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Dayan, E.

Publications and source records attributed to Dayan, E..

10 recordsLinked to original sources

Identity Domains Reveal How Life Experiences and the Microbiome Shape Individuality and Uncover the Presence of Social Memory in Drosophila

Personality is comprised of enduring traits that shape behavior across contexts and over time. Yet outside humans, research often equates individuality with personality, focusing on how one animal differs from another in each behavior. This overlooks the latent space that spans an animals behavioral repertoire, how it is shaped by innate tendencies and prior experiences, and how it differs from transient factors. Here, we bridge this gap by integrating a data-driven framework with unbiased trait interpretation via large language models, revealing how social, sexual, and physiological conditions systematically alter the expression of personality-like traits in Drosophila melanogaster. This approach yielded four identities: space-use (exploration), time-use (social investment), avoidance, and aggression. Large language models produced strong consensus on the interpretation of some of these axes and divergence on others. These dimensions proved stable within individuals yet shifted predictably following mating, isolation, and microbiome manipulation, often in sex dependent ways. Notably, flies behaved differently toward familiar and unfamiliar peers, revealing a capacity for social memory. These findings bridge temperament and personality, showing how life history reshapes baseline tendencies into individualized profiles within constrained dimensions. They establish Drosophila as a tractable system for uncovering the biological logic of personality and advancing cross-species principles for individuality research. Significance StatementPersonality is often treated as a hallmark of complex brains, yet we show that fruit flies express stable behavioral identities with systematic, experience driven plasticity. Using a mathematical framework, we resolve four stable traits and map how sex, social experience, and the gut microbiome shift their expression. We further demonstrate social recognition in flies: they treat familiar cage mates differently from newcomers over repeated encounters, evidencing social memory, a striking result given their simple nervous system. Finally, we introduce large language models to provide objective, reproducible interpretations of trait dimensions, reducing human bias. These advances establish a general, quantitative approach to personality and social behavior in a tractable genetic model.

neuroscience↗

Salience Network Segregation and Symptom Profiles in Psychosis Prodrome Subgroups

BackgroundUnderstanding neurobiological similarities between individuals with prodromal psychosis symptoms can improve early identification and intervention strategies. Here, we aimed to (i) identify neurobiologically similar groups by integrating resting-state functional connectivity and prodromal symptom data and (ii) discern discriminating symptom profiles and brain connectivity patterns in the identified sub-groups. MethodsOur sample (N=922) was extracted from the Philadelphia Neurodevelopmental Cohort and included individuals ages 12-21 years with fMRI and self-reported psychopathology data. Analyses were conducted separately for youth and early adults. We first constructed a two-layer network using pair-wise similarity distances between participants based on resting-state functional connectivity and prodromal positive psychosis symptoms. We then performed community detection via a multiplex stochastic block model to identify subject clusters. ResultsWe identified 2 blocks or communities for both the youth (n=458 and 179) and early adult (n=173 and 112) groups. Connection parameter estimates of the neuroimaging layer were nearly identical between blocks for both age groups whereas there was significant variation for the symptom layer. Psychopathology symptom and brain system segregation profiles were consistent across age groups. The youth block (n=458) with higher salience network segregation values had higher mean scores for prodromal symptoms. However, the early adult block (n=173) with lower salience network segregation had higher mean prodromal scores. ConclusionsBy integrating global similarities in brain connectivity and prodromal symptoms, we identified distinct subgroups. These groups show differences in symptom profiles and network segregation in youth and early adults, indicating significant variations in developmental paths for psychosis spectrum.

neuroscience↗

Redundancy protects processing speed in healthy individuals with accelerated brain aging

