Brain health in diverse settings: How age, demographics and cognition shape brain function.

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.

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.

Fatigue in multiple sclerosis is associated with multimodal interoceptive abnormalities.

BACKGROUND: Fatigue ranks among the most common and disabling symptoms in multiple sclerosis (MS). Recent theoretical works have surmised that this trait might be related to alterations across interoceptive mechanisms. However, this hypothesis has not been empirically evaluated. OBJECTIVES: To determine whether fatigue in MS patients is associated with specific behavioral, structural, and functional disruptions of the interoceptive domain. METHODS: Fatigue levels were established via the Modified Fatigue Impact Scale. Interoception was evaluated through a robust measure indexed by the heartbeat detection task. Structural and functional connectivity properties of key interoceptive hubs were tested by magnetic resonance imaging (MRI) and resting-state functional MRI. Machine learning analyses were employed to perform pairwise classifications. RESULTS: Only patients with fatigue presented with decreased interoceptive accuracy alongside decreased gray matter volume and increased functional connectivity in core interoceptive regions, the insula, and the anterior cingulate cortex. Each of these alterations was positively associated with fatigue. Finally, machine-learning analysis with a combination of the above interoceptive indices (behavioral, structural, and functional) successfully discriminated (area under the curve > 90%) fatigued patients from both non-fatigued and healthy controls. CONCLUSION: This study offers unprecedented evidence suggesting that disruptions of neurocognitive markers subserving interoception may constitute a signature of fatigue in MS.

How meaning unfolds in neural time: Embodied reactivations can precede multimodal semantic effects during language processing.

Research on how the brain construes meaning during language use has prompted two conflicting accounts. According to the 'grounded view', word understanding involves quick reactivations of sensorimotor (embodied) experiences evoked by the stimuli, with simultaneous or later engagement of multimodal (conceptual) systems integrating information from various sensory streams. Contrariwise, for the 'symbolic view', this capacity depends crucially on multimodal operations, with embodied systems playing epiphenomenal roles after comprehension. To test these contradictory hypotheses, the present magnetoencephalography study assessed implicit semantic access to grammatically constrained action and non-action verbs (n = 100 per category) while measuring spatiotemporally precise signals from the primary motor cortex (M1, a core region subserving bodily movements) and the anterior temporal lobe (ATL, a putative multimodal semantic hub). Convergent evidence from sensor- and source-level analyses revealed that increased modulations for action verbs occurred earlier in M1 (∼130-190 ms) than in specific ATL hubs (∼250-410 ms). Moreover, machine-learning decoding showed that trial-by-trial classification peaks emerged faster in M1 (∼100-175 ms) than in the ATL (∼345-500 ms), with over 71% accuracy in both cases. Considering their latencies, these results challenge the 'symbolic view' and its implication that sensorimotor mechanisms play only secondary roles in semantic processing. Instead, our findings support the 'grounded view', showing that early semantic effects are critically driven by embodied reactivations and that these cannot be reduced to post-comprehension epiphenomena, even when words are individually classified. Briefly, our study offers non-trivial insights to constrain fine-grained models of language and understand how meaning unfolds in neural time.

Altered neural signatures of interoception in multiple sclerosis.

Multiple sclerosis (MS) patients present several alterations related to sensing of bodily signals. However, no specific neurocognitive impairment has yet been proposed as a core deficit underlying such symptoms. We aimed to determine whether MS patients present changes in interoception-that is, the monitoring of autonomic bodily information-a process that might be related to various bodily dysfunctions. We performed two studies in 34 relapsing-remitting, early-stage MS patients and 46 controls matched for gender, age, and education. In Study 1, we evaluated the heartbeat-evoked potential (HEP), a cortical signature of interoception, via a 128-channel EEG system during a heartbeat detection task including an exteroceptive and an interoceptive condition. Then, we obtained whole-brain MRI recordings. In Study 2, participants underwent fMRI recordings during two resting-state conditions: mind wandering and interoception. In Study 1, controls exhibited greater HEP modulation during the interoceptive condition than the exteroceptive one, but no systematic differences between conditions emerged in MS patients. Patients presented atrophy in the left insula, the posterior part of the right insula, and the right anterior cingulate cortex, with abnormal associations between neurophysiological and neuroanatomical patterns. In Study 2, controls showed higher functional connectivity and degree for the interoceptive state compared with mind wandering; however, this pattern was absent in patients, who nonetheless presented greater connectivity and degree than controls during mind wandering. MS patients were characterized by atypical multimodal brain signatures of interoception. This finding opens a new agenda to examine the role of inner-signal monitoring in the body symptomatology of MS.

