To contribute to this field of research further, we set out to examine the sexual dimorphism of BA 44 and BA 45 using a state‐of‐the‐art brain mapping technique elegantly combining MRI‐based signal intensities and cytoarchitectonically defined maps (Amunts et al., 1999 ; Zilles and Amunts, 2010 ; Luders et al., 2013 ; Kurth et al., 2015a ). This approach avoids the pitfalls of using highly variable gross anatomic landmarks, such as sulci and gyri (which may have only little functional correspondence), to define BA 44 and BA 45, as discussed elsewhere (Luders et al., 2013 ; Kurth et al., 2015a ). Instead, it relies on probabilistic mappings of the cytoarchitecture of these areas that were derived observer‐independently under the microscope (Amunts et al., 1999 ), thus affording an unbiased assessment of area‐specific gray matter. Given the reported language‐related female superiority (Benton and Hamsher, 1976 ; Murray et al., 1990 ; Spreen and Strauss, 1991 ; Halpern, 1992 ; Morisset et al., 1995 ; Kimura, 1999 ; Bauer et al., 2002 ; Lutchmaya et al., 2002 ; Roulstone et al., 2002 ), we expected larger gray matter volumes for BA 44 as well as BA 45 in women than in men. Given that some (albeit not all) previous studies revealed a less lateralized language processing in females (Shaywitz et al., 1995 ; Frost et al., 1999 ; Sommer et al., 2004 ), we were also interested in whether there is a significant sex difference with respect to gray matter asymmetry.

A sexual dimorphism of the human brain is well established, ranging from global differences in overall brain size to local differences in regional tissue volume and the size of substructures (Luders and Toga, 2010 ; Gong et al., 2011 ; Giedd et al., 2012 ; Sacher et al., 2013 ). One of the frequently reported sex differences pertains to local gray matter, often with more gray matter and/or a thicker cortex in females than in males (Schlaepfer et al., 1995 ; Harasty et al., 1997 ; Good et al., 2001a ; Luders et al., 2005, 2006, 2009 ; Im et al., 2006 ; Sowell et al., 2007 ; Leonard et al., 2008 ; Luders and Toga, 2010 ). Women are known to outperform men on average on certain verbal tasks measuring, for example, verbal fluency, verbal memory, speed of articulation, and speech production (Benton and Hamsher, 1976 ; Hyde and Linn, 1988 ; Spreen and Strauss, 1991 ; Halpern, 1992 ; Kimura, 1999 ), with effect sizes generally ranging between small to medium (Hyde and Linn, 1988 ; Hyde, 2005 ). Moreover, although there is still discrepancy across studies (Shaywitz et al., 1995 ; Frost et al., 1999 ; Sommer et al., 2004 ; Wallentin, 2009 ), some findings point to sex differences in the functional organization of the brain for language. Thus, sex differences in the underlying neuronal substrates of language and speech have especially triggered the interest of researchers. This interest has been further stimulated by observations that girls talk earlier in life than boys, show more spontaneous language, acquire vocabulary faster, and produce longer sentences (Murray et al., 1990 ; Morisset et al., 1995 ; Kimura, 1999 ; Bauer et al., 2002 ; Lutchmaya et al., 2002 ; Roulstone et al., 2002 ; Leaper and Smith, 2004 ). Although there is some discussion on whether these reported sex differences are real and persistent over the life span (Wallentin, 2009 ), it seems intriguing that they seem closely related to language production and working memory tasks; processes that are known to involve Brodmann areas (BA) 44 and 45 (Broca, 1861 ; Cabeza and Nyberg, 2000 ; Wager and Smith, 2003 ; Grodzinsky and Santi, 2008 ; Clos et al., 2013 ). Nevertheless, findings with respect to sex differences in the neuroanatomy of BA 44/45, commonly known as Broca's region in the left hemisphere, are somewhat inconclusive. That is, although some studies did not observe any sex differences in BA 44/45 (Amunts et al., 1999 ; Chen et al., 2007 ; Leonard et al., 2008 ), others reported more gray matter in females compared with males, either within the left hemisphere only (Im et al., 2006 ) or in both hemispheres (Schlaepfer et al., 1995 ; Harasty et al., 1997 ; Good et al., 2001a ; Luders et al., 2005, 2006 ; Sowell et al., 2007 ).

