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@INPROCEEDINGS{Weis:863319,
      author       = {Weis, Susanne and Patil, Kaustubh and Hoffstaedter, Felix
                      and Eickhoff, Simon},
      title        = {{R}egional brain connectivity patterns distinguish males
                      from females},
      reportid     = {FZJ-2019-03399},
      year         = {2019},
      abstract     = {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.},
      month         = {Jun},
      date          = {2019-06-09},
      organization  = {2019 Annual Meeting of the
                       Organization of Human Brain Mapping,
                       Rom (Italy), 9 Jun 2019 - 13 Jun 2019},
      subtyp        = {Other},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571)},
      pid          = {G:(DE-HGF)POF3-571},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/863319},
}