bioRxiv Subject Collection: Neuroscience's Journal

Mood impacts confidence through biased learning of reward likelihood
Background: Intuitively, emotional states guide not only the actions we take, but also our confidence in those actions. This sets the stage for subjective confidence about the best action to take to diverge from the actual likelihood and, clinically, may give rise to over-confidence and risky behaviours during episodes of elevated mood and the reverse during depressive episodes. Whilst computational models have been proposed to explain how emotional states recursively bias perception of action outcomes, these models have not been extended to capture the impacts of mood on confidence. Here we propose a computational model that formalises confidence and its relationship with learning from outcomes and emotional states. Methods: We collected data both in a laboratory context (n=35) and in pre-registered online replication (n=106; https://osf.io/ygc4t). Participants completed a two-armed bandit task, with learning blocks before and after a mood manipulation in which participants unexpectedly received (positive mood induction) or lost (negative mood induction) a relatively large sum of money. Participants periodically reported their decision confidence throughout the task. We examined the extent to which the mood manipulation biased their confidence, predicting that positive and negative moods would lead to over- and under- confidence, respectively. We further predicted that this effect would be stronger in participants with greater propensity towards strong and changeable moods, measured by the Hypomanic Personality Scale. Moreover, we formalized a computational model in which confidence emerges as the difference between the perceived likelihood of reward for the available options. In this model, mood indirectly biases confidence through recursively biased learning of the reward likelihoods for the available options and not from simply shifting overall confidence up or down. Results: In both experiments, we confirmed that moods impacted confidence in the hypothesised direction; absent of any differences in participants' objective performance, average confidence was higher following positive mood induction, and lower following negative mood induction. This effect was larger in participants with higher levels of trait hypomania. Intriguingly, we found that the effect of mood on confidence emerged in concert with learning. Indeed, whilst the shift in mood was greatest immediately post-mood manipulation and returned to baseline by the end of the learning block, the effect of mood on confidence gradually accumulated over learning trials, peaking at the end of the block. These dynamics were captured by simulations of a 'Moody Likelihood' model. Empirically, this model simultaneously accounted for the effects of mood on choices, mood states and confidence through a mood bias parameter. Conclusion: We present a unified model in which moods recursively bias reward learning and, consequently, confidence in decision making. Moods fundamentally bias the accumulation of reward likelihood, rather than directly biasing decision confidence. Clinically, these findings have implications for understanding two core symptoms of mood disorder, suggesting that both perturbed mood and confidence about goal-directed behaviour arise from a common bias during reward learning.

Dual Computational Systems in the Development and Evolution of Mammalian Brains
Analysis of brain volumes across mammalian taxonomic groups reveal a pattern of complementary and inverse covariation between major brain components, including a robust negative covariation between the limbic system and neocortex. To understand the computational basis of this covariation, we investigated the multidimensional representational space of task-optimized machine learning systems. We found that a smooth mapping of this space onto a two-dimensional surface leads to a characteristic layout depending on the structure of its information source. Visual, somatosensory and auditory systems develop ordered spatiotopic maps where units draw information from localized regions of the sensory input. Olfactory and relational memory systems, in contrast, develop fractured maps with distributed patterns of information convergence. Evolutionary optimization of multimodal systems result in inverse covariation between spatiotopic and disordered system components that compete for representational space. These results suggest that the observed pattern of covariation of brain components reflect an essential computational duality in brain evolution.

Humans and marmosets share similar face recognition signatures in shape-based visual face discrimination behavior
Our ability to identify faces is thought to depend on high-level visual processing in the brain. Nonetheless, studies of face recognition have generally relied on 2D face photographs where low-level strategies relying on texture and appearance cues can be employed to adequately support high face identification performance. Here, we designed a fine face discrimination task under 3D pose and lighting variation that was purely based on shape, a task which challenged state-of-the-art artificial vision systems compared to object recognition tasks. In contrast, humans performed this shape based face task at comparable levels to their object recognition performance. We then tested one of the smallest simian primates on this human-level, machine-difficult visual task, the common marmoset -- a small, New World monkey. Marmosets successfully discriminated between face identities across 3D viewing conditions based purely on face shape. Their face recognition performance was on par with their object recognition performance and exhibited face-specific behavioral signatures similar to humans, including lower performance for inverted faces, faces lit from below, and contrast reversed faces. These results demonstrate that a high-level visual behavior, invariant face recognition based purely on geometry and not additional texture and appearance cues, is shared across simian primates from among the smallest to the most advanced, consistent with the presence of common underlying high-level visual brain areas across simian primates.

