Neural correlates of individual differences in multimodal emotion recognition ability.

Studies have reported substantial variability in emotion recognition ability (ERA) - an important social skill - but possible neural underpinnings for such individual differences are not well understood. This functional magnetic resonance imaging (fMRI) study investigated neural responses during emotion recognition in young adults (N = 49) who were selected for inclusion based on their performance (high or low) during previous testing of ERA. Participants were asked to judge brief video recordings in a forced-choice emotion recognition task, wherein stimuli were presented in visual, auditory and multimodal (audiovisual) blocks. Emotion recognition rates during brain scanning confirmed that individuals with high (vs low) ERA received higher accuracy for all presentation blocks. fMRI-analyses focused on key regions of interest (ROIs) involved in the processing of multimodal emotion expressions, based on previous meta-analyses. In neural response to emotional stimuli contrasted with neutral stimuli, individuals with high (vs low) ERA showed higher activation in the following ROIs during the multimodal condition: right middle superior temporal gyrus (mSTG), right posterior superior temporal sulcus (PSTS), and right inferior frontal cortex (IFC). Overall, results suggest that individual variability in ERA may be reflected across several stages of decisional processing, including extraction (mSTG), integration (PSTS) and evaluation (IFC) of emotional information.

Age-dependent effects of oxytocin in brain regions enriched with oxytocin receptors.

Although intranasal oxytocin administration to tap into central functions is the most commonly used non-invasive means for exploring oxytocin's role in human cognition and behavior, the way by which intranasal oxytocin acts on the brain is not yet fully understood. Recent research suggests that brain regions densely populated with oxytocin receptors may play a central role in intranasal oxytocin's action mechanisms in the brain. In particular, intranasal oxytocin may act directly on (subcortical) regions rich in oxytocin receptors via binding to these receptors while only indirectly affecting other (cortical) regions via their neural connections to oxytocin receptor-enriched regions. Aligned with this notion, the current study adopted a novel approach to test 1) whether the connections between oxytocin receptor-enriched regions (i.e., the thalamus, pallidum, caudate nucleus, putamen, and olfactory bulbs) and other regions in the brain were responsive to intranasal oxytocin administration, and 2) whether oxytocin-induced effects varied as a function of age. Forty-six young (24.96 ± 3.06 years) and 44 older (69.89 ± 2.99 years) participants were randomized, in a double-blind procedure, to self-administer either intranasal oxytocin or placebo before resting-state fMRI. Results supported age-dependency in the effects of intranasal oxytocin administration on connectivity between oxytocin receptor-enriched regions and other regions in the brain. Specifically, compared to placebo, oxytocin decreased both connectivity density and connectivity strength of the thalamus for young participants while it increased connectivity density and connectivity strength of the caudate for older participants. These findings inform the mechanisms underlying the effects of exogenous oxytocin on brain function and highlight the importance of age in these processes.

Estimated gray matter volume rapidly changes after a short motor task.

Skill learning induces changes in estimates of gray matter volume (GMV) in the human brain, commonly detectable with magnetic resonance imaging (MRI). Rapid changes in GMV estimates while executing tasks may however confound between- and within-subject differences. Fluctuations in arterial blood flow are proposed to underlie this apparent task-related tissue plasticity. To test this hypothesis, we acquired multiple repetitions of structural T1-weighted and functional blood-oxygen level-dependent (BOLD) MRI measurements from 51 subjects performing a finger-tapping task (FTT; á 2 min) repeatedly for 30-60 min. Estimated GMV was decreased in motor regions during FTT compared with rest. Motor-related BOLD signal changes did not overlap nor correlate with GMV changes. Nearly simultaneous BOLD signals cannot fully explain task-induced changes in T1-weighted images. These sensitive and behavior-related GMV changes pose serious questions to reproducibility across studies, and morphological investigations during skill learning can also open new avenues on how to study rapid brain plasticity.

Moment-to-Moment Brain Signal Variability Reliably Predicts Psychiatric Treatment Outcome.