To examine possible sex differences in gray matter asymmetry, the asymmetry index (AI) was calculated as (left – right)/[0.5 x (left + right)] from the volumes of BA 44 and BA 45, as described in previous publications (Amunts et al., 1999 ; Luders et al., 2004 ; Kurth et al., 2015b, 2015d ). For analysis of asymmetry differences, it is important to keep in mind that all resulting left‐ and right‐hemispheric volumes are heavily influenced by the size of their ROIs (the larger the ROI, the more voxels are included and weighted). Notably, the original cytoarchitectonic probability maps of BA 44 and BA 45 (i.e., the basis for our ROIs) are inherently asymmetric, with larger dimensions on the left than the right (Amunts et al., 1999 ). As such they largely drive the degree of asymmetry for BA 44 and BA 45 when analyzing gray matter volumes. Thus, in the current study, testing for significant asymmetry per se would yield outcomes without true scientific value. In contrast, testing for sex differences in asymmetry yields valid findings because any bias induced by the inherent asymmetric (left > right) nature of the ROIs applies to males and females alike. The resulting AIs for BA 44 and BA 45 were statistically compared between men and women, while removing the variance associated with age. Again, as a safeguard against type I error, Bonferroni corrections were applied to account for the two dependent variables (AI BA 44, AI BA 45) using a threshold of P ≤ 0.025 ( P ≤ 0.05/2).

By multiplying the voxelwise gray matter content with the voxel volume (see previous section) and then adding up the resulting voxel‐specific volumes, we calculated the probability‐weighted gray matter volumes for BA 44 and BA 45 in each hemisphere. Note that these volumes are already corrected for interindividual differences in brain size given the aforementioned modulation of the gray matter segments. The resulting gray matter volumes were statistically compared between men and women, while removing the variance associated with age. As a safeguard against type I error, Bonferroni corrections were applied to account for the four dependent variables (left BA 44, right BA 44, left BA 45, right BA 45) using a threshold of P ≤ 0.0125 ( P ≤ 0.05/4).

Integrated data processing workflow. The right BA 45 is used as an example to illustrate the combination of imaging‐based signal intensities and cytoarchitectonically defined tissue probabilities. The in vivo processing stream on the left depicts key steps of the image preprocessing as applied in the current analysis, resulting in individual gray matter segments (n = 100). The post mortem processing stream on the right (shaded box) was performed by Amunts et al. ( 1999 ), resulting in cytoarchitectonic probabilistic maps of specific ROIs (e.g., right BA 45). The voxelwise integration step denotes the voxelwise multiplication (weighting) of the individual gray matter volumes from the in vivo processing stream with the probability of belonging to a specific ROI from the post mortem processing stream. Summing up the weighted voxelwise gray matter volume (i.e., voxel by voxel) results in the ROI‐specific gray matter volume.

The normalized modulated gray matter volumes were multiplied with cytoarchitectonically derived probability maps of BA 44 and BA 45 (see Fig. 1 ) for both the left and the right hemispheres. The cytoarchitectonic probability maps, which are freely available (Eickhoff et al., 2005 ), were originally created using cell‐body stained histological sections of 10 post mortem brains through observer‐independent mapping of borders between BA 44 and BA 45 and their adjacent brain regions (Amunts et al., 1999 ). The multiplication of these maps with the gray matter segments yields a voxelwise probability‐weighted measure of gray matter content within left and right BA 44 and BA 45, as detailed elsewhere (Luders et al., 2013 ; Kurth et al., 2015a ). Note that, prior to the voxel‐wise multiplication, the left and right maps of BA 44 and BA 45 have to be spatially normalized to the DARTEL template to ensure an accurate spatial match between the probability maps and the individual gray matter segments in DARTEL space. An overview of the applied workflow is depicted in Figure 2 .

