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Aiping Liu

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18 papers

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JBHI Journal 2026 Journal Article

Generalizable Seizure Prediction With LLMs: Converting EEG to Textual Representations

Yuchang Zhao
Aiping Liu
Chang Li
Lanlan Wang
Ruobing Qian
Xun Chen

Seizure prediction through scalp electroencephalogram (EEG) holds considerable practical potential. The primary challenge faced by existing algorithms lies in the individual heterogeneity, which hinders the generalizability of models to new patients. Additionally, inconsistencies in channel settings across various epilepsy centers further limit the applicability of models to diverse datasets. To address these challenges, we incorporate large language models (LLMs) into EEG analysis and propose a novel seizure prediction method based on LLMs (SPLLM), significantly enhancing both model generalizability and applicability. Specifically, this approach reprograms LLMs by transforming EEG signals into textual representations compatible with LLMs via a single-channel pre-training strategy. The method integrates cross-domain knowledge from both text and EEG data through a cross-attention mechanism, utilizing autoregressive pretrained LLMs to capture the temporal dependencies inherent in EEG signals. Moreover, the cross-domain generalization ability of LLMs alleviates patient heterogeneity, while the single-channel pre-training strategy enables the model to adapt to diverse channel settings. On two public datasets and one private dataset, SPLLM increases the average AUC by 8. 2%, and the average balanced accuracy by 8. 4% compared to existing methods. Experimental results demonstrate that the proposed method not only enhances cross-patient prediction accuracy but also adapts to data from different datasets, offering a scalable solution for the clinical application of seizure prediction.

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JBHI Journal 2026 Journal Article

Prior-Guided Selective Parameter Fine-Tuning for Source-Free Domain Adaptive Medical Image Segmentation

Fanzhe Yan
Gang Yang
Xun Chen
Yue Yu
Aiping Liu

Source-free domain adaptation (SFDA) transfers knowledge from pre-trained source models to the un labeled target domain without accessing the private source data. Conventional SFDA methods for medical imageseg mentation typically depend on pseudo-label driven self training with full model fine-tuning. Although these methods have shown decent performance, the underlying principles remain insufficiently explored. In this work, we investigate SFDA through the PAC-Bayesian generalization error bound, demonstrating that its generalization error is jointly constrained by model complexity and pseudo-label noise. Motivated by this, we propose PATH, a selective PArameter fine-tuning framework guided by Topological and Historical priors for SFDA medical image segmentation. Specifically, PATH identifies domain-variant and task-distinctive parameters and sparsely updates them, thereby reducing effective model complexity during adaptation. In addition, PATH estimates pseudo-label reliability by integrating topological structure and historical prediction consistency priors to suppress pseudo-label noise. Extensive experiments on cross-scanner fundus image segmentation and cross modality abdominal multi-organ segmentation benchmarks demonstrate that PATH outperforms competing SFDA methods, achieving state-of-the-art performance. Code will be available at https://github.com/dogeONE-bit/PATH.

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JBHI Journal 2025 Journal Article

A Flexible Spatio-Temporal Architecture Design for Artifact Removal in EEG With Arbitrary Channel-Settings

Yilin Han
Aiping Liu
Heng Cui
Xun Chen

Electroencephalography (EEG) data is easily contaminated by various sources, significantly affecting subsequent analyses in neuroscience and clinical applications. Therefore, effective artifact removal is a key step in EEG preprocessing. While current deep learning methods have demonstrated notable efficacy in EEG denoising, single-channel approaches primarily focus on temporal features and neglect inter-channel correlations. Meanwhile, multi-channel methods mainly prioritize spatial features but often overlook the unique temporal dependencies of individual channels. A common limitation of both single-channel and multi-channel methods is their strict requirements on the input channel setting, which restricts their practical applicability. To address these issues, we design a flexible architecture named Artifact removal Spatio-Temporal Integration Network (ASTI-Net), a dual-branch denoising model capable of handling arbitrary EEG channel settings. ASTI-Net utilizes spatio-temporal attention weighting with dual branches that capture inter-channel spatial characteristics and intra-channel temporal dependencies. Its architecture incorporates deformable convolutional operations and channel-wise temporal processing, accommodating varying numbers of EEG channels and enhancing applicability across diverse clinical and research settings. By integrating features from both branches through a fusion reconstruction module, ASTI-Net effectively restores clean multi-channel EEG. Extensive evaluation on two semi-simulated datasets, along with qualitative assessment on real task-state EEG data, validates that ASTI-Net outperforms existing artifact removal methods.

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JBHI Journal 2025 Journal Article

A GAN Guided Parallel CNN and Transformer Network for EEG Denoising

Jin Yin
Aiping Liu
Chang Li
Ruobing Qian
Xun Chen

Electroencephalography (EEG) signals are often contaminated with various physiological artifacts, seriously affecting the quality of subsequent analysis. Therefore, removing artifacts is an essential step in practice. As of now, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods. However, they still suffer from the following limitations. The existing structure designs have not fully taken into account the temporal characteristics of artifacts. Meanwhile, the existing training strategies usually ignore the holistic consistency between denoised EEG signals and authentic clean ones. To address these issues, we propose a GAN guided parallel CNN and transformer network, named GCTNet. The generator contains parallel CNN blocks and transformer blocks to respectively capture local and global temporal dependencies. Then, a discriminator is employed to detect and correct the holistic inconsistencies between clean and denoised EEG signals. We evaluate the proposed network on both semi-simulated and real data. Extensive experimental results demonstrate that GCTNet significantly outperforms state-of-the-art networks in various artifact removal tasks, as evidenced by its superior objective evaluation metrics. For example, in the task of removing electromyography artifacts, GCTNet achieves 11. 15% reduction in RRMSE and 9. 81% improvement in SNR over other methods, highlighting the potential of the proposed method as a promising solution for EEG signals in practical applications.

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AAAI Conference 2025 Conference Paper

A Lottery Ticket Hypothesis Approach with Sparse Fine-tuning and MAE for Image Forgery Detection and Localization

Jiaying Zhu
Dong Li
Xueyang Fu
Gege Shi
Jie Xiao
Aiping Liu
Zheng-Jun Zha

The rise in sophisticated image forgery techniques, driven by advancements in image editing and generation, has posed new security challenges. Traditional methods, designed for specific tampering artifacts, struggle with out-of-distribution image forgery detection. In this paper, we propose a shift in paradigm, placing greater emphasis on the universal characteristics of authentic images, as opposed to solely focusing on specific forgery signals. We introduce an enhancement to the Masked Autoencoder (MAE), aptly termed the Forgery MAE (FMAE). This modification retains the inherent characteristics of natural images while integrating multi-source forgery information. Our implementation involves applying the lottery ticket hypothesis during pre-training to identify forgery-sensitive parameters, followed by their sparse fine-tuning to target the forgery detection and localization task. Concurrently, we develop a ``mixture of experts'' noise extractor to compile multi-source forgery data. Our FMAE effectively extracts forgery features and shows strong resilience against unseen forgeries. Extensive experiments across multiple datasets confirm our method's superior accuracy and generalization capability over existing techniques.

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JBHI Journal 2025 Journal Article

EEGDfus: A Conditional Diffusion Model for Fine-Grained EEG Denoising

Xiaoyang Huang
Chang Li
Aiping Liu
Ruobing Qian
Xun Chen

Electroencephalogram (EEG) signals are vital in understanding brain activity, but their weak amplitude makes them susceptible to various artifacts. Accurate denoising of EEG data is crucial as a preprocessing step to ensure precise analysis and interpretation. In recent years, the diffusion model has garnered significant attention as a promising approach in generative modeling. This model effectively addresses the issue of over-smoothing in existing deep learning methods and thus has the potential to generate more refined denoised EEG signals. However, the generation process of the standard diffusion model is highly random, limiting its direct application to EEG denoising tasks. To address this limitation, we propose a conditional diffusion model specifically designed for EEG denoising. In this model, the standard diffusion model's denoising network is replaced by a novel dual-branch network, where noisy EEG information is used as a condition to guide the generation of corresponding clean EEG signals. This dual-branch structure leverages the complementary strengths of convolutional neural network (CNN) and Transformer architectures, integrating multi-scale features to comprehensively extract information from the signal. Extensive experiments demonstrate the remarkable performance of EEGDfus in EEG denoising. We tested it on two public datasets. Testing on two public datasets, EEGdenoiseNet and SSED, demonstrated that after denoising, the average correlation coefficient increased to 0. 983 and 0. 992 for EOG artifact removal, respectively. The proposed model outperforms commonly used baseline models, setting a new state-of-the-art benchmark in the field of EEG denoising.

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IJCAI Conference 2025 Conference Paper

Learnable Frequency Decomposition for Image Forgery Detection and Localization

Dong Li
Jiayíng Zhu
Yidi Liu
Xin Lu
Xueyang Fu
Jiawei Liu
Aiping Liu
Zheng-Jun Zha