Uncertainty mapping and probabilistic tractography using Simulation-Based Inference in diffusion MRI: A comparison with classical Bayes
Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI framework for parametric spherical deconvolution of diffusion MRI data of the brain. We demonstrate its utility for estimating white matter fibre orientations, mapping uncertainty of voxel-based estimates and performing probabilistic tractography by spatially propagating fibre orientation uncertainty. We conduct an extensive comparison against established Bayesian methods based on Markov-Chain Monte-Carlo (MCMC) and find that: a) in-silico training can lead to calibrated SBI networks with accurate parameter estimates and uncertainty mapping for both single and multi-shell diffusion MRI, b) SBI allows amortised inference of the posterior distribution of model parameters given unseen observations, which is orders of magnitude faster than MCMC, c) SBI-based tractography yields reconstructions that have a high level of agreement with their MCMC-based counterparts, equal to or higher than scan-rescan reproducibility of estimates. We further demonstrate how SBI design considerations (such as dealing with noise, defining priors and handling model selection) can affect performance, allowing us to identify optimal practices. Taken together, our results show that SBI provides a powerful alternative to classical Bayesian inference approaches for fast and accurate model estimation and uncertainty mapping in MRI.

A key role of PIEZO2 mechanosensitive ion channel in adipose sensory innervation
Compared to the well-established functions of sympathetic innervation, the role of sensory afferents in adipose tissues remains less understood. Recent work revealed the anatomical and physiological significance of adipose sensory innervation; however, its molecular underpinning remains unclear. Here, using organ-targeted single-cell RNA sequencing, we identified the mechanoreceptor PIEZO2 as one of the most prevalent receptors in fat-innervating dorsal root ganglia (DRG) neurons. We found that selective PIEZO2 deletion in fat-innervating neurons phenocopied the molecular alternations in adipose tissue caused by DRG ablation. Conversely, a gain-of-function PIEZO2 mutant shifted the adipose phenotypes in the opposite direction. These results indicate that PIEZO2 plays a major role in the sensory regulation of adipose tissues. This discovery opens new avenues for exploring mechanosensation in organs not traditionally considered mechanically active, such as the adipose tissues, and therefore sheds light on the broader significance of mechanosensation in regulating organ function and homeostasis.

Towards Multimodal Longitudinal Analysis for Predicting Cognitive Decline Using Neuroimaging Biomarkers, Cognitive Assessments, and Demographic Data
Understanding and predicting cognitive decline in Alzheimer's disease (AD) is crucial for timely intervention and management. While neuroimaging biomarkers and clinical assessments are valuable individually, their combined predictive power and interaction with demographic and cognitive variables remain underexplored. This study lays the groundwork for comprehensive longitudinal analyses by integrating neuroimaging markers and clinical data to predict cognitive changes over time. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), feature-driven supervised machine learning techniques were applied to assess cognitive decline predictability. Results showed that while imaging biomarkers alone offered moderate predictive capabilities, including cognitive and demographic factors significantly improved model performance, with the Random-Forest model achieving near-perfect accuracy. The analysis highlighted the leading role of neuroimaging biomarkers as predictors, along with the importance of incorporating cognitive scores and demographic data to enhance predictability. Explainability analyses further revealed that clinical and demographic data could estimate brain imaging metrics, emphasizing the bidirectional nature of these relationships. This study underscores the need for integrating multi-dimensional data in future longitudinal research to capture time-dependent patterns in cognitive decline and guide the development of targeted intervention strategies. We also introduce and provide NeuroLAMA, an open and extensible data engineering and machine-learning system, to support the continued investigation by the community

