Sexual Dimorphism Within Brain Regions Controlling Speech Production

Novel Isolation Method Reveals Sex-Specific Composition and Neurotoxicity of Small Extracellular Vesicles in a Mouse Model of Alzheimer's Disease

We developed a new method to isolate small extracellular vesicles (sEVs) from male and female wild-type and 5xFAD mouse brains to investigate the sex-specific functions of sEVs in Alzheimer's disease (AD). A mass spectrometric analysis revealed that sEVs contained proteins critical for EV formation and Aβ. ExoView analysis showed that female mice contained more GFAP and Aβ-labeled sEVs, suggesting that a larger proportion of sEVs from the female brain is derived from astrocytes and/or more likely to bind to Aβ. Moreover, sEVs from female brains had more acid sphingomyelinase (ASM) and ceramide, an enzyme and its sphingolipid product important for EV formation and Aβ binding to EVs, respectively. We confirmed the function of ASM in EV formation and Aβ binding using co-labeling and proximity ligation assays, showing that ASM inhibitors prevented complex formation between Aβ and ceramide in primary cultured astrocytes. Finally, our study demonstrated that sEVs from female 5xFAD mice were more neurotoxic than those from males, as determined by impaired mitochondrial function (Seahorse assays) and LDH cytotoxicity assays. Our study suggests that sex-specific sEVs are functionally distinct markers for AD and that ASM is a potential target for AD therapy.

Emotional sounds in space: asymmetrical representation within early-stage auditory areas

Evidence from behavioral studies suggests that the spatial origin of sounds may influence the perception of emotional valence. Using 7T fMRI we have investigated the impact of the categories of sound (vocalizations; non-vocalizations), emotional valence (positive, neutral, negative) and spatial origin (left, center, right) on the encoding in early-stage auditory areas and in the voice area. The combination of these different characteristics resulted in a total of 18 conditions (2 categories x 3 valences x 3 lateralizations), which were presented in a pseudo-randomized order in blocks of 11 different sounds (of the same condition) in 12 distinct runs of 6 min. In addition, two localizers, i.e., tonotopy mapping; human vocalizations, were used to define regions of interest. A three-way repeated measure ANOVA on the BOLD responses revealed bilateral significant effects and interactions in the primary auditory cortex, the lateral early-stage auditory areas, and the voice area. Positive vocalizations presented on the left side yielded greater activity in the ipsilateral and contralateral primary auditory cortex than did neutral or negative vocalizations or any other stimuli at any of the three positions. Right, but not left area L3 responded more strongly to (i) positive vocalizations presented ipsi- or contralaterally than to neutral or negative vocalizations presented at the same positions; and (ii) to neutral than positive or negative non-vocalizations presented contralaterally. Furthermore, comparison with a previous study indicates that spatial cues may render emotional valence more salient within the early-stage auditory areas.

State Boredom Partially Accounts for Gender Differences in Novel Lexicon Learning

Gender plays an important role in various aspects of second language acquisition, including lexicon learning. Many studies have suggested that compared to males, females are less likely to experience boredom, one of the frequently experienced deactivating negative emotions that may impair language learning. However, the contribution of boredom to gender-related differences in lexicon learning remains unclear. To address this question, here we conducted two experiments with a large sample of over 1,000 college students to explore the relationships between gender differences in boredom and lexicon learning. In Experiment 1, a cohort of 527 participants (238 males) completed the trait and state boredom scales as well as a novel lexicon learning task without awareness of the testing process. In Experiment 2, an independent cohort of 506 participants (228 males) completed the same novel lexicon learning task with prior knowledge of the testing procedure. Results from both experiments consistently showed significant differences between female and male participants in the rate of forgetting words and the state boredom scores, with female participants performing better than male participants. Furthermore, differences in state boredom scores partially explained differences in the rate of forgetting words between female and male participants. These findings demonstrate a novel contribution of state boredom to gender differences in lexicon learning, which provides new insights into better language-learning ability in females.

