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Tie-Qiang Li

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YNIMG Journal 2026 Journal Article

Explainable MRI radiomics of the basal ganglia and ventral midbrain distinguishes Parkinson’s disease, SWEDD, and healthy controls

  • Jiaxin Liu
  • Yuan-Zhe Li
  • Haomin Yang
  • Chong Duan
  • Tie-Qiang Li

Distinguishing scans without evidence of dopaminergic deficit (SWEDD) from Parkinson's disease (PD) remains challenging on routine MRI. We extracted 1284 radiomic features from T1- and T2-weighted MRI within six a priori subcortical regions of interest in the PPMI cohort. A nested cross-validation framework (inner: univariate ANOVA or Kruskal-Wallis with BH-FDR correction, mRMR, and LASSO; outer: 10-fold) was used for feature selection and classifier training. Five supervised models were evaluated, and performance was summarized by outer-fold micro- and macro-averaged AUCs with bootstrap 95% confidence intervals. Fourteen non-redundant features, primarily from the ventral midbrain, thalamus, putamen, and nucleus accumbens, were retained. The XGBoost classifier achieved a macro-AUC of 0.85 (0.76-0.91), with class-wise AUCs of 0.93 for PD, 0.79 for SWEDD, and 0.79 for healthy controls. SHAP analysis identified ventral midbrain texture heterogeneity and thalamic contrast as dominant contributors to PD prediction, while nucleus accumbens texture and putaminal shape were most informative for SWEDD. Radiomic heterogeneity on standard MRI thus captures disease-relevant patterns along a PD-SWEDD-HC continuum. Although these features are indirect surrogates of microstructure, their spatial profiles align with iron- and connectivity-related alterations reported with quantitative susceptibility and diffusion MRI. This explainable radiomics framework enables biologically coherent, multi-class discrimination between PD and SWEDD, supporting low-burden stratification and hypothesis generation for quantitative MRI studies, with planned external validation in independent cohorts to confirm generalizability.

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EAAI Journal 2026 Journal Article

Hierarchical cerebral blood volume map synthesis from non-contrast magnetic resonance imaging sequences via global and local decomposition

  • Yihua Chen
  • Wangbin Ding
  • Guoqi Lin
  • Lukui Xiong
  • Xinhui Wang
  • Jiaxin Liu
  • Wentao Zhu
  • Zhaohua Lin

Cerebral blood volume (CBV) mapping is valuable for assessing brain tumor angiogenesis but relies on gadolinium-based contrast agents, which carry health risks. Generative Artificial Intelligence (AI) provides a promising non-contrast alternative; however, monolithic models have a fundamental structural limitation—they struggle to simultaneously maintain global anatomical coherence and preserve fine-grained pathological details, often leading to “texture washing” of critical vascular indicators. To overcome this, we propose HierSynth, a hierarchical framework that decomposes synthesis into separate global and local stages. A Global Synthesis Module first generates a coherent anatomical foundation. A Local Synthesis Module then refines high-frequency vascular textures exclusively in the residual domain, allowing independent optimization of fine details without compromising large-scale structure. To maintain diagnostic accuracy, we introduce a Perfusion-aware Constraint (PaC) that operates in the residual domain by supervising local refinements with perfusion-specific features (e. g. , vascular heterogeneity cues), ensuring fidelity to pathological perfusion patterns. Evaluations on internal and external datasets show HierSynth markedly outperforms state-of-the-art monolithic models, achieving higher Region of Interest (ROI) fidelity (e. g. , Structural Similarity Index (SSIM) improvement from 0. 4606 to 0. 4798). This translates clinically to better preservation of tumor vascular textures, enabling more reliable non-contrast assessment of angiogenesis. By structurally separating anatomy from pathology, HierSynth advances safer perfusion imaging and offers a generalizable paradigm for hierarchical synthesis in other diagnostic modalities requiring multi-scale detail preservation.