Temporal expectations modulate coupling between frontal and sensory brain areas
Temporal prediction is a crucial mechanism that allows the brain to optimize sensory processing by anticipating event timing. This predictive ability enhances processing efficiency by modulating brain activity through cortical oscillations that periodically influence neuronal excitability . For example, stimuli with predictable timing are processed more accurately. Although it is well-established that predictions are generated across large-scale brain networks, the roles of different cortical regions, particularly the frontal cortices and sensory areas, remain debated. In this study, we investigated the role of frontal cortices in temporal prediction during auditory perception. Cue tones predicted the timing of auditory targets with different levels of certainty (50%, 80%). We recorded electroencephalographic data (EEG) while participants detected the targets. Functional connectivity analyses revealed that the level of predictability modulates the relative contributions of frontal and auditory cortices during both the anticipation and detection of targets: as the certainty of predictions grows, the involvement of frontal brain regions increases. Our findings suggest that the auditory temporal prediction network relies on an integrated system of frontal and sensory regions, which are sensitive to the level of predictability.

Post-Critical Period Transcriptional and Physiological Adaptations of Thalamocortical Connections after Sensory Loss
Unilateral whisker denervation activates plasticity mechanisms and circuit adaptations in adults. Single nucleus RNA sequencing and multiplex fluorescence in situ hybridization revealed differentially expressed genes related to altered glutamate receptor distributions and synaptogenesis in thalamocortical (TC) recipient layer 4 (L4) neurons of the sensory cortex, specifically those receiving input from the intact whiskers after whisker denervation. Electrophysiology detected increased spontaneous excitatory events at L4 neurons, confirming an increase in synaptic connections. Elevated expression levels of Gria2 mRNA and functional GluA2 subunit of AMPA receptors at the TC synapse indicate the presence of stabilized and potentiated TC synapses to L4 excitatory neurons along the intact pathway after unilateral whisker denervation. These adaptations likely underlie the increased cortical activity observed in rodents during intact whisker sensation after unilateral whisker denervation. Our findings provide new insights into the mechanisms by which the adult brain supports recovery after unilateral sensory loss.

A novel mouse model for developmental and epileptic encephalopathy by Purkinje cell-specific deletion of Scn1b
Loss of function variants of SCN1B are associated with a range of developmental and epileptic encephalopathies (DEEs), including Dravet syndrome. These DEEs feature a wide range of severe neurological disabilities, including changes to social, motor, mood, sleep, and cognitive function which are notoriously difficult to treat, and high rates of early mortality. While the symptomology of SCN1B-associated DEEs indicates broad changes in neural function, most research has focused on epilepsy-related brain structures and function. Mechanistic studies of SCN1B/Scn1b have delineated diverse roles in development and adult maintenance of neural function, via cell adhesion, ion channel regulation, and other intra- and extra-cellular actions. However, use of mouse models is limited as knockout of Scn1b, globally and even in some cell-specific models (e.g., Parvalbumin+ interneuron-specific knockout) in adult mice, leads to severe and progressive epilepsy, health deterioration, and 100% mortality within weeks. Here, we report findings of a novel transgenic mouse line in which Scn1b was specifically deleted in cerebellar Purkinje cells. Unlike most existing models, these mice did not show failure to thrive or early mortality. However, we quantified marked decrements to Purkinje cell physiology as well as motor, social, and cognitive dysfunction. Our data indicate that cerebellar Purkinje cells are an important node for dysfunction and neural disabilities in SCN1B-related DEEs, and combined with previous work identify this as a potentially vital site for understanding mechanisms of DEEs and developing therapies that can treat these disorders holistically.

Vertebrate vision is ancestrally based on competing cone circuits
Vision first evolved in the water, where light becomes increasingly monochromatic with viewing distance. The presence of spectrally broad ('white') light is therefore the exclusive remit of the visual foreground. However, if and how aquatic visual systems exploit this 'white effect' as an inductive bias, for example to judge distance, remains unknown. By combining two-photon imaging with hyperspectral stimulation, genetic cone-type ablation, and behaviour, we here show that zebrafish suppress neural responses to the visual background by contrasting 'greyscale' and 'colour' circuits that emerge at the first synapse of vision. To do so, zebrafish use an early retinal architecture that fundamentally differs from that of mammals: Rather than combining cone signals to drive the retinal output leading to behaviour, zebrafish vision is built around competing ancestral cone systems: Red/UV versus green/blue. Of these, the non-opponent red and UV cones, which are retained in mammals, are necessary and sufficient for vision. By contrast, the colour opponent green and blue cones, which are lost in mammals, form a net-suppressive 'auxiliary' system that shape the 'core' drive from red and UV cones. Our insights challenge the long-held notions that cones act in concert to drive visual behaviour, and that their spectral diversity primarily serves colour vision. Instead, we posit that vertebrate vision is ancestrally built upon opposing cone systems that emerged to exploit the strong spectral interactions of light with water. This alternative view points at terrestrialisation, not nocturnalisation, as the leading driver for visual circuit reorganisation in mammals.