The Overt Pronoun Constraint Across Three Dialects of Spanish.

The overt pronoun constraint (OPC) states that, in null subject languages, overt pronoun subjects of embedded clauses cannot be bound by wh- or quantifier antecedents. Through the administration of two written questionnaires, we examined the OPC in 246 monolingual native speakers of three dialects of Spanish, spoken in Barranquilla (Colombia), Santiago (Chile), and Buenos Aires (Argentina). We tested separately the predictions that overt pronouns cannot be bound by wh- antecedents (Experiment 1) and that they cannot be bound by quantifier antecedents (Experiment 2). We found that the OPC was not operative in any of these dialects. In Experiment 1, the percentage of bound answers was approximately the same as the percentage of anaphoric answers. In Experiment 2, the percentage of bound answers was significantly higher than the percentage of anaphoric answers. Implications both for theories of pronoun resolution in null subject languages and for theories of first and second language acquisition are discussed.

Neuroanatomical markers of social cognition in neglected adolescents.

Growing up in neglectful households can impact multiple aspects of social cognition. However, research on neglect's effects on social cognition processes and their neuroanatomical correlates during adolescence is scarce. Here, we aimed to comprehensively assess social cognition processes (recognition of basic and contextual emotions, theory of mind, the experience of envy and Schadenfreude and empathy for pain) and their structural brain correlates in adolescents with legal neglect records within family-based care. First, we compared neglected adolescents (n = 27) with control participants (n = 25) on context-sensitive social cognition tasks while controlling for physical and emotional abuse and executive and intellectual functioning. Additionally, we explored the grey matter correlates of these domains through voxel-based morphometry. Compared to controls, neglected adolescents exhibited lower performance in contextual emotional recognition and theory of mind, higher levels of envy and Schadenfreude and diminished empathy. Physical and emotional abuse and executive or intellectual functioning did not explain these effects. Moreover, social cognition scores correlated with brain volumes in regions subserving social cognition and emotional processing. Our results underscore the potential impact of neglect on different aspects of social cognition during adolescence, emphasizing the necessity for preventive and intervention strategies to address these deficits in this population.

Moral Disengagement in Adolescent Offenders: Its Relationship with Antisocial Behavior and Its Presence in Offenders of the Law and School Norms.

This study focuses on understanding the relationship between moral disengagement mechanisms in adolescents who engage in law-breaking activities and those who violate school norms. To do so, we administered the Mechanisms of Moral Disengagement Scale (MMDS), which evaluates moral justification, euphemistic labeling, advantageous comparison, deflection of responsibility, diffusion of responsibility, distortion of consequences, dehumanization, and attribution of blame, to 366 adolescents (60.1% males (n = 220) and 39.9% females (n = 146)). Our results confirmed the hypothesis that law-breaking adolescents presented a higher degree of moral disengagement than those adolescents who violate school norms. Additionally, we found that adolescents who violated school norms displayed significantly higher levels of dehumanization than the controls, and law-breaking adolescents obtained the highest score in this domain. Our findings allow us to suggest that the presence of the dehumanization mechanism in adolescents who violate school norms could be used as an early indicator of the emergence of antisocial behaviors, since this was the only component of moral disengagement that significantly differentiated this group from the controls in the study.

Structural inequality and temporal brain dynamics across diverse samples.

BACKGROUND: Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored. METHODS: Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries = 10; healthy individuals = 1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed. FINDINGS: Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions. CONCLUSION: These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.

Brain clocks capture diversity and disparity in aging and dementia.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

The face and its emotion: right N170 deficits in structural processing and early emotional discrimination in schizophrenic patients and relatives.