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NeurIPS Conference 2020 Conference Paper

Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization

  • Haoliang Li
  • Yufei Wang
  • Renjie Wan
  • Shiqi Wang
  • Tie-Qiang Li
  • Alex Kot

Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, the recent advanced models still require accessing sufficiently large and representative datasets for training, which is often unfeasible in clinically realistic environments. When trained on limited datasets, the deep neural network is lack of generalization capability, as the trained deep neural network on data within a certain distribution (e. g. the data captured by a certain device vendor or patient population) may not be able to generalize to the data with another distribution. In this paper, we introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding with a novel linear-dependency regularization term to capture the shareable information among medical data collected from different domains. As a result, the trained neural network is expected to equip with better generalization capability to the ``unseen" medical data. Experimental results on two challenging medical imaging classification tasks indicate that our method can achieve better cross-domain generalization capability compared with state-of-the-art baselines.

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YNICL Journal 2019 Journal Article

Juvenile myoclonic epilepsy has hyper dynamic functional connectivity in the dorsolateral frontal cortex

  • Yanlu Wang
  • Ivanka Savic Berglund
  • Martin Uppman
  • Tie-Qiang Li

PURPOSE: Characterize the static and dynamic functional connectivity for subjects with juvenile myoclonic epilepsy (JME) using a quantitative data-driven analysis approach. METHODS: Whole-brain resting-state functional MRI data were acquired on a 3 T whole-body clinical MRI scanner from 18 subjects clinically diagnosed with JME and 25 healthy control subjects. 2-min sliding-window approach was incorporated in the quantitative data-driven data analysis framework to assess both the dynamic and static functional connectivity in the resting brains. Two-sample t-tests were performed voxel-wise to detect the differences in functional connectivity metrics based on connectivity strength and density. RESULTS: The static functional connectivity metrics based on quantitative data-driven analysis of the entire 10-min acquisition window of resting-state functional MRI data revealed significantly enhanced functional connectivity in JME patients in bilateral dorsolateral prefrontal cortex, dorsal striatum, precentral and middle temporal gyri. The dynamic functional connectivity metrics derived by incorporating a 2-min sliding window into quantitative data-driven analysis demonstrated significant hyper dynamic functional connectivity in the dorsolateral prefrontal cortex, middle temporal gyrus and dorsal striatum. Connectivity strength metrics (both static and dynamic) can detect more extensive functional connectivity abnormalities in the resting-state functional networks (RFNs) and depict also larger overlap between static and dynamic functional connectivity results. CONCLUSION: Incorporating a 2-min sliding window into quantitative data-driven analysis of resting-state functional MRI data can reveal additional information on the temporally fluctuating RFNs of the human brain, which indicate that RFNs involving dorsolateral prefrontal cortex have temporal varying hyper dynamic characteristics in JME patients. Assessing dynamic along with static functional connectivity may provide further insights into the abnormal function connectivity underlying the pathological brain functioning in JME.

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YNIMG Journal 2016 Journal Article

Behavioral correlates of changes in hippocampal gray matter structure during acquisition of foreign vocabulary

  • Martin Bellander
  • Rasmus Berggren
  • Johan Mårtensson
  • Yvonne Brehmer
  • Elisabeth Wenger
  • Tie-Qiang Li
  • Nils C. Bodammer
  • Yee-Lee Shing

Experience can affect human gray matter volume. The behavioral correlates of individual differences in such brain changes are not well understood. In a group of Swedish individuals studying Italian as a foreign language, we investigated associations among time spent studying, acquired vocabulary, baseline performance on memory tasks, and gray matter changes. As a way of studying episodic memory training, the language learning focused on acquiring foreign vocabulary and lasted for 10weeks. T1-weighted structural magnetic resonance imaging and cognitive testing were performed before and after the studies. Learning behavior was monitored via participants' use of a smartphone application dedicated to the study of vocabulary. A whole-brain analysis showed larger changes in gray matter structure of the right hippocampus in the experimental group (N=33) compared to an active control group (N=23). A first path analyses revealed that time spent studying rather than acquired knowledge significantly predicted change in gray matter structure. However, this association was not significant when adding performance on baseline memory measures into the model, instead only the participants' performance on a short-term memory task with highly similar distractors predicted the change. This measure may tap similar individual difference factors as those involved in gray matter plasticity of the hippocampus.