Knockdown of TTLL1 reduces Aβ-induced TAU pathology in human iPSC-derived cortical neurons
Microtubules play a crucial role in neuronal structure and function, with their stability and dynamics regulated by post-translational modifications (PTMs) such as polyglutamylation. In Alzheimer disease (AD), the microtubule-associated protein TAU becomes mislocalized into the somatodendritic compartment (TAU missorting), dissociates from microtubules, aggregates into neurofibrillary tangles, and contributes to microtubule destabilization and neuronal death. Here, we investigated the role of Tubulin-Tyrosine-Ligase-Like proteins (TTLLs) in TAU missorting and microtubule dysregulation using human induced pluripotent stem cell (hiPSC)-derived cortical neurons treated with oligomeric amyloid-beta (oA{beta}) to replicate AD-like conditions. TTLL1, TTLL4, TTLL6 were selectively knocked down (KD) to assess their impact on TAU missorting and microtubule stability. Fluorescence resonance energy transfer (FRET) microscopy was used to examine interactions between TAU and TTLL proteins. We observed TAU missorting, increased tubulin polyglutamylation, decreased microtubule stability, and synaptic declustering in oA{beta}-treated neurons. TTLL1 KD significantly reduced TAU missorting, tubulin polyglutamylation, and synaptic disintegration, while TTLL4 KD showed moderate effects, and TTLL6 KD restored microtubule acetylation. Importantly, TTLL KD did not impair neuritic networks, dendritic complexity, or neuronal activity. FRET microscopy revealed a potential interaction between TAU and TTLL1, but not other TTLLs, suggesting a direct role of TTLL1 in TAU-mediated toxicity. Our findings indicate that targeting TTLL1, either alone or in combination with other TTLLs, may be a promising therapeutic strategy to counteract microtubule and synaptic dysfunction in AD and related neurodegenerative disorders.

Compulsion derived from incentive cocaine-seeking habits is associated with a downregulation of the dopamine transporter in striatal astrocytes
The development of compulsive cue-controlled -incentive- drug-seeking habits, a hallmark of substance use disorder, is predicated on an intrastriatal shift in the locus of control over behaviour from a nucleus accumbens (Nac) core - dorsomedial striatum network to a Nac core - anterior dorsolateral striatum (aDLS) network. Such shift parallels striatal adaptations to chronic drug, including cocaine self-administration, marked by dopamine transporter (DAT) alterations originating in the ventral striatum that spread eventually to encompass the aDLS. Having recently shown that heroin self-administration results in a pan-striatal reduction in astrocytic DAT that precedes the development of aDLS dopamine-dependent incentive heroin-seeking habits we tested the hypothesis that similar adaptations occurr following cocaine exposure. We compared DAT protein levels in whole tissue homogenates, and astrocytes cultured from ventral and dorsal striatal territories of drug naive male Sprague Dawley rats to those of rats with a history of cocaine-taking or an aDLS dopamine-dependent incentive cocaine-seeking habit. Cocaine exposure resulted in a decrease in whole tissue and astrocytic DAT across all territories of the striatum. We further demonstrated that compulsive, i.e., punishment-resistant, incentive cocaine-seeking habits were associated with a reduction in DAT mRNA levels in the Nac shell, but not the Nac core-aDLS incentive habit system. Together with the recent evidence of heroin-induced downregulation of striatal astrocytic DAT, these findings suggest that alterations in astrocytic DAT may represent a common mechanism underlying the development of compulsive incentive drug-seeking habits across drug classes.