Recent advancements in computational learning techniques have enabled the estimation of brain age (BA) from neuroimaging data. The difference between chronological age (CA) and BA, known as the BA gap, can potentially serve as a biomarker of brain health. Studies, however, have documented low correlations between BA gap and cognition in healthy aging. This suggests that protective mechanisms in the brain may help counter the effect of accelerated brain aging. Here, we investigated whether redundancy in brain networks may protect cognitive function in individuals with accelerated brain aging. First, we employed deep learning to estimate individual brain ages from structural magnetic resonance imaging (MRI). Next, we associated CA, BA, and BA gap, with cognitive measures and network topology derived from diffusion MRI and tractography. We found that CA and BA were both similarly related to cognitive measures and network topology, while BA gap did not show strong relationships in either domain. Despite observing no strong relationships between brain-age gap (BA gap) and demographic variables, cognitive measures, or topological features in healthy aging, individuals with accelerated aging (BA gap+) exhibited lower average degree and redundancy within the dorsal attention network compared to those with delayed aging (BA gap-). Furthermore, redundancy in the dorsal attention network was positively associated with processing speed in BA gap+ individuals. These results indicate a potential neuroprotective role of redundancy in structural brain networks for mitigating the impact of accelerated brain atrophy on cognitive performance in healthy aging.

neuroscience↗

Age-related changes in network controllability are mitigated by redundancy in large-scale brain networks

The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we used diffusion MRI data from cognitively intact participants (n=480, ages 40-90) to study age-associated changes in the controllability of structural brain networks, features that could mitigate these changes, and the overall effect on cognitive function. We found age-associated declines in controllability in control hubs and large-scale networks, particularly within the and frontoparietal control and default mode networks. Redundancy, quantified via the assessment of multi-step paths within networks, mitigated the effects of changes in topology on network controllability. Lastly, network controllability, redundancy, and grey matter volume each played important complementary roles in cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.

neuroscience↗

A robust core architecture of functional brain networks supports topological resilience and cognitive performance in aging

Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against aging-related cognitive decline. The topological characteristics of brain networks that support protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness of brain networks, queried via the delineation of the brains core network structure, supports superior cognitive performance in healthy aging individuals (n=320, ages 60-90). First, we decomposed each subjects functional brain networks using k-shell decomposition, finding that cognitive function is associated with more robust connectivity of core nodes, primarily within the frontoparietal control network (FPCN). Next, we find that the resilience of core brain network nodes, within the FPCN in particular, relates to cognition. Finally, we show that the degree of segregation in functional networks mediates relationships between network resilience and cognition. Together, these findings suggest that brain networks balance between robust core connectivity and segregation to facilitate high cognitive performance in aging.

neuroscience↗

The contribution of hippocampal subfields to the progression of neurodegeneration

Mild cognitive impairment (MCI) is often considered the precursor of Alzheimers disease. However, MCI is associated with substantially variable progression rates, which are not well understood. Attempts to identify the mechanisms that underlie MCI progression have often focused on the hippocampus, but have mostly overlooked its intricate structure and subdivisions. Here, we utilized deep learning to delineate the contribution of hippocampal subfields to MCI progression using a total sample of 1157 subjects (349 in the training set, 427 in a validation set and 381 in the testing set). We propose a dense convolutional neural network architecture that differentiates stable and progressive MCI based on hippocampal morphometry. The proposed deep learning model predicted MCI progression with an accuracy of 75.85%. A novel implementation of occlusion analysis revealed marked differences in the contribution of hippocampal subfields to the performance of the model, with presubiculum, CA1, subiculum, and molecular layer showing the most central role. Moreover, the analysis reveals that 10.5% of the volume of the hippocampus was redundant in the differentiation between stable and progressive MCI. Our predictive model uncovers pronounced differences in the contribution of hippocampal subfields to the progression of MCI. The results may reflect the sparing of hippocampal structure in individuals with a slower progression of neurodegeneration.

neuroscience↗

Altered hypothalamic diffusivity in Parkinsonian autonomic dysfunction

The pathophysiological basis of autonomic symptoms in Parkinsons disease remains incompletely understood. The hypothalamus plays a key regulatory role in autonomic function and has been shown to be affected in Parkinsons disease. Here, using diffusion magnetic resonance imaging, we investigated whether microstructural properties of the hypothalamus differ in Parkinsons disease patients with high compared to low autonomic symptom burden.\n\nParkinsons disease patients with low (n=25) and high (n=25) autonomic symptom burden were identified from a larger pool, based on scores from a questionnaire assessing autonomic symptoms in Parkinsons disease (SCOPA-AUT). In each patient, we first segmented the hypothalamus manually, based on anatomical landmarks. Diffusivity measures were then extracted from the hypothalamus. Diffusivity measures calculated in the brainstem and the putamen were used to assess the specificity of the results.\n\nRelative to patients with low autonomic symptom burden, patients with high burden showed increased mean, axial, and radial diffusivity in the hypothalamus. In contrast, we did not find significant group differences in any of these measures extracted from the brainstem or the putamen.\n\nThese results reveal consistent differences in the microstructural properties of the hypothalamus between patients with low and high autonomic symptom burden. Hypothalamic diffusivity properties can thus potentially be used as an imaging marker to assist in the identification of therapeutic targets for autonomic dysfunction in Parkinsons disease.