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YNICL Journal 2016 Journal Article

Resting-state fMRI study of acute migraine treatment with kinetic oscillation stimulation in nasal cavity

  • Tie-Qiang Li
  • Yanlu Wang
  • Rolf Hallin
  • Jan-Erik Juto

Kinetic oscillatory stimulation (KOS) in the nasal cavity is a non-invasive cranial nerve stimulation method with promising efficacy for acute migraine and other inflammatory disorders. For a better understanding of the underlying neurophysiological mechanisms of KOS treatment, we conducted a resting-state functional magnetic resonance imaging (fMRI) study of 10 acute migraine patients and 10 normal control subjects during KOS treatment in a 3 T clinical MRI scanner. The fMRI data were first processed using a group independent component analysis (ICA) method and then further analyzed with a voxel-wise 3-way ANOVA modeling and region of interest (ROI) of functional connectivity metrics. All migraine participants were relieved from their acute migraine symptoms after 10-20 min KOS treatment and remained migraine free for 3-6 months. The resting-state fMRI result indicates that migraine patients have altered intrinsic functional activity in the anterior cingulate, inferior frontal gyrus and middle/superior temporal gyrus. KOS treatment gave rise to up-regulated intrinsic functional activity for migraine patients in a number of brain regions involving the limbic and primary sensory systems, while down regulating temporally the activity for normal controls in a few brain areas, such as the right dorsal posterior insula and inferior frontal gyrus. The result of this study confirms the efficacy of KOS treatment for relieving acute migraine symptoms and reducing attack frequency. Resting-state fMRI measurements demonstrate that migraine is associated with aberrant intrinsic functional activity in the limbic and primary sensory systems. KOS in the nasal cavity gives rise to the adjustment of the intrinsic functional activity in the limbic and primary sensory networks and restores the physiological homeostasis in the autonomic nervous system.

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YNIMG Journal 2014 Journal Article

Changes in perceptual speed and white matter microstructure in the corticospinal tract are associated in very old age

  • Martin Lövdén
  • Ylva Köhncke
  • Erika J. Laukka
  • Grégoria Kalpouzos
  • Alireza Salami
  • Tie-Qiang Li
  • Laura Fratiglioni
  • Lars Bäckman

The integrity of the brain's white matter is important for neural processing and displays age-related differences, but the contribution of changes in white matter to cognitive aging is unclear. We used latent change modeling to investigate this issue in a sample of very old adults (aged 81–103years) assessed twice with a retest interval of 2. 3years. Using diffusion-tensor imaging, we probed white matter microstructure by quantifying mean fractional anisotropy and mean diffusivity of six major white matter tracts. Measures of perceptual speed, episodic memory, letter fluency, category fluency, and semantic memory were collected. Across time, alterations of white matter microstructure in the corticospinal tract were associated with decreases of perceptual speed. This association remained significant after statistically controlling for changes in white matter microstructure in the entire brain, in the other demarcated tracts, and in the other cognitive abilities. Changes in brain volume also did not account for the association. We conclude that white matter microstructure is a potent correlate of changes in sensorimotor aspects of behavior in very old age, but that it is unclear whether its impact extends to higher-order cognition.

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YNIMG Journal 2013 Journal Article

Cortical responses to amphetamine exposure studied by pCASL MRI and pharmacokinetic/pharmacodynamic dose modeling

  • Love Engström Nordin
  • Tie-Qiang Li
  • Jacob Brogren
  • Patrik Johansson
  • Niclas Sjögren
  • Kristin Hannesdottir
  • Charlotta Björk
  • Märta Segerdahl