Treatment With Nepicastat Decreases Contextual Traumatic Memories Persistence in Post-traumatic Stress Disorder

Post-traumatic stress disorder (PTSD) is a common anxiety mental disorder and can be manifested after exposure to a real or perceived life-threatening event. Increased noradrenaline and adrenaline in plasma and urine have been documented in PTSD. Dopamine-β-hydroxylase (DBH) catalyzes the conversion of dopamine to noradrenaline and consequently, DBH inhibition reduces catecholamines. Our aim was to evaluate if nepicastat treatment decreases PTSD signs in an animal model. Wild-type (129x1/SvJ) female mice were submitted to PTSD induction protocol. DBH-inhibitor nepicastat (30 mg/kg) or vehicle (0.2% HPMC) were administered once daily since day 0 until day 7 or 12. The percentage of freezing was calculated on days 0, 1, 2, and 7, and behavioral tests were performed. Quantification of nepicastat in plasma and DBH activity in the adrenal gland was evaluated. Catecholamines were quantified by HPLC with electrochemical detection. mRNA expression of Npas4 and Bdnf in hippocampus was evaluated by qPCR.Mice in the PTSD-group and treated with nepicastat showed a decrease in freezing, and an increase in the time spent and entries in open arms in elevated plus maze test. In mice treated with nepicastat, adrenal gland DBH activity was decreased, and catecholamines were also decreased in plasma and tissues. On day 7, in mice treated with nepicastat, there was an increase of Npas4 and Bdnf mRNA expression in the hippocampus.In conclusion, DBH inhibitor nepicastat has an effect consistent with a decrease in the persistence of traumatic memories and anxiety-like behavior in this PTSD mice model. The disruption of traumatic memories through interference with the formation, consolidation, retrieval, and/or expression processes may be important to decrease PTSD symptoms and signs. The increase in Npas4 and Bdnf mRNA expression in the hippocampus may be important to develop a weaker traumatic contextual memory after nepicastat treatment.

Morphometrical Brain Markers of Sex Difference

Many major neuropsychiatric pathologies, some of which appear in adolescence, show differentiated prevalence, onset, and symptomatology across the biological sexes. Therefore, mapping differences in brain structure between males and females during this critical developmental period may provide information about the neural mechanisms underlying the dimorphism of these pathologies. Utilizing a large dataset collected through the Adolescent Brain Cognitive Development study, we investigated the differences of adolescent (9-10 years old) male and female brains (n = 8325) by using a linear Support-Vector Machine Classifier to predict sex based on morphometry and image intensity values of structural brain imaging data. The classifier correctly classified the sex of 86% individuals with the insula, the precentral and postcentral gyri, and the pericallosal sulcus as the most discernable features. These results demonstrate the existence of complex, yet robustly measurable morphometrical brain markers of sex difference.

The role of sex chromosomes and sex hormones in vocal learning systems

Vocal learning is the ability to imitate and modify sounds through auditory experience, a rare trait found in only a few lineages of mammals and birds. It is a critical component of human spoken language, allowing us to verbally transmit speech repertoires and knowledge across generations. In many vocal learning species, the vocal learning trait is sexually dimorphic, where it is either limited to males or present in both sexes to different degrees. In humans, recent findings have revealed subtle sexual dimorphism in vocal learning/spoken language brain regions and some associated disorders. For songbirds, where the neural mechanisms of vocal learning have been well studied, vocal learning appears to have been present in both sexes at the origin of the lineage and was then independently lost in females of some subsequent lineages. This loss is associated with an interplay between sex chromosomes and sex steroid hormones. Even in species with little dimorphism, like humans, sex chromosomes and hormones still have some influence on learned vocalizations. Here we present a brief synthesis of these studies, in the context of sex determination broadly, and identify areas of needed investigation to further understand how sex chromosomes and sex steroid hormones help establish sexually dimorphic neural structures for vocal learning.

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech

Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients—Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7–15% improvement). This result was observed for both recording types (high-quality microphone and telephone).

Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning

Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advantage — representation learning. We conduct a large-scale systematic comparison profiled in multiple classification and regression tasks on structural MRI images and show the importance of representation learning for DL. Results show that if trained following prevalent DL practices, DL methods have the potential to scale particularly well and substantially improve compared to SML methods, while also presenting a lower asymptotic complexity in relative computational time, despite being more complex. We also demonstrate that DL embeddings span comprehensible task-specific projection spectra and that DL consistently localizes task-discriminative brain biomarkers. Our findings highlight the presence of nonlinearities in neuroimaging data that DL can exploit to generate superior task-discriminative representations for characterizing the human brain.

Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) for brain imaging data analysis. Here, the authors show that if trained following prevalent DL practices, DL methods substantially improve compared to SML methods by encoding robust discriminative brain representations.