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McCabe, C.

Publications and source records attributed to McCabe, C..

At least 19 recordsLinked to original sources

Population and community responses to the fast, slow, and seasonal components of environmental variation

Theory suggests that different components of environmental fluctuations, from daily and seasonal cycles to multidecadal trends, can have distinct and even opposing effects on species abundances and community dynamics, depending on their specific adaptations. But empirical research that deconstructs the influence of these different cycles on communities is lacking. Here, we used long-term biological monitoring data together with flow records of rivers across New Zealand to (i) investigate the role of fast, slow, and seasonal river-flow fluctuations in structuring macroinvertebrate communities; and (ii) to assess whether life-history and mobility traits mediate the response. Using joint species distribution models, we found striking differences in taxon and community responses to the different components of river flow variation. Responses to slow fluctuations were generally stronger and better predicted by traits, while responses to seasonal fluctuations were highly heterogeneous. Fast increases in flow, typical of flooding events, had pervasive negative effects on species abundances, but the severity of impact partly depended on mobility traits. Our results suggest that different ecological mechanisms underpin the response to distinct environmental fluctuations, highlighting the value of jointly considering multiple temporal scales of variation and species functional traits to understand and predict how communities reorganise under fluctuating environmental regimes.

ecology↗

DISCO-seq: 3D single-cell transcriptomics of intact biological systems

Single-cell transcriptomics has transformed tissue analysis, yet current methods struggle to integrate whole-tissue 3D architecture. Conventional techniques restrict molecular profiling to pre-selected 2D sections, losing systemic context and introducing anatomical bias by sampling less than 0.001% of a whole organism. To overcome these challenges, we developed DISCO-seq, a tissue-clearing chemistry that enables superior RNA accessibility compared to fresh or fixed tissues. DISCO-seq integrates whole-organ or organism 3D imaging with both untargeted and targeted transcriptomics, yielding high-quality RNA from cleared tissues comparable to standard samples. We demonstrate its versatility by investigating tumor heterogeneity in a syngeneic glioblastoma mouse model, using 3D imaging to identify spatially distinct microenvironments and characterize their unique transcriptomic signatures. Moreover, DISCO-seq enabled unbiased, whole-body mapping of SARS-CoV-2 S1 protein deposition in mice, followed by transcriptomic profiling of spatially defined niches. By bridgingmesoscale 3D imaging with single-cell transcriptomics, DISCO-seq establishes a paradigm for anatomically contextualized, hypothesis-free tissue interrogation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/693352v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@758ee5org.highwire.dtl.DTLVardef@1f862c6org.highwire.dtl.DTLVardef@1cf3b9org.highwire.dtl.DTLVardef@c5082e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDISCO-seq integrates RNA-preserving tissue-clearing chemistry with whole-organ or organism 3D imaging, enabling anatomically unbiased single-cell transcriptomics. C_LIO_LIDISCO-seq yields RNA quality and transcriptome profiles equivalent to those obtained from matched fresh or fixed tissues. C_LIO_LIDISCO-seq identifies discrete glioblastoma microenvironments and defines their transcriptomic states within the intact brain. C_LIO_LIDISCO-seq enables whole-body mapping of SARS-CoV-2 S1 and uncovers region-specific immune and metabolic responses across anatomical niches. C_LI Supplementary movies can be seen at: http://discotechnologies.org/DISCO-seq/

systems biology↗

Human Neural Synergy when combining Stevia with a Flavor Modifer and the Neural effects of Sucrose vs Stevia

There is a drive to improve the acceptability of sweeteners like stevia by reducing their off-tastes. The main aim was to examine the synergistic neural effects of combining stevia with a flavor modifier and secondly to examine stevia vs. sucrose due to limited human neuroimaging data and concerns that sweeteners may be more addictive than sugar. In a within-subjects fMRI study, 34 healthy adults (Mean age = 25) tasted four conditions: stevia, stevia plus a flavor modifier, the modifier alone, and sucrose. We analyzed whole-brain responses and focused on regions of interest (ROIs) including the insula, postcentral gyrus, and hypothalamus (identified via meta-analysis of sweet taste processing), as well as the nucleus accumbens (NAcc) and amygdala due to their roles in reward and aversion. Stevia combined with the modifier evoked super-additive responses in the postcentral gyrus, parietal cortex, and occipital gyrus (p < 0.05, FWE-corrected). Compared to stevia alone, the stevia-modifier combination elicited reduced hypothalamic activity (p = 0.008) and the hypothalamus tracked pleasantness and mouth fullness only in this condition. The NAcc tracked mouth fullness more for the modified stevia than for stevia alone, and the amygdala tracked bitterness only in the plain stevia condition. Sucrose elicited higher postcentral gyrus activation than stevia (p = 0.01). We provide first evidence that combining stevia with a flavour modifier reveals synergistic neural activity associated with taste sensation, intensity and multisensory integration. Adding a modifier to stevia could increase unconscious desirability for stevia by masking its bitterness and increasing its mouth fullness.