BACKGROUND: Biomarkers of psychiatric treatment response remain elusive. Functional magnetic resonance imaging (fMRI) has shown promise, but low reliability has limited the utility of typical fMRI measures (e.g., average brain signal) as harbingers of treatment success. Notably, although historically considered a source of noise, temporal brain signal variability continues to gain momentum as a sensitive and reliable indicator of individual differences in neural efficacy, yet has not been examined in relation to psychiatric treatment outcomes. METHODS: A total of 45 patients with social anxiety disorder were scanned twice (11 weeks apart) using simple task-based and resting-state fMRI to capture moment-to-moment neural variability. After fMRI test-retest, patients underwent a 9-week cognitive behavioral therapy. Multivariate modeling and reliability-based cross-validation were used to perform brain-based prediction of treatment outcomes. RESULTS: Task-based brain signal variability was the strongest contributor in a treatment outcome prediction model (total rACTUAL,PREDICTED = 0.77), outperforming self-reports, resting-state neural variability, and standard mean-based measures of neural activity. Notably, task-based brain signal variability showed excellent test-retest reliability (intraclass correlation coefficient = 0.80), even with a task length less than 3 minutes long. CONCLUSIONS: Rather than a source of undesirable noise, moment-to-moment fMRI signal variability may instead serve as a highly reliable and efficient prognostic indicator of clinical outcome.

A Quantitative Data-Driven Analysis Framework for Resting-State Functional Magnetic Resonance Imaging: A Study of the Impact of Adult Age.

The objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC). Whole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N = 227, aged 18-76 years old, male/female = 99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index and connectivity density index utilizing the convolutions of the cross-correlation histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately. With the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyri, posterior cingulate cortex (PCC), right insula and inferior parietal lobule of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: (1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects' age; (2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; and (3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age. The proposed QDA framework can produce threshold-free and voxel-wise RFC metrics from R-fMRI data. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.

Dataset of whole-brain resting-state fMRI of 227 young and elderly adults acquired at 3T.

To investigate the impact of adult age on the brain functional connectivity, whole-brain resting-state functional magnetic resonance imaging (R-fMRI) data were acquired on a 3T clinical MRI scanner in a cohort of 227, right-handed, native Swedish-speaking, healthy adult volunteers (N=227, aged 18-74 years old, male/female=99/128). The dataset is mainly consisted of a younger (18-30 years old n=124, males/females=51/73) and elderly adult (n=76, 60-76 years old, males/females=35/41) subgroups. The dataset was analyzed using a new data-driven analysis (QDA) framework. With QDA two types of threshold-free voxel-wise resting-state functional connectivity (RFC) metrics were derived: the connectivity strength index (CSI) and connectivity density index (CDI), which can be utilized to assess the brain changes in functional connectivity associated with adult age. The dataset can also be useful as a reference to identify abnormal changes in brain functional connectivity resulted from neurodegenerative or neuropsychiatric disorders.

Cross-sex hormone treatment and own-body perception: behavioral and brain connectivity profiles.

Referrals for gender dysphoria (GD), characterized by a distressful incongruence between gender identity and at-birth assigned sex, are steadily increasing. The underlying neurobiology, and the mechanisms of the often-beneficial cross-sex hormone treatment are unknown. Here, we test hypothesis that own body perception networks (incorporated in the default mode network-DMN, and partly in the salience network-SN), are different in trans-compared with cis-gender persons. We also investigate whether these networks change with cross-sex hormone treatment. Forty transmen (TrM) and 25 transwomen (TrW) were scanned before and after cross-sex hormone institution. We used our own developed Body Morph test (BM), to assess the perception of own body as self. Fifteen cisgender persons were controls. Within and between-group differences in functional connectivity were calculated using independent components analysis within the DMN, SN, and motor network (a control network). Pretreatment, TrM and TrW scored lower "self" on the BM test than controls. Their functional connections were weaker in the anterior cingulate-, mesial prefrontal-cortex (mPFC), precuneus, the left angular gyrus, and superior parietal cortex of the DMN, and ACC in the SN "Self" identification and connectivity in the mPFC in both TrM and TrW increased from scan 1 to 2, and at scan 2 no group differences remained. The neurobiological underpinnings of GD seem subserved by cerebral structures composing major parts of the DMN.

Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivity.

Individuals with gender incongruence (GI) experience serious distress due to incongruence between their gender identity and birth-assigned sex. Sociological, cultural, interpersonal, and biological factors are likely contributory, and for some individuals medical treatment such as cross-sex hormone therapy and gender-affirming surgery can be helpful. Cross-sex hormone therapy can be effective for reducing body incongruence, but responses vary, and there is no reliable way to predict therapeutic outcomes. We used clinical and MRI data before cross-sex hormone therapy as features to train a machine learning model to predict individuals' post-therapy body congruence (the degree to which photos of their bodies match their self-identities). Twenty-five trans women and trans men with gender incongruence participated. The model significantly predicted post-therapy body congruence, with the highest predictive features coming from the cingulo-opercular (R2 = 0.41) and fronto-parietal (R2 = 0.30) networks. This study provides evidence that hormone therapy efficacy can be predicted from information collected before therapy, and that patterns of functional brain connectivity may provide insights into body-brain effects of hormones, affecting one's sense of body congruence. Results could help identify the need for personalized therapies in individuals predicted to have low body-self congruence after standard therapy.

Neural Systems for Own-body Processing Align with Gender Identity Rather Than Birth-assigned Sex.

Gender identity is a core aspect of self-identity and is usually congruent with birth-assigned sex and own body sex-perception. The neuronal circuits underlying gender identity are unknown, but greater awareness of transgenderism has sparked interest in studying these circuits. We did this by comparing brain activation and connectivity in transgender individuals (for whom gender identity and birth-assigned sex are incongruent) with that in cisgender controls (for whom they are congruent) when performing a body self-identification task during functional magnetic resonance imaging. Thirty transgender and 30 cisgender participants viewed images of their own bodies and bodies morphed in sex toward or opposite to birth-assigned sex, rating each image to the degree they identified with it. While controls identified with images of themselves, transgender individuals identified with images morphed "opposite" to their birth-assigned sex. After covarying out the effect of self-similarity ratings, both groups activated similar self- and body-processing systems when viewing bodies that aligned with their gender identity rather than birth-assigned sex. Additionally, transgender participants had greater limbic involvement when viewing ambiguous, androgynous images of themselves morphed toward their gender identity. These results shed light on underlying self-processing networks specific to gender identity and uncover additional involvement of emotional processing in transgender individuals.

Multimodal MRI suggests that male homosexuality may be linked to cerebral midline structures.

The neurobiology of sexual preference is often discussed in terms of cerebral sex dimorphism. Yet, our knowledge about possible cerebral differences between homosexual men (HoM), heterosexual men (HeM) and heterosexual women (HeW) are extremely limited. In the present MRI study, we addressed this issue investigating measures of cerebral anatomy and function, which were previously reported to show sex difference. Specifically, we asked whether there were any signs of sex atypical cerebral dimorphism among HoM, if these were widely distributed (providing substrate for more general 'female' behavioral characteristics among HoM), or restricted to networks involved in self-referential sexual arousal. Cortical thickness (Cth), surface area (SA), subcortical structural volumes, and resting state functional connectivity were compared between 30 (HoM), 35 (HeM) and 38 (HeW). HoM displayed a significantly thicker anterior cingulate cortex (ACC), precuneus, and the left occipito-temporal cortex compared to both control groups. These differences seemed coordinated, since HoM also displayed stronger cortico-cortical covariations between these regions. Furthermore, functional connections within the default mode network, which mediates self- referential processing, and includes the ACC and precuneus were significantly weaker in HoM than HeM and HeW, whereas their functional connectivity between the thalamus and hypothalamus (important nodes for sexual behavior) was stronger. In addition to these singular features, HoM displayed 'female' characteristics, with a similar Cth in the left superior parietal and cuneus cortices as HeW, but different from HeM. These data suggest both singular and sex atypical features and motivate further investigations of cerebral midline structures in relation to male homosexuality.