Previous studies have reported facial emotion recognition impairments in schizophrenic patients, as well as abnormalities in the N170 component of the event-related potential. Current research on schizophrenia highlights the importance of complexly-inherited brain-based deficits. In order to examine the N170 markers of face structural and emotional processing, DSM-IV diagnosed schizophrenia probands (n=13), unaffected first-degree relatives from multiplex families (n=13), and control subjects (n=13) matched by age, gender and educational level, performed a categorization task which involved words and faces with positive and negative valence. The N170 component, while present in relatives and control subjects, was reduced in patients, not only for faces, but also for face-word differences, suggesting a deficit in structural processing of stimuli. Control subjects showed N170 modulation according to the valence of facial stimuli. However, this discrimination effect was found to be reduced both in patients and relatives. This is the first report showing N170 valence deficits in relatives. Our results suggest a generalized deficit affecting the structural encoding of faces in patients, as well as the emotion discrimination both in patients and relatives. Finally, these findings lend support to the notion that cortical markers of facial discrimination can be validly considered as vulnerability markers.

A pre-registered, multi-lab non-replication of the action-sentence compatibility effect (ACE).

The Action-sentence Compatibility Effect (ACE) is a well-known demonstration of the role of motor activity in the comprehension of language. Participants are asked to make sensibility judgments on sentences by producing movements toward the body or away from the body. The ACE is the finding that movements are faster when the direction of the movement (e.g., toward) matches the direction of the action in the to-be-judged sentence (e.g., Art gave you the pen describes action toward you). We report on a pre-registered, multi-lab replication of one version of the ACE. The results show that none of the 18 labs involved in the study observed a reliable ACE, and that the meta-analytic estimate of the size of the ACE was essentially zero.

Classifying Parkinson's Disease Patients With Syntactic and Socio-emotional Verbal Measures.

Frontostriatal disorders, such as Parkinson's disease (PD), are characterized by progressive disruption of cortico-subcortical dopaminergic loops involved in diverse higher-order domains, including language. Indeed, syntactic and emotional language tasks have emerged as potential biomarkers of frontostriatal disturbances. However, relevant studies and models have typically considered these linguistic dimensions in isolation, overlooking the potential advantages of targeting multidimensional markers. Here, we examined whether patient classification can be improved through the joint assessment of both dimensions using sentential stimuli. We evaluated 31 early PD patients and 24 healthy controls via two syntactic measures (functional-role assignment, parsing of long-distance dependencies) and a verbal task tapping social emotions (envy, Schadenfreude) and compared their classification accuracy when analyzed in isolation and in combination. Complementarily, we replicated our approach to discriminate between patients on and off medication. Results showed that specific measures of each dimension were selectively impaired in PD. In particular, joint analysis of outcomes in functional-role assignment and Schadenfreude improved the classification accuracy of patients and controls, irrespective of their overall cognitive and affective state. These results suggest that multidimensional linguistic assessments may better capture the complexity and multi-functional impact of frontostriatal disruptions, highlighting their potential contributions in the ongoing quest for sensitive markers of PD.

From discourse to pathology: Automatic identification of Parkinson's disease patients via morphological measures across three languages.

Embodied cognition research on Parkinson's disease (PD) points to disruptions of frontostriatal language functions as sensitive targets for clinical assessment. However, no existing approach has been tested for crosslinguistic validity, let alone by combining naturalistic tasks with machine-learning tools. To address these issues, we conducted the first classifier-based examination of morphological processing (a core frontostriatal function) in spontaneous monologues from PD patients across three typologically different languages. The study comprised 330 participants, encompassing speakers of Spanish (61 patients, 57 matched controls), German (88 patients, 88 matched controls), and Czech (20 patients, 16 matched controls). All subjects described the activities they perform during a regular day, and their monologues were automatically coded via morphological tagging, a computerized method that labels each word with a part-of-speech tag (e.g., noun, verb) and specific morphological tags (e.g., person, gender, number, tense). The ensuing data were subjected to machine-learning analyses to assess whether differential morphological patterns could classify between patients and controls and reflect the former's degree of motor impairment. Results showed robust classification rates, with over 80% of patients being discriminated from controls in each language separately. Moreover, the most discriminative morphological features were associated with the patients' motor compromise (as indicated by Pearson r correlations between predicted and collected motor impairment scores that ranged from moderate to moderate-to-strong across languages). Taken together, our results suggest that morphological patterning, an embodied frontostriatal domain, may be distinctively affected in PD across languages and even under ecological testing conditions.

Negation markers inhibit motor routines during typing of manual action verbs.

