Biology subjects

Tu, J. C.

Publications and source records attributed to Tu, J. C..

11 recordsLinked to original sources

bioRxiv2026

Shared Fine-Scale Functional Organization in the Human Subcortex Revealed by Connectivity Hyperalignment

Subcortical structures are central to cognition, emotion, and social processing, and their dysfunction underlies a wide range of neurological and psychiatric conditions. Yet subcortical fine-scale functional organization has remained largely inaccessible to group-level fMRI analysis: nuclei are small and low-SNR, and their functional topography varies idiosyncratically across individuals in ways that anatomical alignment cannot resolve. This idiosyncrasy carries real stakes. For example, deep brain stimulation targets with more variable functional connectivity across individuals show correspondingly less consistent clinical outcomes. Here we show that this seemingly idiosyncratic subcortical organization in fact reflects shared, fine-grained structure that can be recovered by modeling each individual's connectivity in a common functional space (connectivity hyperalignment, CHA), rather than relying on anatomical correspondence. Applying CHA to fMRI data collected during naturalistic movie viewing, we found that aligning subcortical voxels by their cortical connectivity, rather than their anatomical location, sharply increased cross-subject correspondence in both stimulus-evoked response and connectivity profiles. Critically, this alignment sharpened rather than blurred local structure: neighboring voxels became more functionally distinct from one another, and cortical connectivity targets became substantially more decodable across subjects. These results indicate that human subcortical nuclei possess a common fine-scale functional architecture that is masked by anatomical variability but recoverable at the individual level. This reframes a central methodological constraint in subcortical neuroimaging: rather than trading spatial resolution for signal quality via smoothing, group-level analyses can achieve voxel-level precision by aligning function rather than anatomy, opening a path to studying cortical-subcortical circuit organization in greater detail.

neuroscience↗

bioRxiv2025

Neuronal Activity in Orbitofrontal Cortex during Trinary Choices under Risk

Economic choice entails computing and comparing the subjective values of different goods. Orbitofrontal cortex (OFC) is thought to contribute to both operations. However, previous work focused almost exclusively on binary choices, raising the question of whether current notions hold for multinary choices. Here we recorded from rhesus monkeys making trinary choices. Offers varied on three dimensions - juice flavor, quantity, and probability. In these experiments, quantity and probability varied continuously within a preset range. Animal choices were generally risk seeking and satisfied independence of irrelevant alternatives (IIA) - a fundamental assumption in standard economic theory. Different neurons encoded the values of individual offers, the choice outcome, and the chosen value - i.e., the same variables previously identified under binary choices. In addition, other cell groups encoded the chosen probability and the chosen hemifield. Notably, the activity of offer value cells reflected the risk attitude and fluctuated from session to session in ways that matched fluctuations observed behaviorally. In other words, the activity of these neurons reflected the subjective nature of value. Importantly, the representation of decision variables in OFC was invariant to changes in menu size - a property that effectively implies IIA. SIGNIFICANCEOrbitofrontal cortex (OFC) is necessary for the computation and the comparison of subjective values underlying economic choices. However, most previous studies examined choices between two options, and it remains unclear whether current notions apply to multinary choices. Barretto-Garcia and colleagues recorded from the OFC of monkeys choosing between three juice flavors offered in variable quantities and probabilities. Animals choices were consistent with the independence of irrelevant alternatives (IIA) - a condition necessary for rational behavior. Different neurons in OFC encoded the values of individual offers, the choice outcome, and the chosen value. The activity of value-encoding cells reflected the animals risk attitude. Importantly, the representation of decision variables was invariant to changes in menu size - a property that effectively implies IIA.

neuroscience↗

bioRxiv2025

Commonality and Variability in Functional Networks In Young Children Under 5 Years Old

Functional brain networks support human cognition, yet how individualized network architecture emerges in early childhood remains poorly understood. Averaging across participants can obscure age-specific organization and person-to-person differences, particularly in slowly developing association cortices. We developed an age-appropriate functional reference that captured common structure across toddlers without averaging away individual variability, enabling estimation of each childs networks from resting-state fMRI. Across cohorts of 8-60-month-old children, we found individualized network organization--including finer-scale subdivisions and emerging language lateralization--well before age five. Network layouts showed longitudinal stability, with greater consistency in sensory than association regions. Within-network connectivity was stronger and explained age-related variance when networks were defined using individualized rather than group-consensus topography. Left-lateralization of language networks tracked age-normalized verbal ability, linking early functional architecture to emerging cognition. These findings show that behaviorally relevant brain networks arise far earlier than previously recognized, providing a foundation for studying typical development and early biomarkers.

neuroscience↗

bioRxiv2025

Precision Functional Mapping of the Individual Human Brain Near Birth

Cortical areas are a fundamental organizational property of the brain, but their development in humans is not well understood. Key unanswered questions include whether cortical areas are fully established near birth, the extent of individual variation in the arrangement of cortical areas, and whether any such individual variation in cortical area location is greater in later-developing association areas as compared to earlier-developing sensorimotor areas. To address these questions, we used functional MRI to collect precision functional mapping (PFM) data in eight individual neonates (mean 42.7 weeks postmenstrual age) over 2-5 days (mean 77.9 minutes of low motion data per subject [framewise displacement <0.1]). Each subjects dataset was split into two roughly equal halves of data from different days of data collection to measure within-subject reliability and across-subject similarity. Whole-brain patterns of functional connectivity (FC) reached a mean within-subject, across-day reliability of r=0.78 with 41.9 minutes of data. Across subject similarity of whole-brain FC was r=0.62 on average and significantly lower than within-subject similarity (t=5.9, p<0.001). Using established methods to identify transitions in FC across the cortical surface, we identified sets of cortical areas for each individual that were subject-specific and highly reliable across split-halves (mean z=4.4, SD=1.4). The arrangement of cortical areas was thus individually specific across the entire cortical surface, and this individual specificity did not vary as a function of the sensorimotor-association axis. This study establishes the feasibility of neonatal PFM and suggests that cortical area arrangement is individually specific and largely established shortly following birth. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/663543v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@b6c63aorg.highwire.dtl.DTLVardef@1903235org.highwire.dtl.DTLVardef@1d05733org.highwire.dtl.DTLVardef@10d0770_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIIt is feasible to obtain precision functional mapping (PFM) in neonates, acquiring 60+ minutes of fMRI data in individual neonates over multiple days. C_LIO_LINeonatal functional connectivity (FC) measures obtained through PFM are moderately reliable with 40 minutes of data. C_LIO_LIPatterns of neonatal FC across the brain are individually specific. C_LIO_LICortical areas, based on patterns of homogenous FC, can be reliably identified across the entire cortical surface in individual neonates. C_LIO_LIThe arrangement of cortical areas at birth is individually specific across the entire brain. C_LI

neuroscience↗

bioRxiv2024

A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development

The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized into functional networks. These networks show stronger internal and weaker external functional connectivity (FC), with FC profiles more similar within the same network. Previous studies have shown these networks develop from nascent forms before birth to their mature, adult-like structures in childhood. However, these analyses often rely on adult functional network definitions. This study assesses the potential misidentification of infant functional networks when using adult models and explores the consequences and possible solutions to this problem. Our findings suggest that although adult networks only marginally describe infant FC organization better than chance, misidentification is primarily driven by specific areas. Restricting functional networks to areas with adult-like network clustering revealed consistent within-network FC across scans and throughout development. These areas are also near locations with low network identity variability. Our results highlight the implications of using adult networks for infants and offer guidance for selecting and utilizing functional network models based on research questions and scenarios. HighlightsO_LIPrevious studies primarily investigated age-specific networks in infants, with limited focus on how well adult networks describe infant functional connectivity (FC). C_LIO_LIur analysis identified a subset of areas in infants showing adult-like network organization, where within-network FC shows less age-related variation and higher scan-to-scan reliability. C_LIO_LIThese areas are positioned near locations with low variability in functional network identity in adults, indicating a potential link between developmental sequence and interindividual variability in functional network organization. C_LI

neuroscience↗

bioRxiv2023

Functional parcellation of the neonatal brain

The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that adult- and older infant-derived parcels are a poor fit with neonatal data, emphasizing the need for neonatal-specific parcels. We next derive a set of 283 cortical surface parcels from a sample of n=261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies. HIGHLIGHTSO_LINeonatal cortical surface parcels derived based on abrupt changes in functional connectivity (FC) were highly homogenous and were validated in external neonatal datasets. C_LIO_LIBorders between cortical parcels were smoother (less abrupt) in group-average neonatal data compared to adults, likely due to increased heterogeneity in boundary location across individual neonates. C_LIO_LIParcels derived from adults and older infants show poor fit with neonatal resting-state FC data, underscoring the need for a neonatal-specific parcellation. C_LI

neuroscience↗

bioRxiv2023

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels--a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasnt definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruptions severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in A{beta} PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

neuroscience↗

bioRxiv2023

Network level enrichment provides a framework for biological interpretation of machine learning results