neuroscience↗

Neurally-informed modelling unravels a single evidence accumulation process for choices and subsequent confidence reports

How is the content of our decision making processes translated into subjective confidence? Current computational models designed to account for choice- and confidence-reporting behaviour disagree on two fundamental questions: do choice and confidence reports arise from shared versus distinct evaluation processes and what stopping-rule is applied to evidence sampling for confidence reports? Here, we employed a neurally-informed modelling approach in which competing models were compared both based on their fits to behaviour and their ability to recapitulate human electrophysiological signatures of evidence accumulation. Participants made self-paced confidence reports after indicating perceptual choices on a random dot motion task. Model comparison based on behavioural data alone showed that boundary-based stopping rules for confidence reporting provided superior fits than time-based stopping rules. However, results were inconclusive when comparing models with a single evidence accumulation process dictating choice and confidence reports compared to models invoking distinct processes. Importantly, the single-process model markedly outperformed the other models in its ability to account for the observed neural evidence accumulation patterns, including the fact that signal amplitudes predicted reported confidence several hundred milliseconds before the initial choice had been reported. Our study demonstrates that choice behaviour, subjective confidence, and associated neural decision signals can be jointly explained by a model invoking a single process of evidence accumulation with separate boundaries for the initial choice and subsequent confidence reports. Significance StatementHow does the brain generate representations of subjective confidence from categorical decisions? While some models suggest that confidence arises from a read-out of the same information that determined the initial choice, others propose that confidence is determined by a distinct computational process. Definitively testing these alternative accounts has proven difficult because they often make similar predictions for behavioural data. Here, we employ a novel neurally-informed modelling method that evaluates models based on their ability to reproduce key neurophysiological signatures of choice and confidence in addition to behaviour. Our results indicate that choices and subjective confidence are determined by a shared sensory evidence accumulation process.

neuroscience↗

Retro Nasal blockade reduces the Neural Processing of Sucrose in the Human Brain

It is assumed "Non-volatile" tastes like sucrose do not activate retro nasal pathways. Recent studies find that sucrose when aerosolized, can reach the retro nasal olfactory region and be perceived. The neural mechanisms by which the human brain interprets sucrose via retro nasal pathways is unknown. We examined neural activity to sucrose with a nose clip on (blocking retro nasal) and nose clip off, in healthy adults (N=34, mean 25 yrs.). We examined the whole brain and ROIs involved in taste, smell, attention, reward and multi-modal integration; insula, postcentral gyrus, amygdala, olfactory cortex, subgenual and pregenual anterior cingulate, nucleus accumbens and OFC. We also examined correlations with subjective ratings of pleasantness and mouth fullness. We also examined the effect of the nose clip on the time to peak activity for sucrose using the bold signal time course. The nose clip on vs off reduced the subjective experience of mouth fullness. Neural activity to sucrose was reduced with the nose clip on in the primary taste, olfactory, attention and reward ROIs and in the rolandic operculum, lingual gyrus and precuneus in the whole brain analyses. The olfactory and prefrontal cortex ROIs tracked subjective mouth fullness, but this was not apparent with the nose clip on. Blocking retro nasal sensation reduces subjective and neural responses to sucrose taste. Retro nasal sensations could play a role in "pure" taste perception. Developing more satisfying low-sugar foods could be achieved by enhancing the perception of sweetness through aroma modulation.

neuroscience↗

Single-cell transcriptomics reveals the impact of sex and age in the healthy human liver

