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Research on sex differences in empathy has revealed mixed findings. Whereas experimental and neuropsychological measures show no consistent sex effect, self-report data consistently indicates greater empathy in women. However, available results mainly come from separate populations with relatively small samples, which may inflate effect sizes and hinder comparability between both empirical corpora. To elucidate the issue, we conducted two large-scale studies. First, we examined whether sex differences emerge in a large population-based sample (n = 10,802) when empathy is measured with an experimental empathy-for-pain paradigm. Moreover, we investigated the relationship between empathy and moral judgment. In the second study, a subsample (n = 334) completed a self-report empathy questionnaire. Results showed some sex differences in the experimental paradigm, but with minuscule effect sizes. Conversely, women did portray themselves as more empathic through self-reports. In addition, utilitarian responses to moral dilemmas were less frequent in women, although these differences also had small effect sizes. These findings suggest that sex differences in empathy are highly driven by the assessment measure. In particular, self-reports may induce biases leading individuals to assume gender-role stereotypes. Awareness of the role of measurement instruments in this field may hone our understanding of the links between empathy, sex differences, and gender roles.

Interoception is a complex process encompassing multiple dimensions, such as accuracy, learning and awareness. Here, we examined whether each of those dimensions relies on specialized neural regions distributed throughout the vast interoceptive network. To this end, we obtained relevant measures of cardiac interoception in healthy subjects and patients offering contrastive lesion models of neurodegeneration and focal brain damage: behavioural variant fronto-temporal dementia (bvFTD), Alzheimer's disease (AD) and fronto-insular stroke. Neural correlates of the three dimensions were examined through structural and functional resting-state imaging, and online measurements of the heart-evoked potential (HEP). The three patient groups presented deficits in interoceptive accuracy, associated with insular damage, connectivity alterations and abnormal HEP modulations. Interoceptive learning was differentially impaired in AD patients, evidencing a key role of memory networks in this skill. Interoceptive awareness results showed that bvFTD and AD patients overestimated their performance; this pattern was related to abnormalities in anterior regions and associated networks sub-serving metacognitive processes, and probably linked to well-established insight deficits in dementia. Our findings indicate how damage to specific hubs in a broad fronto-temporo-insular network differentially compromises interoceptive dimensions, and how such disturbances affect widespread connections beyond those critical hubs. This is the first study in which a multiple lesion model reveals fine-grained alterations of body sensing, offering new theoretical insights into neuroanatomical foundations of interoceptive dimensions. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’.

From molecular mechanisms to global brain networks, atypical fluctuations are the hallmark of neurodegeneration. Yet, traditional fMRI research on resting-state networks (RSNs) has favored static and average connectivity methods, which by overlooking the fluctuation dynamics triggered by neurodegeneration, have yielded inconsistent results. The present multicenter study introduces a data-driven machine learning pipeline based on dynamic connectivity fluctuation analysis (DCFA) on RS-fMRI data from 300 participants belonging to three groups: behavioral variant frontotemporal dementia (bvFTD) patients, Alzheimer's disease (AD) patients, and healthy controls. We considered non-linear oscillatory patterns across combined and individual resting-state networks (RSNs), namely: the salience network (SN), mostly affected in bvFTD; the default mode network (DMN), mostly affected in AD; the executive network (EN), partially compromised in both conditions; the motor network (MN); and the visual network (VN). These RSNs were entered as features for dementia classification using a recent robust machine learning approach (a Bayesian hyperparameter tuned Gradient Boosting Machines (GBM) algorithm), across four independent datasets with different MR scanners and recording parameters. The machine learning classification accuracy analysis revealed a systematic and unique tailored architecture of RSN disruption. The classification accuracy ranking showed that the most affected networks for bvFTD were the SN + EN network pair (mean accuracy = 86.43%, AUC = 0.91, sensitivity = 86.45%, specificity = 87.54%); for AD, the DMN + EN network pair (mean accuracy = 86.63%, AUC = 0.89, sensitivity = 88.37%, specificity = 84.62%); and for the bvFTD vs. AD classification, the DMN + SN network pair (mean accuracy = 82.67%, AUC = 0.86, sensitivity = 81.27%, specificity = 83.01%). Moreover, the DFCA classification systematically outperformed canonical connectivity approaches (including both static and linear dynamic connectivity). Our findings suggest that non-linear dynamical fluctuations surpass two traditional seed-based functional connectivity approaches and provide a pathophysiological characterization of global brain networks in neurodegenerative conditions (AD and bvFTD) across multicenter data.

Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL’s current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here:  https://demo.sci.tellapp.org/ .

Interoception (the sensing of inner-body signals) is a multi-faceted construct with major relevance for basic and clinical neuroscience research. However, the neurocognitive signatures of this domain (cutting across behavioral, electrophysiological, and fMRI connectivity levels) are rarely reported in convergent or systematic fashion. Additionally, various controversies in the field might reflect the caveats of standard interoceptive accuracy (IA) indexes, mainly based on heartbeat detection (HBD) tasks. Here we profit from a novel IA index (md) to provide a convergent multidimensional and multi-feature approach to cardiac interoception. We found that outcomes from our IA-md index are associated with –and predicted by– canonical markers of interoception, including the hd-EEG-derived heart-evoked potential (HEP), fMRI functional connectivity within interoceptive hubs (insular, somatosensory, and frontal networks), and socio-emotional skills. Importantly, these associations proved more robust than those involving current IA indexes. Furthermore, this pattern of results persisted when taking into consideration confounding variables (gender, age, years of education, and executive functioning). This work has relevant theoretical and clinical implications concerning the characterization of cardiac interoception and its assessment in heterogeneous samples, such as those composed of neuropsychiatric patients. •The varied signatures of cardiac interoception are rarely explored in combination.•We address this issue through a novel multidimensional approach.•We tap behavioral correlates with a novel interoceptive accuracy index (md).•We examine key electrophysiological, hemodynamic and socio-emotional dimensions.•Our md index is associated with canonical neurocognitive markers of interoception.

This paper reviews the neurocognitive particularities of subjects with sustained experience in simultaneous interpreting, a highly demanding form of bilingual processing. The literature converges into three broad empirical patterns. First, significant neurocognitive differences, including behavioral enhancements in verbal and executive domains, are observable after only one or two years of training. Second, such effects, both in interpreting students and/or professional interpreters, seem robust for crucial linguistic (e.g., translation) and executive (e.g., working memory) aspects of the activity, but not for more marginally relevant ones (e.g., conflict resolution) – suggesting that they are non-generalizable beyond directly taxed functions. Third, though more tentatively, some of the observed verbal and executive effects seem to be mutually independent and uninfluenced by other bilingual-experience-related factors (e.g., L2 competence), which could highlight their distinctive relation with interpreting practice. In sum, this particular model of expertise sheds novel light on the adaptive capacity of cognitive systems in bilinguals.

Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions –Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)– across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.

Situated models suggest that social concepts are grounded in interpersonal experience. However, few studies have tested this notion experimentally, and none has targeted individuals with reduced social interaction. Here, we assessed comprehension of text-level social and non-social concepts in persons with and without autistic-like traits. Participants read a social and a non-social text and answered questionnaires targeting social and non-social concepts, respectively. We compared behavioral outcomes, gauged their contribution to subject-level classification, and examined their association with validated measures of autism. Persons with autistic-like traits showed selective deficits in grasping text-level social concepts, even adjusting for intelligence, memory, and vocabulary. Also, social concept comprehension was the only variable that significantly classified between groups. Finally, social concept outcomes correlated negatively with measures of autism, including social interaction. Our results suggest that reduced interpersonal experience selectively compromises text-level social concept processing, offering empirical constraints for situated models of social semantics.

A multidimensional social exposome (MSE)—the combined lifespan measures of education, food insecurity, financial status, access to healthcare, childhood experiences, and more—may shape dementia risk and brain health over the lifespan, particularly in underserved regions like Latin America. However, the MSE effects on brain health and dementia are unknown. We evaluated 2211 individuals (controls, Alzheimer’s disease, and frontotemporal lobar degeneration) from a non-representative sample across six Latin American countries. Adverse exposomes associate with poorer cognition in healthy aging. In dementia, more complex exposomes correlate with lower cognitive and functional performance, higher neuropsychiatric symptoms, and brain structural and connectivity alterations in frontal-temporal-limbic and cerebellar regions. Food insecurity, financial resources, subjective socioeconomic status, and access to healthcare emerge as critical predictors. Cumulative exposome measures surpass isolated factors in predicting clinical-cognitive profiles. Multiple sensitivity analyses confirm our results. Findings highlight the need for personalized approaches integrating MSE across the lifespan, emphasizing prevention and interventions targeting social disparities. The social exposome—lifelong social and economic adversity—can shape brain health and dementia risk. Here, the authors show that an adverse social exposome is linked to poorer clinical, cognitive, and brain changes in Latin American older adults.

Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer’s disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings.