Biology subjects

Ji, J. L.

Publications and source records attributed to Ji, J. L..

17 recordsLinked to original sources

bioRxiv2025

QuNex Recipes: Executable, Human-Readable Workflows for Reproducible Neuroimaging Research

Preprocessing and analysis of neuroimaging data are technically demanding, often requiring a combination of multiple software tools, modality-specific pipelines, and extensive parameter tuning to match dataset characteristics. These complexities make it difficult to document workflows in sufficient detail to ensure complete transparency and reproducibility. To address these challenges, we introduce QuNex recipes, a framework for defining and executing complete neuroimaging workflows - encompassing data onboarding, preprocessing, and analysis - in a transparent, machine- and human-readable format. Recipes are implemented as an integrated feature of the Quantitative Neuroimaging Environment & Toolbox (QuNex), a containerized, open-source platform for end-to-end multimodal and multi-species neuroimaging processing. The recipes framework enables seamless integration of QuNex commands with custom scripts and external tools, capturing every processing step and parameter setting. A fully reproducible study can thus be shared and replicated by providing only (a) the QuNex version used, (b) the recipe file, and (c) the data. This approach standardizes workflow specification, enhances transparency, and enables one-command replication of complex neuroimaging analyses. By providing a standardized way to describe and share workflows, recipes facilitate open exchange of best practices and reproducible methods within the neuroimaging community.

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bioRxiv2025

Ketamine Alters Tuning of Neural and Behavioral Spatial Working Memory Precision

Deficits in working memory (WM) are a hallmark of neuropsy-chiatric disorders such as schizophrenia, yet their neurobiological basis remains poorly understood. Glutamate N-methyl-D-aspartate receptors (NMDARs) are critical for spatial WM (sWM), with NMDAR antagonist ketamine known to attenuate task-evoked activation and reduce sWM accuracy. Cortical microcircuit models hypothesize that NMDAR antagonism impairs sWM by broadening neural spatial tuning, but this mechanism has not been directly tested in humans. Using a pharmacological fMRI approach, we showed how ketamine broadened neural spatial tuning, attenuated activation across visual, parietal, and frontal areas, and worsened sWM performance in healthy humans. Ketamine-induced changes in tuning were more consistent across individuals and brain regions than changes in overall activation and correlated with individual differences in sWM performance. These findings provide empirical evidence linking NMDAR antagonism to disruptions in cortical microcircuit dynamics, the resulting neural tuning alterations, and sWM impairments, advancing frameworks for therapeutic development.

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bioRxiv2024

Personalized circuit modeling captures variation in cortical functional connectivity

Functional magnetic resonance imaging (fMRI) of the human cortex reveals patterns of correlated neural dynamics that are individual-specific and associated with phenotypic variation. However, circuit mechanisms underlying individual variation in functional connectivity (FC) are not well understood. Here, we fit individual-level FC patterns with a biophysically-based circuit model of large-scale cortical dynamics. This model is fit with a small number of neurophysiologically interpretable parameters, and incorporates a hierarchical gradient in local synaptic strengths across cortex parameterized via the structural MRI-derived T1w/T2w map. We applied our modeling framework to resting-state fMRI FC from a large cohort of subjects (N=842) from the Human Connectome Project. We found that the model captures a substantial portion of individual variation in FC, especially with personalized degrees of local synaptic specialization along the hierarchical gradient. Furthermore, the model can capture to the within-subject variation in FC across scans. Empirically, we found that principal modes of individual variation in FC follow interpretable topographic patterns. We developed a framework to assess model expressivity via how these empirical modes of FC variation align with variations in simulated FC induced by parameter perturbations. This framework reveals a straightforward mapping between key parameters and the leading modes of variation across subjects and provides a principled approach to extending computational models. Collectively, our modeling results establish a foundation for personalized computational modeling of functional dynamics in large-scale brain circuits.

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bioRxiv2023

Human brain state dynamics reflect individual neuro-phenotypes

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, en-compassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of statetrait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

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bioRxiv2022

Ketamine induces multiple individually distinct whole-brain functional connectivity signatures

BackgroundKetamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamines molecular mechanisms connect to its neural and behavioral effects. MethodsWe conducted a double-blind placebo-controlled study in which 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hour). We quantified resting-state functional connectivity via data-driven global brain connectivity, related it to individual ketamine-induced symptom variation, and compared it to cortical gene expression targets. ResultsWe found that: i) both the neural and behavioral effects of acute ketamine are multi-dimensional, reflecting robust inter-individual variability; ii) ketamines data-driven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, implicating the role of SST and PVALB interneurons in ketamines acute effects; and iii) behavioral data-driven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the single-subject level. ConclusionsCollectively, these findings support the possibility for developing individually precise pharmacological biomarkers for treatment selection in psychiatry. FundingThis study was supported by NIH grants DP5OD012109-01 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA012870-11 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1-190420); Swiss Neuromatrix Foundation (Grant No. 2016-0111m Grant No. 2015 - 010); Swiss National Science Foundation under the frame-work of Neuron Cofund (Grant No. 01EW1908), Usona Institute (2015 - 2056).