Background & AimsThe liver is a vital organ composed of parenchymal, nonparenchymal, and immune cell populations. Single-cell sequencing approaches now provide the opportunity to understand how sex and age influence gene expression and cellular function across cell types within the liver. MethodsWe analyzed the cellular composition and interactions for the human liver through single-nucleus RNA sequencing (snRNA-seq), incorporating insights from 37 healthy liver samples. The dataset contains cells from female and male donors spanning more than seven decades of life, and analysis was performed to evaluate the impact of sex and age on differential gene expression, pathway enrichment, and predicted ligand-receptor and protein-protein interactions. ResultsExcluding the X and Y chromosomes, we identified 374 genes uniquely enriched in cells of the female liver and 520 genes enriched in cells of the male liver. Differential expression analysis defined unique circuitries enriched within each cell type between females and males and their impact on cell-cell communication and response to external signals, including enrichment of cholesterol/lipid metabolism, transforming growth factor beta (TGF-beta) signaling, and fibronectin (FN1) production in female cells and bone morphogenic protein (BMP) signaling in male cells. With increased age, we observe a greater diversity in gene expression, including enrichment of genes that regulate neuregulin (NGR) signaling at older ages, while genes regulating insulin growth factor (IGF) signaling are enriched at younger ages. Senescence signatures were also identified for each cell type within the liver. ConclusionsThese results define the activities of healthy cell types within the liver across sex and age and provide a foundation for studies to examine how ancestry, geography, and disease states influence liver function within these contexts. Impact and ImplicationsOur study analyzes 37 human liver samples at the single-cell level to understand how sex and age influence gene expression, cell interactions, and response to signals across liver cell types and sub-types. These findings are of particular significance for researchers who need to understand how sex and age may influence the response of individual cell types to injury or treatment of injury. This dataset will also provide a healthy reference for future studies to understand how ancestry, geography, and disease states shape liver biology across age and sex.

cell biology↗

Population genomics and molecular epidemiology of wheat powdery mildew in Europe

Agricultural diseases are a major threat to sustainable food production. Yet, for many pathogens we know exceptionally little about their epidemiological and population dynamics, and this knowledge gap is slowing the development of efficient control strategies. Here we study the population genomics and molecular epidemiology of wheat powdery mildew, a disease caused by the biotrophic fungus Blumeria graminis forma specialis tritici (Bgt). We sampled Bgt for two consecutive years, 2022 and 2023, from 22 countries in Europe and surrounding regions, and compiled a genomic dataset of 415 Bgt isolates. We found one single epidemic unit in the north of Europe, consisting of a highly homogeneous population. Conversely, the south of Europe hosts smaller local populations which are less interconnected. In addition, we show that the population structure can be largely predicted by the prevalent wind patterns. We identified several loci that were under selection in the recent past, including fungicide targets and avirulence genes. Some of these loci are common between populations, while others are not, suggesting different local selective pressures. We reconstructed the evolutionary history of one of these loci, AvrPm17, coding for an effector recognized by the wheat receptor Pm17. We found evidence for a soft sweep on standing genetic variation. Multiple AvrPm17 haplotypes, which can partially escape recognition by Pm17, spread rapidly throughout the continent upon its introduction in the early 2000s. We also identified a new virulent variant, which emerged more recently and can evade Pm17 resistance altogether. Overall, we highlight the potential of genomic surveillance in resolving the evolutionary and epidemiological dynamics of agricultural pathogens, as well as in guiding control strategies.

evolutionary biology↗

Epigenetic disruption of the RARγ complex impairs its function to bookmark AR enhancer interactions required for enzalutamide sensitivity in prostate cancer

The current study in prostate cancer (PCa) focused on the genomic mechanisms at the cross-roads of pro-differentiation signals and the emergence of lineage plasticity. We explored an understudied cistromic mechanism involving RAR{gamma}s ability to govern AR cistrome-transcriptome relationships, including those associated with more aggressive PCa features. The RAR{gamma} complex in PCa cell models was enriched for canonical cofactors, as well as proteins involved in RNA processing and bookmarking. Identifying the repertoire of miR-96 bound and regulated gene targets, including those recognition elements marked by m6A, revealed their significant enrichment in the RAR{gamma} complex. RAR{gamma} significantly enhanced the AR cistrome, particularly in active enhancers and super-enhancers, and overlapped with the binding of bookmarking factors. Furthermore, RAR{gamma} expression led to nucleosome-free chromatin enriched with H3K27ac, and significantly enhanced the AR cistrome in G2/M cells. RAR{gamma} functions also antagonized the transcriptional actions of the lineage master regulator ONECUT2. Similarly, gene programs regulated by either miR-96 or antagonized by RAR{gamma} were enriched in alternative lineages and more aggressive PCa phenotypes. Together these findings reveal an under-investigated role for RAR{gamma}, modulated by miR-96, to bookmark enhancer sites during mitosis. These sites are required by the AR to promote transcriptional competence, and emphasize luminal differentiation, while antagonizing ONECUT2.

