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Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations
(2024)
Sebastian Moguilner
;
Sandra Baez
;
Hernan Hernandez
;
Joaquín Migeot
;
Agustina Legaz
9Scopus© Citations 1
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Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations
(2024)
Sebastian Moguilner
;
Sandra Baez
;
Hernan Hernandez
;
Joaquín Migeot
;
Agustina Legaz
<jats:title>Abstract</jats:title><jats:p>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 (<jats:italic>R</jats:italic>² = 0.37, <jats:italic>F</jats:italic>² = 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.</jats:p>
Scopus© Citations 33 2
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Author Correction: The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds
(2024)
Pavel Prado
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Vicente Medel
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Raul Gonzalez-Gomez
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Agustín Sainz-Ballesteros
;
Victor Vidal
Scopus© Citations 1 1
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The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds
(2023)
Pavel Prado
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Vicente Medel
;
Raul Gonzalez-Gomez
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Agustín Sainz-Ballesteros
;
Victor Vidal
The Latin American Brain Health Institute (BrainLat) has released a unique multimodal neuroimaging dataset of 780 participants from Latin American. The dataset includes 530 patients with neurodegenerative diseases such as Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (bvFTD), multiple sclerosis (MS), Parkinson’s disease (PD), and 250 healthy controls (HCs). This dataset (62.7 ± 9.5 years, age range 21–89 years) was collected through a multicentric effort across five Latin American countries to address the need for affordable, scalable, and available biomarkers in regions with larger inequities. The BrainLat is the first regional collection of clinical and cognitive assessments, anatomical magnetic resonance imaging (MRI), resting-state functional MRI (fMRI), diffusion-weighted MRI (DWI), and high density resting-state electroencephalography (EEG) in dementia patients. In addition, it includes demographic information about harmonized recruitment and assessment protocols. The dataset is publicly available to encourage further research and development of tools and health applications for neurodegeneration based on multimodal neuroimaging, promoting the assessment of regional variability and inclusion of underrepresented participants in research.
30Scopus© Citations 22
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Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia
(2023)
Franco J. Ferrante
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Joaquín Migeot
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Agustina Birba
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Lucía Amoruso
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Gonzalo Pérez
<jats:title>Abstract</jats:title><jats:sec><jats:title>INTRODUCTION</jats:title><jats:p>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).</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>DISCUSSION</jats:title><jats:p>Word‐property analysis of fluency can boost AD characterization and diagnosis.</jats:p></jats:sec><jats:sec><jats:title>Highlights</jats:title><jats:p><jats:list list-type="bullet"> <jats:list-item><jats:p>We report novel word‐property analyses of verbal fluency in AD and bvFTD.</jats:p></jats:list-item> <jats:list-item><jats:p>Standard valid response counts captured deficits and brain patterns in both groups.</jats:p></jats:list-item> <jats:list-item><jats:p>Specific word properties (e.g., frequency, granularity) were altered only in AD.</jats:p></jats:list-item> <jats:list-item><jats:p>Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD.</jats:p></jats:list-item> <jats:list-item><jats:p>Word‐property analysis of fluency can boost AD characterization and diagnosis.</jats:p></jats:list-item> </jats:list></jats:p></jats:sec>
3Scopus© Citations 10
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Multidimensional inhibitory signatures of sentential negation in behavioral variant frontotemporal dementia
(2022)
Mariano N Díaz-Rivera
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Agustina Birba
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Sol Fittipaldi
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Débora Mola
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Yurena Morera
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Processing of linguistic negation has been associated to inhibitory brain mechanisms. However, no study has tapped this link via multimodal measures in patients with core inhibitory alterations, a critical approach to reveal direct neural correlates and potential disease markers.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>Here we examined oscillatory, neuroanatomical, and functional connectivity signatures of a recently reported Go/No-go negation task in healthy controls and behavioral variant frontotemporal dementia (bvFTD) patients, typified by primary and generalized inhibitory disruptions. To test for specificity, we also recruited persons with Alzheimer's disease (AD), a disease involving frequent but nonprimary inhibitory deficits.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>In controls, negative sentences in the No-go condition distinctly involved frontocentral delta (2–3 Hz) suppression, a canonical inhibitory marker. In bvFTD patients, this modulation was selectively abolished and significantly correlated with the volume and functional connectivity of regions supporting inhibition (e.g. precentral gyrus, caudate nucleus, and cerebellum). Such canonical delta suppression was preserved in the AD group and associated with widespread anatomo-functional patterns across non-inhibitory regions.</jats:p> </jats:sec> <jats:sec> <jats:title>Discussion</jats:title> <jats:p>These findings suggest that negation hinges on the integrity and interaction of spatiotemporal inhibitory mechanisms. Moreover, our results reveal potential neurocognitive markers of bvFTD, opening a new agenda at the crossing of cognitive neuroscience and behavioral neurology.</jats:p> </jats:sec>
