Browsing by Author "Hesse, Eugenia"
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Publication Automated free speech analysis reveals distinct markers of Alzheimer's and frontotemporal dementia(2024) Lopes da Cunha, Pamela; Ruiz, Fabián; Ferrante, Franco; Sterpin, Lucas; Ibáñez, Agustín; Slachevsky Chonchol, Andrea; Matallana, Diana; Martínez, Ángela; Hesse, Eugenia; García, AdolfoDementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer's disease (AD) compromises the processing of entities expressed by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective with increased third-person references. Yet, no study has examined whether these patterns can be captured in connected speech via natural language processing tools. To tackle such gaps, we asked 96 participants (32 AD patients, 32 bvFTD patients, 32 healthy controls) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers (via part-of-speech and morphological tagging). We also extracted objective properties (frequency, phonological neighborhood, length, semantic variability) from each content word. In our main study (with 21 AD patients, 21 bvFTD patients, and 21 healthy controls), we used inferential statistics and machine learning for group-level and subject-level discrimination. The above linguistic features were correlated with patients' scores in tests of general cognitive status and executive functions. We found that, compared with HCs, (i) AD (but not bvFTD) patients produced significantly fewer nouns, (ii) bvFTD (but not AD) patients used significantly more third-person markers, and (iii) both patient groups produced more frequent words. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.71). A generalizability test, with a model trained on the entire main study sample and tested on hold-out samples (11 AD patients, 11 bvFTD patients, 11 healthy controls), showed even better performance, with AUCs of 0.76 and 0.83 for AD and bvFTD, respectively. No linguistic feature was significantly correlated with cognitive test scores in either patient group. These results suggest that specific cognitive traits of each disorder can be captured automatically in connected speech, favoring interpretability for enhanced syndrome characterization, diagnosis, and monitoring.Publication Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia(2024) Ferrante, Franco J.; Migeot, Joaquín; Birba, Agustina; Amoruso, Lucía; Pérez, Gonzalo; Hesse, Eugenia; Tagliazucchi, Enzo; Estienne, Claudio; Serrano, Cecilia; Slachevsky, Andrea; Matallana, Diana; Reyes, Pablo; Ibáñez, Agustín; Fittipaldi, Sol; Gonzalez, Cecilia; García, Adolfo M.Introduction: Verbal fluency tasks are common in Alzheimer's disease (AD) assessments. Yet, standard valid response counts fail to reveal disease-specific semantic memory patterns. Here, we leveraged automated word-property analysis to capture neurocognitive markers of AD vis-à-vis behavioral variant frontotemporal dementia (bvFTD). Methods: Patients and healthy controls completed two fluency tasks. We counted valid responses and computed each word's frequency, granularity, neighborhood, length, familiarity, and imageability. These features were used for group-level discrimination, patient-level identification, and correlations with executive and neural (magnetic resonanance imaging [MRI], functional MRI [fMRI], electroencephalography [EEG]) patterns. Results: Valid responses revealed deficits in both disorders. Conversely, frequency, granularity, and neighborhood yielded robust group- and subject-level discrimination only in AD, also predicting executive outcomes. Disease-specific cortical thickness patterns were predicted by frequency in both disorders. Default-mode and salience network hypoconnectivity, and EEG beta hypoconnectivity, were predicted by frequency and granularity only in AD. Discussion: Word-property analysis of fluency can boost AD characterization and diagnosis. Highlights: We report novel word-property analyses of verbal fluency in AD and bvFTD. Standard valid response counts captured deficits and brain patterns in both groups. Specific word properties (e.g., frequency, granularity) were altered only in AD. Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD. Word-property analysis of fluency can boost AD characterization and diagnosis.Item The power of knowledge about dementia in Latin America across health professionals working on aging(2020) Ibanez, Agustin; Flichtentrei, Daniel; Hesse, Eugenia; Dottori, Martin; Tomio, Ailin; Slachevsky, Andrea; Serrano, Cecilia M; Gonzalez-Billaut, Christian; Custodio, Nilton; Miranda, Claudia; Bustin, Julian; Cetckovitch, Marcelo; Torrente, Fernando; Olavarria, Loreto; Leon, Tomas; Costa Beber, Barbara; Bruki, Sonia; Suemoto, Claudia K.; Nitrini, Ricardo; Miller, Bruce L.; Yokoyama, Jennifer S.Methods: We investigated opinions among health professionals working on aging in LACs (N =3365) with regression models including expertise-related information (public policies, BI), individual differences (work, age, academic degree), and location. Results: Experts specified low public policy knowledge (X2 = 41.27, P < .001), high levels of stigma (X2 = 2636.37, P < .001), almost absent BI knowledge (X2 = 56.58, P < .001), and needs for regional diagnostic manuals (X2 = 2893.63, df = 3, P < .001) and data-sharing platforms (X2 = 1267.5, df = 3, P < .001). Lack of dementia knowledge was modulated by different factors. An implemented BI-based treatment for a proposed prevention program improved perception across experts. Discussion: Our findings help to prioritize future potential actions of governmental agencies and non-governmental organizations (NGOs) to improve LACs’ dementia knowledge.Item Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing(Nature Publishing Group, 2017) Dottori, Martin; Sedeño, Lucas; Martorell, Miguel; Alifano, Florencia; Hesse, Eugenia; Mikulan, Ezequiel; García, Adolfo; Ruiz-Tagle, Amparo; Lillo, Patricia; Slachevsky, Andrea; Serrano, Cecilia; Fraiman, Daniel; Ibañez, AgustinDeveloping 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.Item Your perspective and my benefit: multiple lesion models of self-other integration strategies during social bargaining(01/09/2016) Melloni, Margherita; Billeke, Pablo; Baez, Sandra; Hesse, Eugenia; De la Fuente, Laura; Forno, Gonzalo; Birba, Agustina; García-Cordero, Indira; Serrano, Cecilia; Plastino, Angelo; Slachevsky, Andrea; Huepe, David; Sigman, Mariano; Manes, Facundo; García, Adolfo; Sedeño, Lucas; Ibáñez, AgustínRecursive social decision-making requires the use of flexible, context-sensitive long-term strategies for negotiation. To succeed in social bargaining, participants' own perspectives must be dynamically integrated with those of interactors to maximize self-benefits and adapt to the other's preferences, respectively. This is a prerequisite to develop a successful long-term self-other integration strategy. While such form of strategic interaction is critical to social decision-making, little is known about its neurocognitive correlates. To bridge this gap, we analysed social bargaining behaviour in relation to its structural neural correlates, ongoing brain dynamics (oscillations and related source space), and functional connectivity signatures in healthy subjects and patients offering contrastive lesion models of neurodegeneration and focal stroke: behavioural variant frontotemporal dementia, Alzheimer's disease, and frontal lesions. All groups showed preserved basic bargaining indexes. However, impaired self-other integration strategy was found in patients with behavioural variant frontotemporal dementia and frontal lesions, suggesting that social bargaining critically depends on the integrity of prefrontal regions. Also, associations between behavioural performance and data from voxel-based morphometry and voxel-based lesion-symptom mapping revealed a critical role of prefrontal regions in value integration and strategic decisions for self-other integration strategy. Furthermore, as shown by measures of brain dynamics and related sources during the task, the self-other integration strategy was predicted by brain anticipatory activity (alpha/beta oscillations with sources in frontotemporal regions) associated with expectations about others' decisions. This pattern was reduced in all clinical groups, with greater impairments in behavioural variant frontotemporal dementia and frontal lesions than Alzheimer's disease. Finally, connectivity analysis from functional magnetic resonance imaging evidenced a fronto-temporo-parietal network involved in successful self-other integration strategy, with selective compromise of long-distance connections in frontal disorders. In sum, this work provides unprecedented evidence of convergent behavioural and neurocognitive signatures of strategic social bargaining in different lesion models. Our findings offer new insights into the critical roles of prefrontal hubs and associated temporo-parietal networks for strategic social negotiation