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bioRxiv2022

QuNex -- An Integrative Platform forReproducible Neuroimaging Analytics

Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration (1-3), particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. The platform enables inter-operable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia (4), including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.

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bioRxiv2021

Computational Neuroimaging of Cognition-Emotion Interactions: Affective and Task-similar Interference Differentially Impact Working Memory

Cognition depends on resisting interference and responding to relevant stimuli. Distracting information, however, varies based on content, requiring distinct filtering mechanisms. For instance, affective information captures attention, disrupts performance and attenuates activation along frontal-parietal regions during cognitive engagement, while recruiting bottom-up regions. Conversely, distraction matching task features (i.e. task-similar) increases fronto-parietal activity. Neural mechanisms behind unique effects of different distraction on cognition remain unknown. Using fMRI in 45 adults, we tested whether affective versus task-similar interference show distinct signals during delayed working memory (WM). We found robust differences between distractor types along fronto-parietal versus affective-ventral neural systems. We studied a hypothesized mechanism of this effect via a biophysically-based computational WM model that implements a functional antagonism between affective/cognitive neural modules. This architecture reproduced experimental effects: task-similar distractors increased, whereas affective distractors attenuated cognitive module activity while increasing affective module signals. The model architecture suggested that task-based connectivity may be altered in affective-ventral vs. fronto-parietal networks depending on distractor type. Empirically, affective interference significantly increased connectivity within the affective-ventral network, but reduced connectivity between affective-ventral and fronto-parietal networks, which predicted WM performance. These findings detail an antagonistic architecture between cognitive and affective systems, capable of flexibly engaging distinct distractions during cognition.

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bioRxiv2021

Transcriptomics-informed large-scale cortical model captures topography of pharmacological neuroimaging effects of LSD

Psychoactive drugs can transiently perturb brain physiology while preserving brain structure. The role of physiological state in shaping neural function can therefore be investigated through neuroimaging of pharmacologically-induced effects. This paradigm has revealed that neural and experiential effects of lysergic acid diethylamide (LSD) are attributable to its agonist activity at the serotonin-2A receptor. Here, we integrate brainwide transcriptomics with biophysically-based large-scale circuit modeling to simulate acute neuromodulatory effects of LSD on human cortical dynamics. Our model captures the topographic effects of LSD-induced changes in cortical BOLD functional connectivity. These findings suggest that serotonin-2A-mediated modulation of pyramidal cell gain is the circuit mechanism through which LSD alters cortical functional topography. Individual-subject fitting reveals that the model captures patterns of individual neural differences in drug response that predict altered states of consciousness. This work establishes a framework for linking molecular-level manipulations to salient changes in brain function, with implications for precision medicine.

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bioRxiv2020

Mapping Brain-Behavior Space Relationships Along the Psychosis Spectrum

Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlight a need to develop a stable neurobiologically-grounded mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 cross-diagnostic PSD patients, we derived and replicated a dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits. In turn, these symptom axes mapped onto distinct, reproducible brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) do not produce stable results. Instead, we show that a univariate brain-behavioral space (BBS) can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and gene expression maps. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable path that can be iteratively optimized for personalized clinical biomarker endpoints.

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bioRxiv2020

On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations

Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS solutions is stability of feature patterns driving an association. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues in a systematic manner, we developed a generative modeling framework to simulate synthetic datasets, parameterized by dimensionality, variance structure, and association strength. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the latent pattern underlying the discovered association. We confirmed these trends across two neuroimaging modalities, functional and diffusion MRI, and in independent datasets, Human Connectome Project (n{approx}1000) and UK Biobank (n{approx}20000) and found that only the latter comprised sufficient samples for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses for future studies.

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bioRxiv2018

Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics

The large-scale organization of dynamical neural activity across cortex emerges through long-range interactions among local circuits. We hypothesized that large-scale dynamics are also shaped by heterogeneity of intrinsic local properties across cortical areas. One key axis along which microcircuit properties are specialized relates to hierarchical levels of cortical organization. We developed a large-scale dynamical circuit model of human cortex that incorporates heterogeneity of local synaptic strengths, following a hierarchical axis inferred from MRI-derived T1w/T2w mapping, and fit the model using multimodal neuroimaging data. We found that incorporating hierarchical heterogeneity substantially improves the model fit to fMRI-measured resting-state functional connectivity and captures sensory-association organization of multiple fMRI features. The model predicts hierarchically organized high-frequency spectral power, which we tested with resting-state magnetoencephalography. These findings suggest circuit-level mechanisms linking spatiotemporal levels of analysis and highlight the importance of local properties and their hierarchical specialization on the large-scale organization of human cortical dynamics.