cancer biology↗

Cellular dynamics across aged human brains uncover a multicellular cascade leading to Alzheimer's disease

Alzheimers Disease (AD) is a progressive neurodegenerative disease seen with advancing age. Recent studies have revealed diverse AD-associated cell states, yet when and how they impact the causal chain leading to AD remains unknown. To reconstruct the dynamics of the brains cellular environment along the disease cascade and to distinguish between AD and aging effects, we built a comprehensive cell atlas of the aged prefrontal cortex from 1.64 million single-nucleus RNA-seq profiles. We associated glial, vascular and neuronal subpopulations with AD-related traits for 424 aging individuals, and aligned them along the disease cascade using causal modeling. We identified two distinct lipid-associated microglial subpopulations, one contributed to amyloid-{beta} proteinopathy while the other mediated the effect of amyloid-{beta} in accelerating tau proteinopathy, as well as an astrocyte subpopulation that mediated the effect of tau on cognitive decline. To model the coordinated dynamics of the entire cellular environment we devised the BEYOND methodology which uncovered two distinct trajectories of brain aging that are defined by distinct sequences of changes in cellular communities. Older individuals are engaged in one of two possible trajectories, each associated with progressive changes in specific cellular communities that end with: (1) AD dementia or (2) alternative brain aging. Thus, we provide a cellular foundation for a new perspective of AD pathophysiology that could inform the development of new therapeutic interventions targeting cellular communities, while designing a different clinical management for those individuals on the path to AD or to alternative brain aging.

neuroscience↗

Multi-region brain transcriptomes uncover two subtypes of aging individuals with differences in Alzheimer risk and the impact of APOEe4

The heterogeneity of the older population suggests the existence of subsets of individuals which share certain brain molecular features and respond differently to risk factors for Alzheimers disease, but this population structure remains poorly defined. Here, we performed an unsupervised clustering of individuals with multi-region brain transcriptomes to assess whether a broader approach, simultaneously considering data from multiple regions involved in cognition would uncover such subsets. We implemented a canonical correlation-based analysis in a Discovery cohort of 459 participants from two longitudinal studies of cognitive aging that have RNA sequence profiles in three brain regions. 690 additional participants that have data in only one or two of these regions were used in the Replication effort. These clustering analyses identified two meta-clusters, MC-1 and MC-2. The two sets of participants differ primarily in their trajectories of cognitive decline, with MC-2 having a delay of 3 years to the median age of incident dementia. This is due, in part, to a greater impact of tau pathology on neuronal chromatin architecture and to broader brain changes including greater loss of white matter integrity in MC-1. Further evidence of biological differences includes a significantly larger impact of APOE{varepsilon}4 risk on cognitive decline in MC-1. These findings suggest that our proposed population structure captures an aspect of the more distributed molecular state of the aging brain that either enhances the effect of risk factors in MC-1 or of protective effects in MC-2. These observations may inform the design of therapeutic development efforts and of trials as both become increasingly more targeted molecularly. One Sentence Summary: There are two types of aging brains, with one being more vulnerable to APOE{varepsilon}4 and subsequent neuronal dysfunction and cognitive loss.

neuroscience↗

The effect of background noise and its removal on the analysis of single-cell expression data

BACKGROUNDIn droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events. Here, we characterize this background noise exemplified by three single-cell RNA-seq (scRNA-seq) and two single-nucleus RNA-seq (snRNA-seq) replicates of mouse kidney cells. For each experiment, kidney cells from two mouse subspecies were pooled, allowing to identify cross-genotype contaminating molecules and estimate the levels of background noise. RESULTSWe find that background noise is highly variable across replicates and individual cells, making up on average 3-35% of the total counts (UMIs) per cell and show that this has a considerable impact on the specificity and detectability of marker genes. In search of the source of background noise, we find that expression profiles of cell-free droplets are very similar to expression profiles of cross-genotype contamination and hence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure. CONCLUSIONOur findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it.

bioinformatics↗

Cell-subtype specific effects of genetic variation in the aging and Alzheimer cortex