Machine learning algorithms are increasingly used to identify brain connectivity biomarkers linked to behavior and clinical outcomes. However, non-standard methodological choices in neuroimaging datasets, especially those with families or twins, have prevented robust machine learning applications. Additionally, prioritizing prediction accuracy over biological interpretability has made it challenging to understand the biological processes behind psychopathology. In this study, we employed a linear support vector regression model to study the relationship between resting-state functional connectivity networks and chronological age using data from the Human Connectome Project. We examined the effect of shared variance from twins and siblings by using cross-validation, either randomly assigning or keeping family members together. We also compared models with and without a Pearson feature filter and utilized a network enrichment approach to identify predictive brain networks. Results indicated that not accounting for shared family variance inflated prediction performance, and the Pearson filter reduced accuracy and reliability. Enhancing biological interpretability was achieved by inverting the machine learning model and applying network-level enrichment on the connectome, while directly using regression coefficients as feature weights led to misleading interpretations. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for comprehensible biological interpretation.

neuroscience↗

bioRxiv2019

Rule adherence warps decision-making

We have the capacity to follow arbitrary stimulus-response rules, meaning policies that determine how we will behave across circumstances. Yet, it is not clear how rules guide sensorimotor decision-making in the brain. Here, we recorded from neurons in three regions linked to decision-making, the orbitofrontal cortex, ventral striatum, and dorsal striatum, while macaques performed a rule-based decision-making task. We found that different rules warped the neural representations of chosen options by expanding rule-relevant coding dimensions relative to rule-irrelevant ones. Some cognitive theories suggest that warping could increase processing efficiency by facilitating rule-relevant computations at the expense of irrelevant ones. To test this idea, we modeled rules as the latent causes of decisions and identified a set of "rule-free" choices that could not be explained by simple rules. Contrasting these with rule-based choices revealed that following rules decreased the energetic cost of decision-making while warping the representational geometry of choice. SIGNIFICANCE STATEMENTOne important part of our ability to adapt flexibly to the world around us is our ability to implement arbitrary stimulus-response mappings, known as "rules". Many studies have shown that when we follow a rule, its identity is encoded in neuronal firing rates. However, it remains unclear how rules regulate behavior. Here, we report that rules warp the way that sensorimotor information is represented in decision-making circuits: enhancing information that is relevant to the current rule at the expense of information that is irrelevant. These results imply that rules are implemented as a kind of attentional gate on what information is available for decision-making.

neuroscience↗

bioRxiv2019

Multicentric tracking of multiple agents by anterior cingulate cortex during pursuit and evasion

Successful pursuit and evasion require rapid and precise coordination of navigation with adaptive motor control. We hypothesized that the dorsal anterior cingulate cortex (dACC), which communicates bidirectionally with both the hippocampal complex and premotor/motor areas, would serve a mapping role in this process. We recorded responses of dACC ensembles in two macaques performing a joystick-controlled continuous pursuit/evasion task. We found that dACC multiplexes two sets of signals, (1) world-centric variables that together form a representation of the position and velocity of all relevant agents (self, prey, and predator) in the virtual world, and (2) avatar-centric variables, i.e. self-prey distance and angle. Both sets of variables are multiplexed within an overlapping set of neurons. Our results suggest that dACC may contribute to pursuit and evasion by computing and continuously updating a multicentric representation of the unfolding task state, and support the hypothesis that it plays a high-level abstract role in the control of behavior.

neuroscience↗

bioRxiv2019

The Neural Basis of Predictive Pursuit

It remains unclear how and to what extent non-human animals make demanding on-the-fly predictions during pursuit. We studied this problem in a novel laboratory pursuit task that incentivizes prediction of future prey positions. We trained three macaques to perform joystick-controlled pursuit of prey that followed intelligent escape algorithms. Subjects reliably aimed towards the preys likely future positions, indicating that they generate internal predictions and use those predictions to guide behavior. We then developed a generative model that explains real-time pursuit trajectories and showed that our subjects use prey position, velocity, and acceleration to make predictions. We identified neurons in the dorsal anterior cingulate cortex (dACC) whose responses track these three variables. These neurons multiplexed prediction-related variables with a distinct and explicit representation of the preys future position. Our results provide a clear demonstration that the brain can explicitly represent future predictions and highlight the critical role of anterior cingulate cortex for future-oriented cognition.\n\nOne-sentence summaryIn a dynamic pursuit environment, monkeys actively predict future prey positions and dACC neurons encode these future positions.

neuroscience↗