The brain images were processed in SPM8 ( http://www.fil.ion.ucl.ac.uk/spm ) and the VBM8 toolbox ( http://dbm.neuro.uni-jena.de/vbm.html ), as previously described (Luders et al., 2013 ; Kurth et al., 2015a ). Briefly, using the same generative model, images were first corrected for magnetic field inhomogeneities and tissue classified into gray matter, white matter, and cerebrospinal fluid. The segmentation procedure was based on maximum a posteriori estimations (Rajapakse et al., 1997 ) and used a partial volume estimation algorithm (Tohka et al., 2004 ) as well as a spatially adapting nonlinear means denoising filter (Manjon et al., 2010 ) and a hidden Markov random field model (Cuadra et al., 2005 ). The gray matter partitions were spatially normalized to the DARTEL template (Ashburner, 2007 ), provided with the VBM8 toolbox, using linear (12‐parameter affine) transformations and nonlinear (high‐dimensional) warping. The resulting normalized gray matter segments were then divided by the nonlinear components of the Jacobian determinant derived from the normalization matrix (Ashburner and Friston, 2000 ; Good et al., 2001b ; Kurth et al., 2015c ). Since those nonlinear components encode local volume differences in the brain (whereas linear components encode global differences in brain size), this latter modulation step preserves all individual local differences in gray matter volume. In contrast, on a global level, we account for the differences in brain size across individuals (because we are not dividing the resulting normalized gray matter segments by the linear components). This approach is comparable to including brain volume as a covariate of no interest in the statistical model.

As shown in Table I , left and right gray matter volumes of BA 44 and BA 45 were larger, on average, in female brains. The group differences reached statistical significance for all four regions, specifically, left BA 44 (F 1,97 = 8.47, n = 100, P = 0.0045), right BA 44 (F 1,97 = 7.14, n = 100, P = 0.0088), left BA 45 (F 1,97 = 8.54, n = 100, P = 0.0043), and right BA 45 (F 1,97 = 6.91, n = 100, P = 0.0100). Figure 3 depicts the median, quartiles, and 1.5 interquartile ranges for every region and each hemisphere. In terms of hemispheric differences, we observed a leftward asymmetry (especially for BA 44) as expected (because of the inherent asymmetric nature of the applied cytoarchitectonic maps; see Amunts et al., 1999 ). Interestingly, there were no significant sex differences with respect to the magnitude of this left‐hemispheric asymmetry either for BA 44 (F 1,97 = 0.01, n = 100, P = 0.9264) or for BA 45 (F 1,97 < 0.01, n = 100, P = 0.9690).

DISCUSSION

To our knowledge, this is the first study integrating cytoarchitectonically defined probabilities with MRI‐based signal intensities to assess sex differences in gray matter within BA 44 and BA 45 in a sizable cohort. The approach employed differs from classic landmark‐based ROI studies in that it does not depend on macroanatomic landmarks (sulci, gyri, etc.) that may be highly variable across individuals or even entirely missing. Our findings of more gray matter in BA 44 and BA 45 in females compared with males seem to be in close agreement with some (albeit not all) previous studies addressing sex differences in the human brain, as further discussed below. In addition, we elucidate how different analysis methods may impact study outcomes, and we conclude by elaborating on the potential functional relevance and possible determinants of the observed effects.