The "Ocular Response Function" for encoding and decoding oculomotor related neural activity
Oculomotor activity provides critical insights into cognition and health, with growing evidence demonstrating its involvement in various cognitive functions such as attention, memory, and sensory processing. Furthermore, eye movements are emerging as significant indicators of psychopathologies and neurological disorders, including schizophrenia, dementia, depression, and tinnitus. Despite its crucial importance across domains, the role of oculomotion has often been underexplored in neuroimaging studies - largely due to methodological challenges. Eye movements have traditionally been viewed as artefacts in the neural signal, leading to the exclusion of epochs containing them, or correction methods to remove their influence. However, this strategy does not allow us to determine their role in a range of neural effects or mapping between tasks and neural responses. To enable such nuanced investigations in typical function and disease, we introduce what we term "Ocular Response Functions". We used simultaneous magnetoencephalographic and eye-tracking recordings during the resting-state combined with temporal response functions to precisely map the relationship between oculomotion and neural activity. Our approach allows for the temporally and spatially precise prediction of neural activity based on ocular action, and vice versa. We further validate this method in a passive listening task, highlighting its potential for uncovering cognitive insights in experimental settings. By providing a robust framework for examining the interplay between eye movements and neural processes, our method opens new avenues for both research and clinical applications, potentially advancing early detection and intervention strategies for neurological and psychiatric disorders.

Iso-orientation bias of layer 2/3 connections: the unifying mechanism of spontaneous, visually and optogenetically driven V1 dynamics
Functionally specific long-range lateral connectivity in layer 2/3 of the primary visual cortex (V1) supports the integration of visual information across visual space and shapes spontaneous, visual and optogenetically driven V1 activity. However, a comprehensive understanding of how these diverse cortical regimes emerge from this underlying cortical circuitry remains elusive. Here we address this gap by showing how the same model assuming moderately iso-orientation biassed long-range cortical connectivity architecture explains diverse phenomena, including (i) range of visually driven phenomena, (ii) modular spontaneous activity, (iii) the propagation of spontaneous cortical waves, and (iv) neural responses to patterned optogenetic stimulation. The model offers testable predictions, including presence of slower and iso-tropic spontaneous wave propagation in layer 4 and non-monotonicity of optogenetically driven cortical response to increasingly larger disk of illumination. We thus offer a holistic framework for studying how cortical circuitry governs information integration across multiple operating regimes.

Scents Modulate Anxiety Levels, but Electroencephalographic and Electrocardiographic Assessments Could Diverge from Subjective Reports
Scents could modulate anxiety levels, such as anxiety in a medical office. Here we investigated the impact of two scents on the subjective and physiological anxiety markers in the dental office environment, utilizing self-reported anxiety assessments alongside physiological assessment with electroencephalographic (EEG) and electrocardiographic (ECG) measurements. Lavender was the first tested scent with the previously reported calming effect. African stone was the second stimulus with a musky scent. Twenty healthy participants took part in scent exposure sessions. Anxiety levels were assessed using the State-Trait Anxiety Inventory (STAI), EEG-based theta, alpha, and beta power ratios, and heart rate variability (HRV) indices derived from ECG data. Lavender exposure significantly decreased self-reported anxiety whereas African stone reduced physiological indicators of anxiety. Namely, African stone exposure led to decreased theta and increased alpha power in the parietal-occipital EEG signals. Additionally, decreases were observed in low-frequency (LF) HRV power and total HRV power, reflecting lowered autonomic arousal. These findings support the potential effectiveness for olfactory interventions to aid in anxiety management within clinical environments, but draw attention to the issue of proper evaluation of anxiety. In particular, the difference between the subjective reports and traditional EEG and HRV markers indicates that anxiety involves a complexity of factors, which makes its treatment by scents challenging.

