Biology subjects

Barnett, A. J.

Publications and source records attributed to Barnett, A. J..

8 recordsLinked to original sources

bioRxiv2026

HippoGenes: A robust workflow for subregional transcriptomic profiling of the human hippocampus

The human hippocampus is a unique cortical structure central to brain function, plasticity, and disease. Unravelling its complex organization requires the integration of multiscale data, linking molecular features to mesoscale anatomy and macroscale functional patterns. Gene expression is a fundamental microscale phenotype, and its profiling can provide a reference description of how molecular features are distributed across the brain. Capitalizing on recent imaging-transcriptomic analyses, we introduce HippoGenes, a repository of fine-grained gene expression patterns across human hippocampal subregions. We leveraged spatial statistical models and hippocampal surface mapping to reconstruct dense transcriptomic maps from sparse post-mortem tissue samples of the Allen Human Brain Atlas, generating continuous expression estimates for thousands of genes aligned to a common surface-based coordinate system. We illustrate the utility of HippoGenes to (i) map medial-lateral and anterior-posterior transcriptomic gradients that align with subfield and tripartite subdivisions of the hippocampal formation, (ii) examine associations between gene expression and canonical microstructural and functional features of the hippocampus, and (iii) perform a molecular decoding of subregional alterations in neurological patients with hippocampal pathology. HippoGenes provides a framework for exploring the molecular organization of the hippocampus, opening avenues for multiscale integration in health and disease, and is openly available on https://hippogenes.readthedocs.io/.

neuroscience↗

bioRxiv2025

Memory network activity flow failures in temporal and frontal lobe epilepsy

Declarative memory deficits represent considerable challenges to adequate functioning and wellbeing in temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE), two of the most common pharmaco-resistant epilepsies. TLE and FLE are impacted differently however, with TLE affecting primarily episodic, and mildly semantic memory, while FLE presenting with overall lesser declarative impairments and a greater involvement of language processes. Although functional magnetic resonance imaging (fMRI) studies are overall compatible with differential disruptions of medial temporal and fronto-limbic networks in both syndromes, direct comparisons of brain activity and connectivity remain scarce. The current study investigated how alterations in intrinsic functional brain organization, as mapped with resting-state connectivity (rsFC), shapes altered brain network activations in both episodic and semantic memory states. To this end, we acquired task- and rs-fMRI data in 28 TLE patients and 17 FLE patients and 87 age- and sex-matched healthy controls (HCs). We used activity flow mapping (AFM), a generative machine learning technique that derives plausible task activation patterns via individualized rsFC as well as normative task activation data. Previous studies in HC as well as patient populations have shown that this technique has been effective in identifying mechanisms contributing to atypical functional organization across different context. Overall, AFM reliably predicted task activations across HC, TLE, and FLE, but prediction accuracy was consistently reduced in both patient groups, indicating impaired propagation of task-relevant signals. These reductions co-occurred with abnormal episodic and semantic task activation patterns, atypical rsFC, and behavioral profiles marked by preserved semantic but impaired episodic memory performance in both patient groups. Importantly, although neither task-evoked abnormalities nor rsFC disruptions alone were associated with clinical variables, lower AFM accuracy in patients robustly tracked poorer episodic and semantic memory performance and longer disease duration. Prediction accuracy was driven more by intrinsic functional than structural network features, suggesting that altered network communication, rather than gross anatomy, constrains AFM in pharmaco-resistant epilepsy. Our work revealed syndrome-specific yet convergent disruptions in paralimbic and heteromodal association systems, linking intrinsic dysconnectivity to a shared episodic vulnerability and semantic resilience across TLE and FLE. By modelling how intrinsic connectivity shapes task-evoked responses, AFM provides a mechanistic account of imbalanced memory-state activations and isolates network-flow pathways as targets for intervention and rehabilitation.

neuroscience↗

bioRxiv2025

Natural language processing captures memory content associated with shared neural patterns at encoding

People can experience the same event yet form distinct memories shaped by individual interpretations. Prior research shows that multivariate activity patterns in the Default Mode Network (DMN) are correlated across individuals during shared experiences, suggesting a role in representing high-level event features. However, it remains unclear whether these shared neural patterns reflect similarity in subsequent memory content. Here, we examined whether memory similarity correlates with intersubject spatial patterns in the DMN. Using topic modeling, we transformed verbal recall into vectors of latent topics to quantify memory similarity across participants. Twenty-four individuals watched and recounted two cartoon movies during fMRI scanning. We found that greater similarity in recalled content was associated with stronger shared activation patterns at encoding, particularly in the posterior medial cortex. These findings highlight the utility of natural language processing tools in linking memory representations to brain activity and underscore the DMNs role in encoding and interpreting complex event features.

neuroscience↗

bioRxiv2025

Systematic comparison of Generative AI-Protein Models reveals fundamental differences between structural and sequence-based approaches.