Correspondence to Prior Findings Our results indicating a sexual dimorphism with respect to gray matter in BA 44/45 are in good correspondence with the outcomes of other in vivo studies (Schlaepfer et al., 1995; Good et al., 2001a; Luders et al., 2005, 2006; Im et al., 2006; Sowell et al., 2007). More specifically, an ROI analysis that investigated sex differences in frontal, temporal, and inferior parietal gray matter indicated larger volumes in females than in males in frontal areas related to language processing (Schlaepfer et al., 1995). Furthermore, vertexwise investigations across the entire brain revealed a larger thickness of the cerebral cortex in females than in males within a region of the inferior frontal gyrus that matches BA 44/45, where effects were observed either in the left hemisphere only (Im et al., 2006) or in both hemispheres (Luders et al., 2006; Sowell et al., 2007). Similarly, voxelwise investigations across the entire brain exposed larger gray matter volumes or higher gray matter concentrations in females than in males within BA 44/45 in both hemispheres (Good et al., 2001a; Luders et al., 2005). Last but not least, our findings also correspond well with outcomes from a post mortem study (Harasty et al., 1997) that was specifically designed to examine language‐associated cortical regions, including the inferior frontal gyrus (which includes Broca's area in the dominant hemisphere). In that study, the cortical volume fraction of the region that includes Broca's area was 20.4% larger in female brains than in male brains. Interestingly, the original post mortem study (Amunts et al., 1999) that provided the cytoarchitectonic tissue probabilities for our present analysis did not detect sex differences in BA 44/45. However, given the extremely small sample (five males/five females) in that study, the lack of a sexual dimorphism might be due to missing statistical power. Our results indicate a lack of sexual dimorphism with respect to gray matter asymmetry in BA 44/45 and as such are in line with prior findings that do not support a sex difference in region‐specific asymmetry (Good et al., 2001a; Watkins et al., 2001; Luders et al., 2004; Takao et al., 2011; Savic, 2014). Furthermore, they seem to agree with conclusions drawn based on a meta‐analysis (Frost et al., 1999; Sommer et al., 2004) negating that women have more bilateral language representation (and thus presumably less asymmetry) than men, as also critically reviewed elsewhere (Wallentin, 2009). Nevertheless, other studies have reported less lateralized language processing in females (Shaywitz et al., 1995) or more left‐asymmetric gray matter in males (Gur et al., 1999). However, different methodological approaches might account for the discrepancies across studies, as discussed further below.

Possible Impact of Methodological Approaches Different methodological approaches measure different cerebral attributes that may not be readily comparable. Thus, varying findings in the literature with respect to a sexual dimorphism in BA 44/45 are not unexpected. For example, in structural magnetic resonance images, the manual delineation of an ROI covering BA 44 and BA 45 requires detailed tracing guidelines. Consequently, this makes the resulting ROI highly dependent on the applied protocol (i.e., in terms of its preciseness, correctness, and reproducibility) as well as on the precision and consistency of the rater. In case of BA 44/45, things are even more challenging; not only are visible macroanatomic landmarks variable across individuals, but they also show only little (if any) spatial correspondence with the cytoarchitectonic boundaries (Amunts et al., 1999, 2007; Zilles and Amunts, 2010). Different studies may therefore include different regions pertaining to BA 44/45. The current approach circumvents these issues altogether by considering probabilistic a priori information on cytoarchitecture, which allows reliably capturing the boundaries of the ROI with high consistency across all subjects. Similarly to the current approach, vertex‐ or voxelwise techniques are not dependent on delineation protocols, raters, or judgment calls. However, these approaches come with an inherent and substantial multiple‐comparisons problem that may render them less sensitive. Last but not least, regardless of the applied measurement, another factor might have contributed to the discrepancy in findings across studies: On average, male brains are larger than female brains (Luders and Toga, 2010; Gong et al., 2011; Giedd et al., 2012; Sacher et al., 2013). Thus, without proper corrections for individual brain size, relatively more gray matter in BA 44/45 in female brains might remain undetected (i.e., if comparing only absolute volumes). Importantly, the present results indicating more gray matter in BA 44/45 in female brains were derived from data that were corrected for differences in overall brain size.