Industrial Land and Development Database 1992 Analysis

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Factor loadings on the questions from the first section of the NYC-Q describing the content of self-generated thoughts.

<p>Questions (rows) were decomposed into factors (columns) using Exploratory Factor analysis. Factors were named based on subjective interpretation of the loadings. Weights (how much each question contributes to each factor) are represented both numerically as well as on a colour scale. Questions adapted from DSSQ are marked with an asterisk.</p

Spatial distribution of fALFF, ReHo, and DC measures across subjects.

<p>Each map was obtained from a one sample t test, converted to z values and thresholded ad Z = 10 (for visualisation purposes). The bottom row features the data inferred mask used in the group analysis. pCC and mPFC show high ReHo values and mPFC show high fALFF values. Those are the major hubs of DMN which suggests that even without relating the measures to the questionnaire results DMN plays an important role in brain activation at rest.</p

Significant fALFF clusters (A–F), scatterplots showing relation between dependent variables (mean fALFF values) and contrast scores (questionnaire factors), and networks obtained by seeding with the corresponding cluster.

<p>All derivatives have been z scored. All scatter plots represent the whole population (n = 121). Note that only clusters that passed the conservative multiple comparison corrected threshold are shown in this figure.</p

Sex beyond the genitalia: The human brain mosaic

Whereas a categorical difference in the genitals has always been acknowledged, the question of how far these categories extend into human biology is still not resolved. Documented sex/gender differences in the brain are often taken as support of a sexually dimorphic view of human brains ("female brain" or "male brain"). However, such a distinction would be possible only if sex/gender differences in brain features were highly dimorphic (i.e., little overlap between the forms of these features in males and females) and internally consistent (i.e., a brain has only "male" or only "female" features). Here, analysis of MRIs of more than 1,400 human brains from four datasets reveals extensive overlap between the distributions of females and males for all gray matter, white matter, and connections assessed. Moreover, analyses of internal consistency reveal that brains with features that are consistently at one end of the "maleness-femaleness" continuum are rare. Rather, most brains are comprised of unique "mosaics" of features, some more common in females compared with males, some more common in males compared with females, and some common in both females and males. Our findings are robust across sample, age, type of MRI, and method of analysis. These findings are corroborated by a similar analysis of personality traits, attitudes, interests, and behaviors of more than 5,500 individuals, which reveals that internal consistency is extremely rare. Our study demonstrates that, although there are sex/gender differences in the brain, human brains do not belong to one of two distinct categories: male brain/female brain

Location and statistical parameters of clusters obtained using the multiple comparison corrected threshold.

<p>Cluster labels correspond to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097176#pone-0097176-g005" target="_blank">Figures 5</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097176#pone-0097176-g006" target="_blank">6</a>.</p

Location and statistical parameters of additional clusters obtained using the liberal threshold the more liberal threshold not corrected for the number of derivatives.

<p>Cluster labels correspond to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097176#pone-0097176-g007" target="_blank">Figures 7</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097176#pone-0097176-g008" target="_blank">8</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097176#pone-0097176-g009" target="_blank">9</a>.</p

Factor loadings on the questions recovered from the second section of the NYC-Q describing the form of self-generated thoughts.

<p>Questions (rows) were decomposed into factors (columns) using Exploratory Factor analysis. Factors were named based on subjective interpretation of the loadings. Weights (how much each question contributes to each factor) are represented both numerically as well as on a colour scale.</p

Partial (lower diagonal) and full (upper diagonal) Pearson correlations between age, head motion and estimated factors of the NYC-Q.

<p>Numbers and colours represent r values. Significant correlations (two-tailed p = 0.025) are marked with an asterisk.</p