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bioRxiv2018

A whole-brain and cross-diagnostic perspective on functional brain network dysfunction

A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations have seemed to support various theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in broad, whole-brain perspective. Using a graph distance measure - connectome-wide correlation - we found that whole-brain resting-state functional network organization in humans is highly similar across a variety of mental diseases and healthy controls. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease those differences are informative. Such small network alterations may reflect the fact that most psychiatric patients maintain overall cognitive abilities similar to those of healthy individuals (relative to, e.g., the most severe schizophrenia cases), such that whole-brain functional network organization is expected to differ only subtly even for mental diseases with devastating effects on everyday life. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases.

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bioRxiv2017

Reciprocal Disruptions in Thalamic and Hippocampal Resting-State Functional Connectivity in Youth with 22q11.2 Deletions

22q11.2 deletion syndrome (22q11DS) is a recurrent copy number variant (CNV) with high penetrance for developmental neuropsychiatric disorders. Study of individuals with 22q11DS therefore may offer key insights into neural mechanisms underlying such complex illnesses. Resting-state functional MRI (rs-fMRI) studies in idiopathic schizophrenia have consistently revealed disruption of thalamic and hippocampal circuitry. Here, we sought to test whether this circuitry is similarly disrupted in the context of this genetic high-risk condition. To this end, resting-state functional connectivity patterns were assessed in a sample of young men and women with 22q11DS (n=42) and demographically matched healthy controls (n=39). Neuroimaging data were acquired via single-band protocols, and analyzed in line with methods provided by the Human Connectome Project (HCP). We computed functional relationships between individual-specific anatomically-defined thalamic and hippocampal seeds and all gray matter voxels in the brain. Whole-brain type I error protection was achieved through nonparametric permutation-based methods. 22q11DS patients displayed reciprocal disruptions in thalamic and hippocampal functional connectivity relative to control subjects. Thalamo-cortical coupling was increased in sensorimotor cortex, and reduced across associative networks. The opposite effect was observed for the hippocampus in regards to sensory and associative network connectivity. The thalamic and hippocampal dysconnectivity observed in 22q11DS suggest that high genetic risk for psychiatric illness is linked with disruptions in large-scale cortico-subcortical networks underlying higher-order cognitive functions. These effects highlight the translational importance of large-effect CNVs for informing mechanisms underlying neural disruptions observed in idiopathic developmental neuropsychiatric disorders.\n\nSIGNIFICANCE STATEMENTInvestigation of neuroimaging biomarkers in highly penetrant genetic syndromes represents a more biologically tractable approach to identify neural circuit disruptions underlying developmental neuropsychiatric conditions. 22q11.2 deletion syndrome confers particularly high risk for psychotic disorders, and is thus an important translational model in which to investigate systems-level mechanisms implicated in idiopathic illness. Here, we show resting-state fMRI evidence of large-scale sensory and executive network disruptions in youth with 22q11DS. In particular, this study provides the first evidence that these networks are disrupted in a reciprocal fashion with regard to the functional connectivity of the thalamus and hippocampus, suggesting circuit-level dysfunction.

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bioRxiv2017

Changes in global brain connectivity in LSD-induced altered states of consciousness are attributable to the 5-HT2A receptor

Lysergic acid diethylamide (LSD) is a psychedelic drug with predominantly agonist activity at various serotonin (5-HT) and dopamine receptors. Despite the therapeutic and scientific interest in LSD, the specific receptor contributions to its neurobiological effects remain largely unknown. To address this knowledge gap, we conducted a double-blind, randomized, counterbalanced, cross-over study during which 24 healthy participants received either i) placebo+placebo, ii) placebo+LSD (100 g po), or iii) ketanserin - a selective 5-HT2A receptor antagonist. Here we focus on resting-state fMRI, a measure of spontaneous neural fluctuations that can map functional brain connectivity. We collected resting-state data 75 and 300 minutes after LSD/placebo administration. We quantified resting-state functional connectivity via a fully data-driven global brain connectivity (GBC) method to comprehensively map LSD neuropharmacological effects. LSD administration caused widespread GBC alterations that followed a specific topography: LSD reduced connectivity in associative areas, but concurrently increased connectivity across sensory and somatomotor areas. The 5-HT2A receptor antagonist, ketanserin, fully blocked the subjective and neural LSD effects. We show that whole-brain data-driven spatial patterns of LSD effects matched 5-HT2A receptor cortical gene expression in humans, which along with ketanserin effects, strongly implicates the 5-HT2A receptor in LSDs neuropharmacology. Critically, the LSD-induced subjective effects were associated with somatomotor networks GBC changes. These data-driven neuropharmacological results pinpoint the critical role of 5-HT2A in LSDs mechanism, which informs its neurobiology and guides rational development of psychedelic-based therapeutics