Introduction Perfusion measurement by arterial spin labeling (ASL) techniques is well suited for pharmaceutical magnetic resonance imaging (phMRI) studies to investigate how drugs change the cerebral perfusion status and further, neuronal activity. Materials and method Twelve healthy normal male volunteers participated in the study which was based on a double blinded design. Six subjects were randomly selected to receive a single oral dose of 20mg d-amphetamine and six were given placebo. Perfusion measurements by pseudo-continuous ASL (pCASL) technique were repeatedly performed at 10 different time points with a 3T clinical MRI scanner during a 10hour period after dose together with physiologic data and blood sample collections. The dynamic changes in cerebral perfusion in response to the plasma concentration variations of d-amphetamine were analyzed at voxel-level and for regions of interest. Results Compared to the placebo group a 20% reduction in cerebral blood flow (CBF) was observed in gray matter for the subjects that received d-amphetamine. The most significant reduction of regional CBF (rCBF) was detected in the basal ganglia, frontal region and insular cortex using voxel based analysis. A relation between d-amphetamine exposure and CBF response was found using PK/PD modeling, which predicted on average a 15% decrease of the CBF in gray matter at a plasma concentration of 30ng/ml. Conclusion In this study we have demonstrated that repeated perfusion measurements by pCASL technique was sufficiently robust to differentiate the neurological response between the groups that received d-amphetamine and placebo. Quantitative and repetitive CBF measurements can be used for PK/PD modeling of CNS drug responses in humans.

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YNIMG Journal 2009 Journal Article

Susceptibility contrast in high field MRI of human brain as a function of tissue iron content

  • Bing Yao
  • Tie-Qiang Li
  • Peter van Gelderen
  • Karin Shmueli
  • Jacco A. de Zwart
  • Jeff H. Duyn

Magnetic susceptibility provides an important contrast mechanism for MRI. Increasingly, susceptibility-based contrast is being exploited to investigate brain tissue microstructure and to detect abnormal levels of brain iron as these have been implicated in a variety of neuro-degenerative diseases. However, it remains unclear to what extent magnetic susceptibility-related contrast at high field relates to actual brain iron concentrations. In this study, we performed susceptibility weighted imaging as a function of field strength on healthy brains in vivo and post-mortem brain tissues at 1. 5 T, 3 T and 7 T. Iron histology was performed on the tissue samples for comparison. The calculated susceptibility-related parameters R2 ⁎ and signal frequency shift in four iron-rich regions (putamen, globus pallidus, caudate, and thalamus) showed an almost linear dependence (r ≥0. 90 for R2 ⁎; r ≥0. 83 for phase, p <0. 01) on field strength, suggesting that potential ferritin saturation effects are not relevant to susceptibility-weighted contrast for field strengths up to 7 T. The R2 ⁎ dependence on the putative (literature-based) iron concentration was 0. 048 Hz/T/ppm. The histological data from brain samples confirmed the linear dependence of R2 ⁎ on field strength and showed a slope against iron concentration of 0. 0099 Hz/T/ppm dry-weight, which is equivalent to 0. 05 Hz/T/ppm wet-weight and closely matched the calculated value in vivo. These results confirm the validity of using susceptibility-weighted contrast as an indicator of iron content in iron-rich brain regions. The absence of saturation effects opens the way to exploit the benefits of MRI at high field strengths for the detection of iron distributions with high sensitivity and resolution.

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YNIMG Journal 2006 Journal Article

Extensive heterogeneity in white matter intensity in high-resolution T2*-weighted MRI of the human brain at 7.0 T

  • Tie-Qiang Li
  • Peter van Gelderen
  • Hellmut Merkle
  • Lalith Talagala
  • Alan P. Koretsky
  • Jeff Duyn

MRI at high magnetic field strength potentially allows for an increase in resolution and image contrast. The gains are particularly dramatic for T2 *-weighted imaging, which is sensitive to susceptibility effects caused by a variety of sources, including deoxyhemoglobin, iron concentration, and tissue microstructure. On the other hand, the acquisition of high quality whole brain MRI at high field is hampered by the increased inhomogeneity in Bo and B1 fields. In this report, high-resolution gradient echo MRI was performed using an 8-channel detector to obtain T2 *-weighted images over large brain areas. The high SNR achieved with the multi-channel array enabled T2 *-weighted images of the brain with an unprecedented spatial resolution of up to 0. 2×0. 2×0. 5 mm3. This high resolution greatly facilitated the detection of microscopic susceptibility effects. In addition to the expected contrast between gray, white matter, cerebral spinal fluid, and veins, a large degree of heterogeneity in contrast was observed throughout the white matter of normal brain. The measured T2 * values in white matter varied as much as 30% with some of the variation apparently correlating with the presence of large fiber bundles.

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