Scopus© Citations 18 4
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Your perspective and my benefit: multiple lesion models of self-other integration strategies during social bargaining
(2016)
Margherita Melloni
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BILLEKE BOBADILLA, PABLO ERNESTO
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Sandra Baez
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Eugenia Hesse
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Laura de la Fuente
Scopus© Citations 100 1 1
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Interoception Primes Emotional Processing: Multimodal Evidence from Neurodegeneration
(2021)
Paula C. Salamone
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Agustina Legaz
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Lucas Sedeño
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Sebastián Moguilner
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Matías Fraile-Vazquez
7Scopus© Citations 80
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Multi-feature computational framework for combined signatures of dementia in underrepresented settings
(2022)
Sebastian Moguilner
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Agustina Birba
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Sol Fittipaldi
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Cecilia Gonzalez-Campo
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Enzo Tagliazucchi
<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective.</jats:italic> The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings. <jats:italic>Approach</jats:italic>. We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat). <jats:italic>Main results</jats:italic>. The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens). <jats:italic>Results</jats:italic>. Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data. <jats:italic>Significance</jats:italic>. The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.</jats:p>
Scopus© Citations 19 1 3
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Multimodal mechanisms of human socially reinforced learning across neurodegenerative diseases
(2021)
Agustina Legaz
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Sofía Abrevaya
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Martín Dottori
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Cecilia González Campo
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Agustina Birba
<jats:title>Abstract</jats:title> <jats:p>Social feedback can selectively enhance learning in diverse domains. Relevant neurocognitive mechanisms have been studied mainly in healthy persons, yielding correlational findings. Neurodegenerative lesion models, coupled with multimodal brain measures, can complement standard approaches by revealing direct multidimensional correlates of the phenomenon.</jats:p> <jats:p>To this end, we assessed socially reinforced and non-socially reinforced learning in 40 healthy participants as well as persons with behavioural variant frontotemporal dementia (n = 21), Parkinson’s disease (n = 31) and Alzheimer’s disease (n = 20). These conditions are typified by predominant deficits in social cognition, feedback-based learning and associative learning, respectively, although all three domains may be partly compromised in the other conditions. We combined a validated behavioural task with ongoing EEG signatures of implicit learning (medial frontal negativity) and offline MRI measures (voxel-based morphometry).</jats:p> <jats:p>In healthy participants, learning was facilitated by social feedback relative to non-social feedback. In comparison with controls, this effect was specifically impaired in behavioural variant frontotemporal dementia and Parkinson’s disease, while unspecific learning deficits (across social and non-social conditions) were observed in Alzheimer’s disease. EEG results showed increased medial frontal negativity in healthy controls during social feedback and learning. Such a modulation was selectively disrupted in behavioural variant frontotemporal dementia. Neuroanatomical results revealed extended temporo-parietal and fronto-limbic correlates of socially reinforced learning, with specific temporo-parietal associations in behavioural variant frontotemporal dementia and predominantly fronto-limbic regions in Alzheimer’s disease. In contrast, non-socially reinforced learning was consistently linked to medial temporal/hippocampal regions. No associations with cortical volume were found in Parkinson’s disease. Results are consistent with core social deficits in behavioural variant frontotemporal dementia, subtle disruptions in ongoing feedback-mechanisms and social processes in Parkinson’s disease and generalized learning alterations in Alzheimer’s disease. This multimodal approach highlights the impact of different neurodegenerative profiles on learning and social feedback.</jats:p> <jats:p>Our findings inform a promising theoretical and clinical agenda in the fields of social learning, socially reinforced learning and neurodegeneration.</jats:p>
2Scopus© Citations 28

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