We explored whether negation markers recruit inhibitory mechanisms during keyboard-based action-verb typing. In each trial, participants read two sentences: the first featured a context (There is a contract) and the second ended with a relevant verb which had to be immediately typed. Crucially, the verb could describe manual actions, non-manual actions or non-motor processes, with either affirmative (You do sign it) or negative (You don't sign it) polarity. We assessed the impact of verb type and polarity on two typing dimensions: motor programming (lapse between target onset and first keystroke) and motor execution (lapse between first and last keystroke). Negation yielded no effect on motor planning, but it selectively delayed typing execution for manual-action verbs, irrespective of the subjects' typing skills. This suggests that processing negations during comprehension of manual-action sentences recruits inhibitory mechanisms acting on same-effector movements. Our novel finding extends embodied models of language and effector-specific motor-language integration.

Reading Shakespearean tropes in a foreign tongue: Age of L2 acquisition modulates neural responses to functional shifts.

Functional shifts (FSs) - morphosyntactically marked words evoking coherent but novel meanings - are ubiquitous in English and, specially, in Shakespearean literature. While their neural signatures have been explored in native speakers, no study has targeted foreign-language users, let alone comparing early and late bilinguals. Here, we administered a validated FS paradigm to subjects from both populations and evaluated time-frequency modulations evoked by FS and control sentences. Early bilinguals exhibited greater sensitivity towards FSs, indexed by reduced fronto-posterior theta-band oscillations across semantic- and structural-integration windows. Such oscillatory modulations may represent a key marker of age-of-acquisition effects during foreign-language wordplay processing.

Explicit and implicit monitoring in neurodegeneration and stroke.

Monitoring is a complex multidimensional neurocognitive phenomenon. Patients with fronto-insular stroke (FIS), behavioural variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) show a lack of self-awareness, insight, and self-monitoring, which translate into anosognosia and daily behavioural impairments. Notably, they also present damage in key monitoring areas. While neuroscientific research on this domain has accrued in recent years, no previous study has compared monitoring performance across these brain diseases and none has applied a multiple lesion model approach combined with neuroimaging analysis. Here, we evaluated explicit and implicit monitoring in patients with focal stoke (FIS) and two types of dementia (bvFTD and AD) presenting damage in key monitoring areas. Participants performed a visual perception task and provided two types of report: confidence (explicit judgment of trust about their performance) and wagering (implicit reports which consisted in betting on their accuracy in the perceptual task). Then, damaged areas were analyzed via structural MRI to identify associations with potential behavioral deficits. In AD, inadequate confidence judgments were accompanied by poor wagering performance, demonstrating explicit and implicit monitoring impairments. By contrast, disorders of implicit monitoring in FIS and bvFTD patients occurred in the context of accurate confidence reports, suggesting a reduced ability to turn self-knowledge into appropriate wagering conducts. MRI analysis showed that ventromedial compromise was related to overconfidence, whereas fronto-temporo-insular damage was associated with excessive wagering. Therefore, joint assessment of explicit and implicit monitoring could favor a better differentiation of neurological profiles (frontal damage vs AD) and eventually contribute to delineating clinical interventions.

Consistent Gradient of Performance and Decoding of Stimulus Type and Valence From Local and Network Activity.

The individual differences approach focuses on the variation of behavioral and neural signatures across subjects. In this context, we searched for intracranial neural markers of performance in three individuals with distinct behavioral patterns (efficient, borderline, and inefficient) in a dual-valence task assessing facial and lexical emotion recognition. First, we performed a preliminary study to replicate well-established evoked responses in relevant brain regions. Then, we examined time series data and network connectivity, combined with multivariate pattern analyses and machine learning, to explore electrophysiological differences in resting-state versus task-related activity across subjects. Next, using the same methodological approach, we assessed the neural decoding of performance for different dimensions of the task. The classification of time series data mirrored the behavioral gradient across subjects for stimulus type but not for valence. However, network-based measures reflected the subjects' hierarchical profiles for both stimulus types and valence. Therefore, this measure serves as a sensitive marker for capturing distributed processes such as emotional valence discrimination, which relies on an extended set of regions. Network measures combined with classification methods may offer useful insights to study single subjects and understand inter-individual performance variability. Promisingly, this approach could eventually be extrapolated to other neuroscientific techniques.