Recent advances in artificial intelligence have led to the development of generative models for de novo protein design. We compared 13 state-of-the-art generative protein models, assessing their ability to produce feasible, diverse, and novel protein monomers. Structural diffusion models generally create designs with higher confidence in predicted structures and more biologically plausible energy distributions, but exhibit limited diversity and strong sequence biases. Conversely, protein language models generate more diverse and novel designs but with lower structural confidence. We also evaluated these models ability to generate unique proteins, conditionally based on the Tobacco Etch Virus (TEV) protease. Generative models were successful in producing functional enzymes, albeit with diminished activity compared to the wildtype TEV. Our systematic benchmarking provides a foundation for evaluating and selecting generative protein models, while highlighting the complementary strengths of different generative paradigms. This framework will facilitate an informed application of these tools for bio-medical engineering and design.

synthetic biology↗

bioRxiv2022

Leveraging the resting brain to predict memory decline after temporal lobectomy

ObjectivesAnterior temporal lobectomy as a treatment for temporal lobe epilepsy is associated with a variable degree of postoperative memory decline, and estimating this decline for individual patients is a critical step of preoperative planning. Presently, predicting memory morbidity relies on indices of preoperative temporal lobe structural and functional integrity. However, epilepsy is increasingly understood as a network disorder, and memory a network phenomenon. We aimed to assess the utility of functional network measures to predict postoperative memory changes. MethodsPatients with left and right temporal lobe epilepsy (TLE) were recruited from an epilepsy clinic. Patients underwent preoperative resting-state fMRI (rs-fMRI) and pre- and postoperative neuropsychological assessment approximately one year after surgery. We compared functional connectivity throughout the memory network of each patient to a healthy control template based on 19 individuals to identify differences in global organization. A second metric indicated the degree of integration of the to-be-resected temporal lobe with the rest of the memory network. We included these measures in a linear regression model alongside standard clinical and demographic variables as predictors of memory change after surgery. ResultsSeventy-two adults with TLE were included in this study (37 left/35 right). Left TLE patients with more abnormal memory networks, and with greater functional integration of the to-be-resected region with the rest of the memory network preoperatively, experienced the greatest decline in verbal memory after surgery. Together, these two measures explained 44% of variance in verbal memory change (F(2,31)=12.01, p=0.0001), outperforming standard clinical and demographic variables. None of the variables examined in this study were associated with visuospatial memory change in patients with right TLE. ConclusionResting-state connectivity provides valuable information concerning both the integrity of to-be-resected tissue as well as functional reserve across memory-relevant regions outside of the to-be-resected tissue. Intrinsic functional connectivity has the potential to be useful for clinical decision-making regarding memory outcomes in left TLE, and more work is needed to identify the factors responsible for differences seen in right TLE.

neuroscience↗

bioRxiv2022

Long-term, multi-event surprise enhances autobiographical memory

Neurobiological and psychological models of learning emphasize the importance of prediction errors (surprises) for memory formation. These effects have focused on memory for information surrounding a momentary surprising event; however, it is less clear whether surprise that unfolds across multiple events and timescales impacts memory. We asked basketball fans about their most positive and negative autobiographical memories of individual plays, games, and seasons, allowing surprise measurements spanning seconds, hours, and months. We used advanced analytics on National Basketball Association play-by-play data and betting odds spanning 17 seasons, >22K games, >5.6M plays to compute and align the estimated surprise values of each memory. We found that surprising events biased positive memories on the scale of seconds and months and negative memories across all three timescales. Critically, game and season memories could not be explained by surprise at shorter timescales, suggesting that long-term, multi-event surprise influences memory. These results expand the role of surprise in models of learning and reinforce its relevance in real-world domains.

neuroscience↗

bioRxiv2022

Dynamic hippocampal-cortical interactions during event boundaries support retention of complex narrative events

According to most memory theories, encoding involves continuous communication between the hippocampus and neocortex leaving the temporal dynamics of hippocampal-neocortical interactions often overlooked. Recent work has shown that we perceive complex events in our lives as dynamic, with relatively distinct starting and stopping points known as event boundaries. Event boundaries may be important for memory, as they are associated with increased activity in the hippocampus, and extended neocortical regions (the posterior cingulate cortex, lateral parietal cortex, and parahippocampal cortex). Our objective was to determine how functional connectivity between the hippocampus and neocortical regions during the encoding of naturalistic events (movies) related to subsequent retrieval and retention of those events. Participants encoded two 16-minute cartoon movies during fMRI scanning. After encoding, participants freely recalled one of the movies immediately, and the other after a 2-day delay. We quantified hippocampal-neocortical functional connectivity (FC) at time windows around each event onset, middle, and offset, and compared these FC measures with subsequent recall. These analyses revealed that higher FC between the hippocampus and the posterior medial network (PMN) at an events offset related to whether that event was subsequently recalled. In contrast, mid-event connectivity between the hippocampus and PMN was associated with poorer memory. Furthermore, hippocampal-PMN offset connectivity predicted not only whether events were retained in memory, but also the degree to which these events could be recalled in detail after a 2-day delay. These data demonstrate that the relationship between memory encoding and hippocampal-neocortical interaction is more dynamic than suggested by most memory theories, and they converge with recent modeling work suggesting that event offset is an optimal time for encoding.

neuroscience↗

bioRxiv2020

Organization of cortico-hippocampal networks in the human brain

Episodic memory depends on interactions between the hippocampus and the interconnected regions comprising default mode network (DMN). Here, using data-driven analyses of resting-state fMRI data, we identified the networks that interact with the hippocampus--the DMN and a "Medial Temporal Network" (MTN) that included regions in the medial temporal lobe and retrosplenial cortex. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into three subnetworks: a "Posterior-Medial" Subnetwork comprised of posterior cingulate, and lateral parietal cortices, an "Anterior-Temporal" Subnetwork comprised of regions in the temporopolar, and dorsomedial prefrontal cortex, and a "Medial-Prefrontal" Subnetwork comprised of regions primarily in the medial prefrontal cortex. These networks vary in their functional connectivity along the hippocampal long-axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.

neuroscience↗