Global and selective effects of auditory attention on arousal: insights from pupil dilation
This study investigated the interplay between attention and arousal in humans by measuring pupil dilation as a function of task engagement and stimulus relevance. Arousal in response to task-relevant and unexpected irrelevant sounds was measured in healthy young adults during the performance of an auditory detection task, the Competitive Attention Test, and in sensory matched passive conditions. Attention was manipulated using informative and uninformative visual cues. Both relevant and irrelevant sounds elicited a larger increase in pupil dilation in the active compared to the passive condition, revealing a global effect of task engagement on arousal. Additionally, in the active condition, the pupil dilation amplitude during the anticipation and detection of the task-relevant sound was greater following an informative compared to an uninformative cue, while no cue effect was found on the pupil dilation response to distracting sounds. This finding suggests that arousal can be selectively enhanced by attention for task-relevant, but not -irrelevant, events.

The osteoarthritis associated sphingolipid sphingomyelin 34:1 causes inflammatory pain in mice
Osteoarthritis (OA) is a condition affecting synovial joints that has a multifactorial pathogenesis and where pain is the main symptom driving clinical decision making. During OA, a plethora of mediators are released by infiltrating immune cells and resident cells, such fibroblast-like synoviocytes. Although the roles of certain OA-associated disease mediators are well-understood, there are a number of molecules that are dysregulated in OA for which no role has been identified. For example, in dogs and humans with OA, dysregulation of the synovial fluid lipidome occurs and some findings have been replicated by studying the plasma lipidome in a mouse model of osteoarthritis. One upregulated lipid is the sphingomyelin N-palmitoyl-D-erythro-sphingosylphosphorylcholine (d18:1/16:0), also known as SM(d34:1), referred to here as SM. This study investigated the ability SM to cause joint pain and neuronal hyperexcitability in mice. Overnight incubation of sensory neurons with either SM or a structurally related ceramide produced a decrease in rheobase, i.e. hyperexcitability. By contrast, when injected into the knee joint of mice, SM, but not the related ceramide, evoked joint swelling, mechanical hyperalgesia and decreased digging behaviour. Moreover, when studying the excitability of retrograde traced, knee-innervating sensory neurons, only those isolated from SM-injected mice exhibited hyperexcitability. The results generated demonstrate that a dysregulated lipidome can contribute to inflammatory OA pain, further work being necessary to determine the mechanism by which SM exerts its activity.

Anatomical characterisation of somatostatin-expressing neurons belonging to the anterolateral system
Anterolateral system (ALS) spinal projection neurons are essential for pain perception. However, these cells are heterogeneous, and there has been extensive debate about the roles of ALS populations in the different pain dimensions. We recently performed single-nucleus RNA sequencing on a developmentally-defined subset of ALS neurons, and identified 5 transcriptomic populations. One of these, ALS4, consists of cells that express Sst, the gene coding for somatostatin, and we reported that these were located in the lateral part of lamina V. Here we use a SstCre mouse line to characterise these cells and define their axonal projections. We find that their axons ascend mainly on the ipsilateral side, giving off collaterals throughout their course in the spinal cord. They target various brainstem nuclei, including the parabrachial internal lateral nucleus, and the posterior triangular and medial dorsal thalamic nuclei. We also show that in the L4 segment Sst is expressed by ~75% of ALS neurons in lateral lamina V and that there are around 120 Sst-positive lateral lamina V cells on each side. Our findings indicate that this is a relatively large population, and based on projection targets we conclude that they are likely to contribute to the affective-motivational dimension of pain.

Early-Life Immune Activation Shapes Microglial Responses and Reduces Abeta Pathology in 5xFAD mice
Early infection in life has been implicated in increasing the risk for neurological disorders. Here we performed single-cell sequencing of microglia and monocytes from 6-month-old WT and 5xFAD mice subjected to one dose of LPS (1mg/kg) at postnatal day 9. We successfully mapped disease-associated microglia (DAM) and perivascular macrophages in our data and demonstrated a subpopulation of microglia that adopted a monocyte-like profile, marked by Lyz2, Tmsb10, Lgals1and Lgals3. This unique subset appeared in response to early systemic LPS challenge and AD pathology but diminished in the presence of double stimulus. Different cytokines were altered in the brain and periphery as seen using mesoscale plates. GM-CSF and MIP-1beta levels were altered in an amyloid-beta(A beta)-dependent manner in hippocampus. MIP-1beta and IFN-gamma were altered upon early LPS stimulation. In the periphery, we found MMP-9 was significantly increased in serum samples from 5xFAD mice. Interestingly, early LPS stimulation significantly elevated TNF-alpha in serum from WT and 5xFAD mice, but was reduced in the hippocampus due to A beta pathology. The LPS treatment in 5xFAD mice had a tendency to improve the short-term memory deficit. Taken together, we observed long-lasting effects from early life stress, including activation of inflammation in the periphery and brain through modulation of different signaling cascades.