The relationship between genetic variation and gene expression in individual brain cell types and subtypes has remained elusive. Here, we generated single-nucleus RNA sequencing data from the dorsolateral prefrontal cortex of 424 individuals of advanced age; analyzing 1.5 million nuclear transcriptomes, we assessed the effect of genetic variants on RNA expression in cis (cis-eQTL) for 7 cell types and 81 cell subtypes. This effort identified 10,004 eGenes at the cell type level and 8,138 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer pathology, accounting for the effect of APOE{varepsilon}4, providing mechanistic insights into both pathologies. While eQTLs are readily detected, only a TMEM106B variant robustly affects the proportion of cell subtypes. Integration of these results with GWAS highlighted the targeted cell type and likely causal gene within susceptibility loci for Alzheimers, Parkinsons, schizophrenia, and educational attainment.

genomics↗

SnFFPE-Seq: towards scalable single nucleus RNA-Seq of formalin-fixed paraffin-embedded (FFPE) tissue

Profiling cellular heterogeneity in formalin-fixed paraffin-embedded (FFPE) tissues is key to characterizing clinical specimens for biomarkers, therapeutic targets, and drug responses. Here, we optimize methods for isolating intact nuclei and single nucleus RNA-Seq from FFPE tissues in the mouse brain, and demonstrate a pilot application to a human clinical specimen of lung adenocarcinoma. Our method opens the way to broad applications of snRNA-Seq to archival tissues, including clinical samples.

genomics↗

Cell type- and state- resolved immune transcriptomic profiling identifies glucocorticoid-responsive molecular defects in multiple sclerosis T cells

The polygenic and multi-cellular nature of multiple sclerosis (MS) immunopathology necessitates cell-type-specific molecular studies in order to improve our understanding of the diverse mechanisms underlying immune cell dysfunction in MS. Here, by generating a dataset of 1,075 transcriptomes from 209 participants (167 MS and 42 healthy), we assessed MS-associated transcriptional changes in six implicated cell-type-states: naive and memory helper T cells and classical monocytes purified from peripheral blood, each in their primary (ex vivo, unstimulated) and in vitro stimulated states. Our data suggest that primary profiles show larger MS-associated differences than the post-stimulation contexts. We further identified shared and distinct changes in individual genes, biological pathways, and co-expressed gene modules in MS T cells and monocytes, and prioritized genes such as ZBTB16 as MS-associated regulators in both cell types. Of six identified MS-associated co-expressed gene modules, three (two lymphoid and one myeloid) were replicated in independent data from peripheral blood mononuclear cells (PBMC) and monocyte-derived macrophages. A subsequent in silico drug screen prioritized small-molecule compounds for reversing the perturbation of the MS-associated modules. The effects of glucocorticoid receptor agonists as the top-identified therapeutic class for the replicated T cell modules were validated using targeted in silico analyses and in vitro experiments, suggesting the coordinated dysregulation of glucocorticoid-responsive genes in MS T cells. In summary, our study identifies and validates individual genes and co-expressed gene modules from T and myeloid cells that are perturbed in MS, offering new targets for therapeutic discovery and biomarker development to guide the management of MS.

genomics↗

Development of coarse-grained model for a minimal stratum corneum lipid mixture

Molecular dynamics simulations of mixtures of the ceramide N-(tetracosanoyl)-sphingosine (NS), cholesterol, and a free fatty acid are performed to gain a molecular-level understanding of the structure of the lipids found in the stratum corneum layer of skin. A new coarse-grained model for cholesterol, developed using the multistate iterative Boltzmann inversion method, is compatible with previously developed coarse-grained forcefields for ceramide NS, free fatty acid, and water, and validated against atomistic simulations of these lipids using the CHARMM force field. Self-assembly simulations of multilayer structures using these coarse-grained force fields are performed, revealing that a large fraction of the ceramides adopt extended conformations, which cannot occur in the bilayer structures typically studied using simulation. Cholesterol fluidizes the membrane by promoting packing defects and it is observed that an increase in cholesterol content reduces the bilayer height, due to an increase in interdigitation of the C24 lipid tails, consistent with experimental observations. Through the use of a simple reverse-mapping procedure, a self-assembled coarse-grained multilayer system is used to construct an equivalent structure with atomistic resolution. Simulations of this atomistic structure are found to closely agree with experimentally derived neutron scattering length density profiles. Significant interlayer hydrogen bonding is observed in the inner layers of the atomistic multilayer structure that are not found in the outer layers in contact with water or in equivalent bilayer structures. These results identify several significant differences in the structure and hydrogen bonding of multilayer structures as compared to the more commonly studied bilayer systems, and, as such, highlight the importance of simulating multilayer structures for more accurate comparisons with experiment. These results also provide validation of the efficacy of the coarse-grained forcefields and the framework for multiscale simulation.