neuroscience↗

Integrative Functional Network Interactions Underlie the Association between Physical Activity and Cognition in Neurodegenerative Diseases

Physical activity (PA) has preventive and possibly restorative effects in aging-related cognitive decline, which relate to intrinsic functional interactions (functional connectivity, FC) in large-scale brain networks. Preventive and ameliorative effects of PA on cognitive decline have also been documented in neurodegenerative diseases, such as Parkinson's disease (PD). However, the neural substrates that mediate the association between PA and cognitive performance under such neurological conditions remain unknown. Here we set out to examine if the association between PA and cognitive performance in PD is mediated by FC in large-scale sensorimotor and association brain networks. Data from 51 PD patients were analyzed. Connectome-level analysis based on a whole-brain parcellation showed that self-reported levels of PA were associated with increased FC between, but not within the default mode (DMN) and salience networks (SAL) (p < .05, false discovery rate corrected). Additionally, multiple parallel mediation analysis further demonstrated that FC between left lateral parietal nodes in the DMN and rostral prefrontal nodes in the SAL mediated the association between PA and executive function performance. These findings are in line with previous studies linking FC in large-scale association networks with the effects of PA on cognition in healthy aging. Our results extend these previous results by demonstrating that the association between PA and cognitive performance in neurodegenerative diseases such as PD is mediated by integrative functional interactions in large-scale association networks.

neuroscience↗

Distributed cortical structural properties contribute to motor cortical excitability and inhibition

The link between the local structure of the primary motor cortex and motor function has been well documented. However, motor function relies on a network of interconnected brain regions and the link between the structural properties characterizing these distributed brain networks and motor function remains poorly understood. Here, we examined whether distributed patterns of brain structure, extending beyond the primary motor cortex can help classify two forms of motor function: corticospinal excitability and intracortical inhibition. To this effect, we recorded high-resolution structural magnetic resonance imaging scans in 25 healthy volunteers. To measure corticospinal excitability and inhibition in the same volunteers we recorded motor evoked potentials (MEPs) elicited by single-pulse transcranial magnetic stimulation (TMS) and short-interval intracortical inhibition (SICI) in a separate session. Support vector machine (SVM) pattern classification was used to identify distributed multivoxel gray matter areas, which distinguished subjects who had lower and higher MEPs and SICIs. We found that MEP and SICI classification could be predicted based on a widely distributed, largely non-overlapping pattern of voxels in the frontal, parietal, temporal, occipital and cerebellar regions. Thus, structural properties distributed over the brain beyond the primary motor cortex relate to motor function.

neuroscience↗

Alterations in striato-pallidal intrinsic functional connectivity as a prodrome of Parkinson’s disease

Although the diagnosis of Parkinsons disease (PD) remains anchored around the cardinal motor symptoms of bradykinesia, rest tremor, rigidity and postural instability, it is becoming increasingly clear that the clinical phase of the disease is preceded by a long period of neurodegeneration, which is not readily evident in terms of motor dysfunction. The neurobiological mechanisms that underpin this prodromal phase of PD remain poorly understood. Based on converging evidence of basal ganglia (BG) dysfunction in early PD, we set out to establish whether the prodromal phase of the disease is characterized by alterations in functional communication within the input and output structures of the BG. We analyzed resting-state functional MRI data collected from patients with REM sleep behavior disorder (RBD) and/or hyposmia, two of the strongest markers of prodromal PD, in comparison to age-matched controls. Relative to controls, subjects in the prodromal group showed reduced intra- and interhemispheric functional connectivity in a striato-thalamo-pallidal network. Functional connectivity alterations were restricted to the BG and did not extend to functional connections with the cortex. The data suggest that local interactions between input and output BG structures may be disrupted already in the prodromal phase of PD.

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