New MRI Studies Support the Blanchard Typology of Male-to-Female Transsexualism
Bibliographic record
Abstract
Twoindependentempiricalarticleshaverecentlyappearedintheliterature that, taken together , bear out an hypothesis Blanchard(2008) postulated in the Archives about brain development intranssexualism:[T]he brains of both homosex ual and heterosexual male-to-female transsexuals probably differ from the brains oftypicalheterosexualmen,butindifferentways.Inhomo-sexual male-to-female transsexuals, the difference doesinvolvesex-dimorphicstructures,andthenatureofthedif-ference is a shift in the female -typical direction. If there isany neuroanatomic intersexuality, it is in the homosexualgroup. In heterosexual male-to-female transsexuals, thedifferencemaynotinvolve sex-dimorphic structuresatall,andthenatureofthestructura ldifferenceisnotnecessarilyalong the male–female dimension. (p. 437)Blanchard’s prediction follows from studies that have repeat-edly shown that the homosexual male-to-female transsexualsare‘‘female-shifted’’in multiple, sexually dimorphic character-istics,whereastheheterosexual male-to-femaletranssexualsarenot(Blanchard,1989a,1989b).Forexample,homosexualmale-to-female transsexuals are sexually attracted to natal males,expressgreaterinterestinfemale-typicalactivities(eveninchild-hood), and are naturally effemina te in mannerism. In contrast,heterosexual male-to-female transsexuals are indistinguishablefrom nontranssexual natal males o n these variables. The hetero-sexual transsexuals are still dis tinct from typical males in otherways, however, such as by manif esting ‘‘autogynephilia’’—theeroticinterestinorsexualarousalinresponsetobeingorseemingfemale. The consistent detection of cross-sex features amonghomosexual male-to-female tra nssexuals, but not among heter-osexual male-to-female transse xuals, led Blanchard to predictthat the cross-sex pattern would also emerge at the level of brainanatomyandbelimitedtothehomosexualmale-to-femaletrans-sexuals.Thatpredictionnowappearstobethecase,withRamettiet al. (2010) supporting his prediction for the homosexual trans-sexuals, and Savic and Arver ( 2010), for the heterosexual trans-sexuals.The Rametti team used an MRI technique called DiffusionTensor Imaging to compare homosexual male-to-female trans-sexuals(n=18)withnontranssexual,heterosexualcontrolmales(n=19) and with nontranssexual, h eterosexual control females(n=19). They contrasted the male controls with the femalecontrols to identify the sex-dimorphic portions of the brain andthen contrasted the homosexual transsexuals with each of thecontrol groups on the dimorphic brain regions so identified. Theinitial contrasts identified six sex-dimorphic brain regions. Thehomosexual transsexual sample w as intermediate in volume onall six brain structures, significan tly different from the male con-trolsonfiveofthesix(andsignific antlydifferentfromthefemalecontrols on all six). That is, the se male-to-female transsexualsweredifferentfromthecontrolmales,shiftedtowardsthefemaledirection on all parameters.Savic and Arver (2010) applied anatomical MRIs with ananalogousresearchdesign,identifyingthesex-dimorphicpor-tions of the brain and contrasting the (this time) heterosexualtranssexual sample (n=24) with each control sample (n’s=24each)onthesex-dimorphicbrainregions.Oftheeightbrainregionsthatdistinguishedmalefromfemalebrains,thehetero-sexual transsexual sample differed from the male controls onnone(Savic&Arver,2010,Table3).Ofthefourbrainregionsthat distinguished these heterosexual transsexuals from themale controls, sex-dimorphism was present in none (Savic &Arver,2010,Table3).AsSavicandArverthemselvesempha-sized, ‘‘Contrary to the primary hypothesis, no sex-atypical
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".