biochemistry↗

Multi-cellular communities are perturbed in the aging human brain and with Alzheimer's disease

The role of different cell types and their interactions in Alzheimers disease (AD) is an open question. Here we pursued it by assembling a high-resolution cellular map of the aging frontal cortex by single nucleus RNA-seq of 24 individuals with different clinicopathologic characteristics. We used the map to infer the neocortical cellular architecture of 638 individuals profiled by bulk RNA-seq, providing the sample size necessary for identifying statistically robust associations. We uncovered diverse cell populations associated with AD, including inhibitory neuronal subtypes and oligodendroglial states. We further recovered a network of multicellular communities, each composed of coordinated subpopulations of neuronal, glial and endothelial cells, and found that two of these communities are altered in AD. Finally, we used mediation analyses to prioritize cellular changes that might contribute to cognitive decline. Thus, our deconstruction of the aging neocortex provides a roadmap for evaluating the cellular microenvironments underlying AD and dementia.

neuroscience↗

Effects of Serotonin and Dopamine Depletion on Neural Prediction Computations during Social Learning

We recently found that individuals with high depression scores demonstrate impaired learning from social outcomes. Given that depression has been linked to altered serotonin (5-HT) and dopamine (DA) functioning, the current study aimed to elucidate the role of these neurotransmitters in social learning with the use of dietary precursor depletion. In a double-blind design, 70 healthy volunteers were randomly allocated to the 5-HT depletion (N=24), DA depletion (N = 24), or placebo (N = 22) group. Participants performed a social learning task during fMRI scanning, as part of which they learned associations between name cues and rewarding (happy faces) or aversive (fearful faces) social outcomes. Behaviourally, 5-HT depleted subjects demonstrated impaired social reward learning compared to placebo controls, with a marginal effect in the same direction in the DA depletion group. On the neural level, computational modelling-based fMRI analyses revealed that 5-HT depletion altered social reward prediction signals in the insula, temporal lobe, and prefrontal cortex. DA depletion affected social reward prediction encoding only in the prefrontal cortex. These results indicate that 5-HT depletion impairs learning from social rewards, on both the behavioural and the neural level, while DA depletion has a less extensive effect. Interestingly, the behavioural and neural responses observed after 5-HT depletion in the current study closely resemble our previous findings in individuals with high depression scores. It may thus be the case that decreased 5-HT levels contribute to social learning deficits in depression.

neuroscience↗

Impaired Social Learning Predicts Reduced Real-life Motivation in Individuals with Depression: A Computational fMRI Study

BackgroundMajor depressive disorder is associated with altered social functioning and impaired learning, on both the behavioural and the neural level. These deficits are likely related, considering that successful social interactions require learning to predict other peoples emotional responses. Yet, there is little research examining this relation.\n\nMethodsForty-three individuals with high (HD; N=21) and low (LD; N=22) depression scores answered questions regarding their real-life social experiences and performed a social learning task during fMRI scanning. As part of the task, subjects learned associations between name cues and rewarding (happy faces) or aversive (fearful faces) social outcomes. Using computational modelling, behavioural and neural correlates of social learning were examined and related to real-life social experiences.\n\nResultsHD participants reported reduced motivation to engage in real-life social activities and demonstrated elevated uncertainty about social outcomes in the task. Moreover, HD subjects displayed altered encoding of social reward predictions in the insula, temporal lobe and parietal lobe. Interestingly, across all subjects, higher task uncertainty and reduced parietal prediction encoding were associated with decreased motivation to engage in real-life social activities.\n\nLimitationsThe size of the included sample was relatively small. The results should thus be regarded as preliminary and replications in larger samples are called for.\n\nConclusionTaken together, our findings suggest that reduced learning from social outcomes may impair depressed individuals ability to predict other peoples responses in real life, which renders social situations uncertain. This uncertainty, in turn, may contribute to reduced social engagement (motivation) in depression.

neuroscience↗