Neuroscience Ireland Annual Conference
LATEST RESEARCH
Gillan Lab Researchers published in Nature Communications!
Using language in social media posts to study the network dynamics of depression longitudinally
This study led by Sean Kelley from the Gillan Lab constructed personalised, within-subject, networks based on depression-relevant linguistic features from Twitter data. It was found that people with greater depression severity had higher overall network connectivity between depression-relevant linguistic features than those with less severe depression. Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. Increasing evidence suggests that depression network connectivity may be a risk factor for transitioning and sustaining a depressive state.
The study analysed social media (Twitter) data from 946 participants who retrospectively self-reported the dates of any depressive episodes in the past 12 months and current depressive symptom severity. Personalised, within-subject, networks were constructed based on depression-related linguistic features. An association existed between current depression severity and 8 out of 9 text features examined. Individuals with greater depression severity had higher overall network connectivity between depression-relevant linguistic features than those with lesser severity. The study observed within-subject changes in overall network connectivity associated with the dates of a self-reported depressive episode. The connectivity within personalized networks of depression-associated linguistic features may change dynamically with changes in current depression symptoms.
You can find the full publication in Nature Communications.
Kelley, S. W., & Gillan, C. M. (2022). Using language in social media posts to study the network dynamics of depression longitudinally. Nature communications, 13(1), 1-11.
Multimodal mechanisms of human socially reinforced learning across neurodegenerative diseases
Social reinforcement is a powerful facilitator of learning. However, most evidence
comes from healthy individuals, offering limited (correlational) information to identify critical
neural signatures. The neurodegenerative lesion model approach partially overcomes these limitations by revealing direct links between affected brain mechanisms and behavioral performance. This study lead by Ibanez Lab in collaboration with the Gillan Lab assessed socially reinforced and non-socially reinforced learning in healthy participants as well as persons with behavioral variant frontotemporal dementia (bvFTD), Parkinson’s disease (PD), and Alzheimer’s disease (AD). In healthy participants, learning was facilitated by social feedback relative to non-social feedback. In comparison with controls, this effect was specifically impaired in vcFTD and PD, 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 behavioral variant frontotemporal dementia. Neuroanatomical results revealed extended temporo-parietal and fronto-limbic correlates of socially reinforced learning, with specific temporo-parietal associations in behavioral 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 behavioral 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. Our findings inform a promising theoretical and clinical agenda in the fields of social learning, socially-reinforced learning and neurodegeneration.
You can find the full publication here.
Legaz A, Abrevaya S, Dottori M, Campo CG, Birba A, Caro MM, Aguirre J, Slachevsky A, Aranguiz R, Serrano C, Gillan CM, Leroi I, García AM, Fittipaldi S, Ibañez A. Multimodal mechanisms of human socially reinforced learning across neurodegenerative diseases. Brain. 2021 Sep 16:awab345. doi: 10.1093/brain/awab345. PMID: 34529034.
Predicting and characterizing neurodegenerative subtypes with multimodal neurocognitive signatures of social and cognitive processes
Although social cognition is critically compromised across neurodegenerative disorders (including behavioral variant frontotemporal dementia, bvFTD; Alzheimer’s disease, AD; and Parkinson disease, PD) their power to predict diagnosis subtype and their neuroanatomical signatures (brain atrophy and connectivity) are unclear. This study led by Ibanez Lab tackled these gaps by developing multiple group discriminant function analyses (MDAs) to perform a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors. Although all patient groups revealed deficits in social cognition, CS, and EF, the classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3% , social cognition), AD versus PD (98.6% , social cognition + CS), and bvFTD versus AD (71.7% , social cognition + CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions. Thus, standardized validated measures of social cognition, in combination with cognitive screening, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses
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You can find the full publication here.
Ibañez A, Fittipaldi S, Trujillo C, Jaramillo T, Torres A, Cardona JF, Rivera R, Slachevsky A, García A, Bertoux M, Baez S. Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes. J Alzheimers Dis. 2021 Jul 15. doi: 10.3233/JAD-210163.
Interoception primes emotional Processing at behavioral multimodal neuroimaging levels: Evidence from Neurodegeneration
Are external social signals differently appraised by the brain depending on our inner body states?
This study lead by Ibanez Lab evidenced that focusing on internal body states (cardiac interoception) regulates the appraisal of others’ emotions at behavioral, electrophysiological, neuroanatomical, and functional connectivity levels. Moreover, frontotemporal dementia, a condition characterized by socio-emotional impairments, presented convergent and multimodal neurocognitive markers of interoceptive disruption (selective behavioral deficits, abnormal heart evoked potential modulations, insular-cingulate atrophy, and salience network alterations) during emotional processing, in comparisons with Alzheimer’s, Parkinson’s disease and controls. These results support a predictive coding account of interoceptive emotions and a disbalanced allostasis in frontotemporal dementia.
You can find the full publication here.
Salamone PC, Legaz A, Sedeño L, Moguilner S, Fraile-Vazquez M, Campo CG, Fittipaldi S, Yoris A, Miranda M, Birba A, Galiani A, Abrevaya S, Neely A, Caro MM, Alifano F, Villagra R, Anunziata F, Okada de Oliveira M, Pautassi RM, Slachevsky A, Serrano C, García AM, Ibañez A. Interoception Primes Emotional Processing: Multimodal Evidence from Neurodegeneration. J Neurosci. 2021 May 12;41(19):4276-4292. doi: 10.1523/JNEUROSCI.2578-20.2021.
Model-Based Planning Deficits in Compulsivity Are Linked to Faulty Neural Representations of Task Structure
Compulsivity is linked to poorer performance on tasks that require model-based planning, but it is unclear what precise mechanisms underlie this deficit. Do compulsive individuals fail to engage cognitive control at the time of choice? Or do they have difficulty in building and maintaining an accurate representation of their environment, the foundation needed to behave in a goal-directed manner?
This collaboration between the Gillan Lab and O'Connell Lab examined reaction time and EEG measures in 192 individuals who performed a two-step decision-making task. The study found that compulsive individuals are less sensitive to surprising action–state transitions, where they slow down less and show less alpha band suppression following a rare transition. These findings implicate failures in maintaining an accurate model of the world in model-based planning deficits in compulsivity.
You can find the full publication here.
Seow, T. X., Benoit, E., Dempsey, C., Jennings, M., Maxwell, A., O'Connell, R., & Gillan, C. M. (2021). Model-based planning deficits in compulsivity are linked to faulty neural representations of task structure. Journal of Neuroscience, 41(30), 6539-6550.
The Whelan Lab introduces β-burst volume as a key measure of human inhibition
Enz et al. identified a novel neural measure "β-burst volume" that predicts successful response inhibition, which can become impaired during normal aging and in a wide range of psychiatric diseases.
You can find the full publication here.
Enz, N., Ruddy, K. L., Rueda-Delgado, L. M., & Whelan, R. (2021). Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition. Journal of Neuroscience, 41(23), 5069-5079.
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