MRI R2* captures inflammation in disconnected brain structures after stroke: a translational study
Ischemic strokes disrupt brain networks, leading to remote effects in key regions like the thalamus, a critical hub for brain functions. However, non-invasive methods to quantify these remote consequences still need to be explored. This study aimed to demonstrate that MRI-derived R2* changes can capture iron accumulation linked with inflammation secondary to stroke-induced disconnection. In order to link remote R2* changes to stroke-induced disconnection, we first conducted a secondary analysis of 156 prospectively included stroke patients who underwent MRI at baseline and 1-year follow-up. We mapped fibers disconnected by baseline infarcts to compare the R2* changes over 1 year according to the disconnectivity status in specific thalamic nuclei groups. We also identified the predictors of elevated R2* at 1 year in a multivariate context through linear regressions. In parallel, to understand the biological underpinning of the remote R2* changes, we set up a translational mouse model through photothrombotic induction of focal cortical infarcts or sham procedures in 110 C57BL/6J mice. We explored the mice through combinations of in vivo MRI at 72h, 2-, 4-, and 8-weeks, histology, qPCR for gene expression, mass spectrometry for iron concentration quantification, and additional ex vivo high-resolution diffusion tensor imaging. In stroke patients, we found a significant increase of R2* within severely disconnected medial and lateral thalamic nuclei groups from baseline to 1 year. At the same time, no change occurred if these structures were not disconnected. We also showed that the disconnectivity status at baseline was a significant predictor of R2* at follow-up, independently from confounders, establishing a direct and independent relationship between baseline disconnection and the subsequent R2* increase within the associated locations. In mice, we recapitulated the patients' conditions by observing increased R2* in the stroke groups, specifically within the disconnected thalamic nuclei. Such remote and focal R2* changes peaked at 2 weeks, preceding and correlating with longer-term atrophy at 8 weeks. We established that the remote R2* increase was spatially and temporally correlated with a significant increase of chemically determined iron load bound to ferritin within activated microglial cells. This study provides critical evidence that R2* is a sensitive marker of inflammation secondary to network disconnection, potentially informing future neuroprotective strategies targeting remote brain regions after stroke.

Neurostructural and cognitive signatures of novel polygenic risk scores for molecular brain aging
The world population is shifting sharply toward an older-age demographic. To navigate the escalating burden of physical and cognitive decline common to aging, and heightened risk of neurodegenerative and neuropsychiatric disease, we require advances in treatment and prevention interventions. These advances are predicated on attaining a deeper understanding of the molecular processes underlying brain aging. Here, we employed novel GWAS and cis-eQTL-based polygenic risk scores (GWASAGE-PRS and cis-eQTLAGE-PRS) indexing genetic risk for accelerated molecular brain aging, and examined their associations with cortical thickness and performance in age-sensitive cognitive domains in 31 384 participants (16 392 women, age 64.1+/-7.65) from the UK Biobank. While GWASAGE-PRS was nominally associated with lower cortical thickness in frontotemporal regions, cis-eQTLAGE-PRS displayed robust associations with greater cortical thickness in age-sensitive frontal, temporal, and parietal regions, including the left and right precentral (pFDR<0.0001, pFDR=0.05), left insula (pFDR=0.05), as well as the right supramarginal (pFDR=0.05) and precuneus (pFDR=0.05) regions. Similar pFDR trending associations occurred bilaterally in the caudal middle frontal (pFDR=0.052, pFDR=0.078) and right insula (pFDR=0.071). These structural findings co-occurred alongside increased executive function performance on the Trail Making Test B (pFDR=0.035), suggesting a potential neurostructural and cognitive reserve phenotype. This resilience profile may reflect previously uncharacterized pathways of brain reserve in age-related pathology, informing future translational research identifying novel treatment and prevention targets.