parcel, accuracies for males and females were computed as the number of correctly classified males / females divided by the total number of males / females. For all brain parcels, classification accuracies for males (mAcc) and females (fAcc) were above chance (range mAcc = (65.0%: 79.0%), fAcc = (61.7%,82.1%)). Averaged across all parcels, accuracies did not differ significantly between the sexes (mean mAcc: 72.8%, S.D. 2.5%; mean fACC: 73.1%, S.D. 3.5%; t = 1.26; p > 0.05). Regions displaying significantly higher classification accuracies (χ2(1) > 7.75, p < 0.005) for males compared to females were located in bilateral pre- and postcentral gyri and the right occipital lobe. Meta-analytical characterization (Fox et al., 2014) of these regions associated them with language functions and speech execution. Significantly higher accuracy for females was identified in one parcel in right superior and medial orbital gyrus, which was associated with emotional processing of reward. Across most parts of the brain, classification was achieved with similar accuracies for males and females, indicating that, for most brain regions, connectivity patterns are both typical within sex and distinctive enough to enable successful sex classification. However, in a specific subset of regions which were associated with speech and language functions, classification accuracies were significantly lower for females, indicating a more varied connectivity pattern in comparison with males. More complex connectivity patterns for language related areas in females might indicate a larger variety of cognitive strategies, which, in turn, might form the basis for the female advantage in verbal processing that has repeatedly been shown in behavior and functional brain activation studies (Clements et al., 2006). On the other hand, areas associated with emotional processing of reward appear to show a more typical connectivity pattern in females, which might go along with more efficient emotion regulation strategies in females (McRae et al., 2008).Cahill, L. Why sex matters for neuroscience. Nat Rev Neurosci. 2006 Jun;7(6):pp 477-84.Van Essen, D.C. (2012), ‘The Human Connectome Project: a data acquisition perspective’, NeuroImage, 62, pp. 2222-2231.Salimi-Khorshidi, G, Douaud, G., Beckmann, C.F., Glasser, M.F., Griffanti, L., Smith S.M. (2014), ‘Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers’, NeuroImage, 90, pp. 449-468. Schaefer, A. (2017), ‘Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI’, Cerebral Cortex, 18, pp. 1-20.Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., Yang, Z., Chu, C., Xie, S., Laird, A.R., Fox, P.T., Eickhoff, S.B., Yu, C., Jiang, T. (2016). The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cereb Cortex, 26(8), pp. 3508-3526.Chang , C.C., Lin, C.J. (2011), ‘LIBSVM : a library for support vector machines’, ACM Transactions on Intelligent Systems and Technology, 2, 27, pp. 1-27.Fox, P. T., Lancaster, J. L., Laird, A. R., & Eickhoff, S. B. (2014). Meta-analysis in human neuroimaging: computational modeling of large-scale databases. Annu Rev Neurosci, 37, 409-434. doi:10.1146/annurev-neuro-062012-170320.Clements, A. M., Rimrodt, S. L., Abel, J. R., Blankner, J. G., Mostofsky, S. H., Pekar, J. J., Denckla, M.B., Cutting, L. E. (2006).Sex differences in cerebral laterality of language and visuospatial processing. Brain Lang, 98(2), pp 150-158.McRae, K., Ochsner, K.N., Mauss, I.B., Gabrieli, J.J.D., Gross, J.J. (2008). Gender Differences in Emotion Regulation: An fMRI Study of Cognitive Reappraisal.Process Intergroup Relat. 2008 Apr;11(2):143-162. |