publications

2024

1. Network-Level Enrichment Provides a Framework for Biological Interpretation of Machine Learning Results

   Jiaqi Li, Ari Segel, Xinyang Feng, and 8 more authors

   Network Neuroscience, Jul 2024

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   Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.

2. Functional Parcellation of the Neonatal Cortical Surface

   Michael J Myers, Alyssa K Labonte, Evan M Gordon, and 16 more authors

   Cerebral Cortex, Feb 2024

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   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 existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. 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.

3. Early Life Neuroimaging: The Generalizability of Cortical Area Parcellations across Development

   Jiaxin Tu, Michael Myers, Wei Li, and 8 more authors

   bioRxiv : the preprint server for biology, Feb 2024

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4. Increasing Hub Disruption Parallels Dementia Severity in Autosomal Dominant Alzheimer Disease

   Jiaxin Cindy Tu, Peter R Millar, Jeremy F Strain, and 8 more authors

   Network Neuroscience, Feb 2024

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5. A Subset of Brain Regions within Adult Functional Connectivity Networks Demonstrate High Reliability across Early Development

   Jiaxin Cindy Tu, Yu Wang, Xintian Wang, and 8 more authors

   bioRxiv : the preprint server for biology, Feb 2024

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2023

1. Effects of Functional Network Model Definition on Biomarker Outcome Prediction

   Xinyang Feng, Jiaqi Li, Ari Segel, and 8 more authors

   In Organization for Human Brain Mapping, Feb 2023
2. Functional Parcellation of the Neonatal Brain

   Michael J Myers, Alyssa K Labonte, Evan M Gordon, and 8 more authors

   bioRxiv : the preprint server for biology, Feb 2023

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3. Development of Functional Systems in 0–2-Year-Olds

   Jiaxin Cindy Tu, Michael Myers, Chad Sylvester, and 8 more authors

   In Flux: The Society for Developmental Cognitive Neuroscience, Feb 2023
4. Developing and Benchmarking Functional Parcellations in Infant Neuroimaging Datasets

   Jiaxin Cindy Tu, Michael Myers, Chad Sylvester, and 17 more authors

   In Organization for Human Brain Mapping, Jun 2023
5. Brain Network Decoupling with Increased Serum Neurofilament and Reduced Cognitive Function in Alzheimer’s Disease

   Muriah D Wheelock, Jeremy F Strain, Patricia Mansfield, and 164 more authors

   Brain, Jan 2023

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   Abstract Neurofilament light chain, a putative measure of neuronal damage, is measurable in blood and CSF and is predictive of cognitive function in individuals with Alzheimer’s disease. There has been limited prior work linking neurofilament light and functional connectivity, and no prior work has investigated neurofilament light associations with functional connectivity in autosomal dominant Alzheimer’s disease. Here, we assessed relationships between blood neurofilament light, cognition, and functional connectivity in a cross-sectional sample of 106 autosomal dominant Alzheimer’s disease mutation carriers and 76 non-carriers. We employed an innovative network-level enrichment analysis approach to assess connectome-wide associations with neurofilament light. Neurofilament light was positively correlated with deterioration of functional connectivity within the default mode network and negatively correlated with connectivity between default mode network and executive control networks, including the cingulo-opercular, salience, and dorsal attention networks. Further, reduced connectivity within the default mode network and between the default mode network and executive control networks was associated with reduced cognitive function. Hierarchical regression analysis revealed that neurofilament levels and functional connectivity within the default mode network and between the default mode network and the dorsal attention network explained significant variance in cognitive composite scores when controlling for age, sex, and education. A mediation analysis demonstrated that functional connectivity within the default mode network and between the default mode network and dorsal attention network partially mediated the relationship between blood neurofilament light levels and cognitive function. Our novel results indicate that blood estimates of neurofilament levels correspond to direct measurements of brain dysfunction, shedding new light on the underlying biological processes of Alzheimer’s disease. Further, we demonstrate how variation within key brain systems can partially mediate the negative effects of heightened total serum neurofilament levels, suggesting potential regions for targeted interventions. Finally, our results lend further evidence that low-cost and minimally invasive blood measurements of neurofilament may be a useful marker of brain functional connectivity and cognitive decline in Alzheimer’s disease.

2021

1. Multicentric Tracking of Multiple Agents by Anterior Cingulate Cortex during Pursuit and Evasion

   Seng Bum Michael Yoo, Jiaxin Cindy Tu, and Benjamin Yost Hayden

   Nature Communications, Mar 2021

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   Successful pursuit and evasion require rapid and precise coordination of navigation with adaptive motor control. We hypothesize 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 find that dACC carries 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.

2020

1. Rules Warp Feature Encoding in Decision-Making Circuits

   R Becket Ebitz, Jiaxin Cindy Tu, and Benjamin Y Hayden

   PLoS biology, Mar 2020

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2. The Neural Basis of Predictive Pursuit

   Seng Bum Michael Yoo, Jiaxin Cindy Tu, Steven T. Piantadosi, and 1 more author

   Nature Neuroscience, Feb 2020

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2019

1. Rule Adherence Warps Decision-Making

   R Becket Ebitz, Jiaxin Cindy Tu, and Benjamin Y Hayden

   bioRxiv : the preprint server for biology, Feb 2019

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2. Ventromedial Prefrontal Cortex Tracks Multiple Environmental Variables during Search

   Priyanka S Mehta, Jiaxin Cindy Tu, Giuliana A LoConte, and 2 more authors

   Journal of Neuroscience, Feb 2019

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2018

1. Neural Representation of Allocentric and Egocentric Positions in a Dynamic Foraging Task

   Jiaxin Tu, Seng Bum Yoo, and Benjamin Y. Hayden

   In Society for Neuroscience Poster, Feb 2018

2017

1. Understanding Drosophila Navigation Using Visual and Olfactory Cues

   Jiaxin Tu, and Trevor J. Wardill

   Department of Physiology, Development and Neuroscience, University of Cambridge, Feb 2017