Cerebral sex dimorphism and sexual orientation.

The neurobiology of sexual orientation is frequently discussed in terms of cerebral sex dimorphism (defining both functional and structural sex differences). Yet, the information about possible cerebral differences between sex-matched homo and heterosexual persons is limited, particularly among women. In this multimodal MRI study, we addressed these issues by investigating possible cerebral differences between homo and heterosexual persons, and by asking whether there is any sex difference in this aspect. Measurements of cortical thickness (Cth), subcortical volumes, and functional and structural resting-state connections among 40 heterosexual males (HeM) and 40 heterosexual females (HeF) were compared with those of 30 homosexual males (HoM) and 30 homosexual females (HoF). Congruent with previous reports, sex differences were detected in heterosexual controls with regard to fractional anisotropy (FA), Cth, and several subcortical volumes. Homosexual groups did not display any sex differences in FA values. Furthermore, their functional connectivity was significantly less pronounced in the mesial prefrontal and precuneus regions. In these two particular regions, HoM also displayed thicker cerebral cortex than other groups, whereas HoF did not differ from HeF. In addition, in HoM the parietal Cth showed "sex-reversed" values, not observed in HoF. Homosexual orientation seems associated with a less pronounced sexual differentiation of white matter tracts and a less pronounced functional connectivity of the self-referential networks compared to heterosexual orientation. Analyses of Cth suggest that male and female homosexuality are not simple analogues of each other and that differences from heterosexual controls are more pronounced in HoM.

Role of testosterone and Y chromosome genes for the masculinization of the human brain.

Women with complete androgen insensitivity syndrome (CAIS) have a male (46,XY) karyotype but no functional androgen receptors. Their condition, therefore, offers a unique model for studying testosterone effects on cerebral sex dimorphism. We present MRI data from 16 women with CAIS and 32 male (46,XY) and 32 female (46,XX) controls. METHODS: FreeSurfer software was employed to measure cortical thickness and subcortical structural volumes. Axonal connections, indexed by fractional anisotropy, (FA) were measured with diffusion tensor imaging, and functional connectivity with resting state fMRI. RESULTS: Compared to men, CAIS women displayed a "female" pattern by having thicker parietal and occipital cortices, lower FA values in the right corticospinal, superior and inferior longitudinal tracts, and corpus callosum. Their functional connectivity from the amygdala to the medial prefrontal cortex, was stronger and amygdala-connections to the motor cortex weaker than in control men. CAIS and control women also showed stronger posterior cingulate and precuneus connections in the default mode network. Thickness of the motor cortex, the caudate volume, and the FA in the callosal body followed, however, a "male" pattern. CONCLUSION: Altogether, these data suggest that testosterone modulates the microstructure of somatosensory and visual cortices and their axonal connections to the frontal cortex. Testosterone also influenced functional connections from the amygdala, whereas the motor cortex could, in agreement with our previous reports, be moderated by processes linked to X-chromosome gene dosage. These data raise the question about other genetic factors masculinizing the human brain than the SRY gene and testosterone. Hum Brain Mapp 38:1801-1814, 2017. © 2017 Wiley Periodicals, Inc.

Multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines.

White matter changes (WMC) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMC labor intensive and prone to subjective bias. We present a fast, fully automated method for WMC segmentation using a cascade of reduced support vector machines (SVMs) with active learning. Data of 102 subjects was used in this study. Two MRI sequences (T1-weighted and FLAIR) and masks of manually outlined WMC from each subject were used for the image analysis. The segmentation framework comprises pre-processing, classification (training and core segmentation) and post-processing. After pre-processing, the model was trained on two subjects and tested on the remaining 100 subjects. The effectiveness and robustness of the classification was assessed using the receiver operating curve technique. The cascade of SVMs segmentation framework outputted accurate results with high sensitivity (90%) and specificity (99.5%) values, with the manually outlined WMC as reference. An algorithm for the segmentation of WMC is proposed. This is a completely competitive and fast automatic segmentation framework, capable of using different input sequences, without changes or restrictions of the image analysis algorithm.