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bioRxiv2017

Mapping the human brain’s cortical-subcortical functional network organization

Highlights O_LILarge-scale functional network map of the entire human brain\nC_LIO_LICortical networks based on multiband fMRI, recently-identified regions\nC_LIO_LISubcortical extension of networks covering all subcortical structures\nC_LIO_LIMultiple quality assessments demonstrate robustness of functional networks\nC_LIO_LINetwork atlas released as public resource, providing framework for future studies\nC_LI\n\nABSTRACTUnderstanding complex systems such as the human brain requires characterization of the systems architecture across multiple levels of organization - from neurons, to local circuits, to brain regions, and ultimately large-scale brain networks. Here we focus on characterizing the human brains large-scale network organization, as it provides an overall framework for the organization of all other levels. We developed a highly principled approach to identify cortical network communities at the level of functional systems, calibrating our community detection algorithm using extremely well-established sensory and motor systems as guides. Building on previous network partitions, we replicated and expanded upon well-known and recently identified networks, including several higher-order cognitive networks such as a left-lateralized language network. We expanded these cortical networks to subcortex, revealing 358 highly organized subcortical parcels that take part in forming whole-brain functional networks. Notably, the identified subcortical parcels are similar in number to a recent estimate of the number of cortical parcels (360). This whole-brain network atlas - released as an open resource for the neuroscience community - places all brain structures across both cortex and subcortex into a single large-scale functional framework, with the potential to facilitate a variety of studies investigating large-scale functional networks in health and disease.

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bioRxiv2017

Hierarchy of transcriptomic specialization across human cortex captured by myelin map topography

Hierarchy provides a unifying principle for the macroscale organization of anatomical and functional properties across primate cortex, yet the microscale bases of specialization across human cortex are poorly understood. Cortical hierarchy is conventionally informed by invasive measurements of long-range projections, creating the need for a principled proxy measure of hierarchy in humans. Moreover, cortex exhibits marked interareal variation in patterns of gene expression, yet organizing principles of its transcriptional architecture remain unclear. We hypothesized that functional specialization of human cortical microcircuitry involves hierarchical gradients of gene expression. We found that a noninvasive neuroimaging measure, the MRI-derived myelin map, reliably indexes hierarchy and closely resembles the dominant pattern of transcriptomic variation across human cortex. We found strong hierarchical gradients in expression profiles of genes related to microcircuit function and neuropsychiatric disorders. Our findings suggest that hierarchy defines an axis shared by the transcriptomic and anatomical architectures of human cortex, and that hierarchical gradients of microscale properties contribute to macroscale specialization of cortical function.

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bioRxiv2017

Schizophrenia Exhibits Bi-Directional Brain-Wide Alterations in Cortico-Striato-Cerebellar Circuits

Distributed neural dysconnectivity is considered a hallmark feature of schizophrenia, yet a tension exists between studies pinpointing focal disruptions versus those implicating brain-wide disturbances. The cerebellum and the striatum communicate reciprocally with the thalamus and cortex through monosynaptic and polysynaptic connections, forming cortico-striatal-thalamic-cerebellar (CSTC) functional pathways that may be sensitive to brain-wide dysconnectivity in schizophrenia. It remains unknown if the same pattern of alterations persists across CSTC systems, or if specific alterations exist along key functional elements of these networks. We characterized connectivity along major functional CSTC subdivisions using resting-state functional magnetic resonance imaging in 159 chronic patients and 162 matched controls. Associative CSTC subdivisions revealed consistent brain-wide bi-directional alterations in patients, marked by hyper-connectivity with sensory-motor cortices and hypo-connectivity with association cortex. Focusing on the cerebellar and striatal components, we validate the effects using data-driven k-means clustering of voxel-wise dysconnectivity and support vector machine classifiers. We replicate these results in an independent sample of 202 controls and 145 patients, additionally demonstrating that these neural effects relate to cognitive performance across subjects. Taken together, these results from complementary approaches implicate a consistent motif of brain-wide alterations in CSTC systems in schizophrenia, calling into question accounts of exclusively focal functional disturbances.

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