Concern for image authenticity spurs research in image forgery detection and localization (IFDL). Most deep learning-based methods focus primarily on spatial domain modeling and have not fully explored frequency domain strategies. In this paper, we observe and analyze the frequency characteristic changes caused by image tampering. Observations indicate that manipulation traces are especially prominent in phase components and span both low and high-frequency bands. Based on these findings, we propose a forensic frequency decomposition network (F2D-Net), which incorporates deep Fourier transforms and leverages both phase information and high and low-frequency components to enhance IFDL. Specifically, F2D-Net consists of the Spectral Decomposition Subnetwork (SDSN) and the Frequency Separation Subnetwork (FSSN). The former decomposes the image into amplitude and phase, focusing on learning the semantic content in the phase spectrum to identify forged objects, thus improving forgery detection accuracy. The latter further adaptively decomposes the output of the SDSN to obtain corresponding high and low frequencies, and applies a divide-and-conquer strategy to refine each frequency band, mitigating the optimization difficulties caused by coupled forgery traces across different frequencies, thereby better capturing the pixels belonging to the forged object to improve localization accuracy. Experiments on multiple datasets demonstrate that our method outperforms state-of-the-art image forgery detection and localization techniques both qualitatively and quantitatively.

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JBHI Journal 2025 Journal Article

Task-Aware Effective Connectivity Modeling for Cognitive Function Prediction

Wantong Zou
Yu Li
Xiang Hu
Xun Chen
Aiping Liu

Effective connectivity (EC) derived from resting-state Functional Magnetic Resonance Imaging (rs fMRI) has emerged as a critical tool for deepening our understanding of brain function in both health and dis ease. However, most studies estimate EC on an individual basis, treating it as a hidden parameter within the model and requiring retraining the model for each subject. They often overlook the valuable population-level information and limit their generalizability. Additionally, EC is typically obtained independently of downstream tasks, reducing its capacity to effectively capture task-specific variations. To address these limitations, we propose a flexible Task-Aware Effective Connectivity (TAEC) model, designed to construct individualized, task-aware, and nonlinear causal brain networks without requiring subject-specific retraining. In this framework, a Causal Discovery Module (CDM) is introduced to capture the implicit neural representation of the EC by a spatial-temporal attention mechanism, producing the estimation of an individual EC. Subsequently, we propose a Task-Aware Graph Neural Network (GNN) Predictor, which incorporates a task-aware penalty to enable end-to-end prediction, enhancing task performance and the identification of task-dependent EC patterns. Extensive experiments on twelve cognitive tasks from the Human Connectome Project (HCP) dataset demonstrate that the proposed method achieves state-of-the-art performance, validating its effectiveness in task-aware effective connectivity modeling. Furthermore, the framework discovers discriminative and task-specific EC patterns, which offer additional in-sights into cognitive functions.

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AAAI Conference 2024 Conference Paper

Learning Discriminative Noise Guidance for Image Forgery Detection and Localization

Jiaying Zhu
Dong Li
Xueyang Fu
Gang Yang
Jie Huang
Aiping Liu
Zheng-Jun Zha

This study introduces a new method for detecting and localizing image forgery by focusing on manipulation traces within the noise domain. We posit that nearly invisible noise in RGB images carries tampering traces, useful for distinguishing and locating forgeries. However, the advancement of tampering technology complicates the direct application of noise for forgery detection, as the noise inconsistency between forged and authentic regions is not fully exploited. To tackle this, we develop a two-step discriminative noise-guided approach to explicitly enhance the representation and use of noise inconsistencies, thereby fully exploiting noise information to improve the accuracy and robustness of forgery detection. Specifically, we first enhance the noise discriminability of forged regions compared to authentic ones using a de-noising network and a statistics-based constraint. Then, we merge a model-driven guided filtering mechanism with a data-driven attention mechanism to create a learnable and differentiable noise-guided filter. This sophisticated filter allows us to maintain the edges of forged regions learned from the noise. Comprehensive experiments on multiple datasets demonstrate that our method can reliably detect and localize forgeries, surpassing existing state-of-the-art methods.

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AAAI Conference 2024 Conference Paper

TMFormer: Token Merging Transformer for Brain Tumor Segmentation with Missing Modalities

Zheyu Zhang
Gang Yang
Yueyi Zhang
Huanjing Yue
Aiping Liu
Yunwei Ou
Jian Gong
Xiaoyan Sun

Numerous techniques excel in brain tumor segmentation using multi-modal magnetic resonance imaging (MRI) sequences, delivering exceptional results. However, the prevalent absence of modalities in clinical scenarios hampers performance. Current approaches frequently resort to zero maps as substitutes for missing modalities, inadvertently introducing feature bias and redundant computations. To address these issues, we present the Token Merging transFormer (TMFormer) for robust brain tumor segmentation with missing modalities. TMFormer tackles these challenges by extracting and merging accessible modalities into more compact token sequences. The architecture comprises two core components: the Uni-modal Token Merging Block (UMB) and the Multi-modal Token Merging Block (MMB). The UMB enhances individual modality representation by adaptively consolidating spatially redundant tokens within and outside tumor-related regions, thereby refining token sequences for augmented representational capacity. Meanwhile, the MMB mitigates multi-modal feature fusion bias, exclusively leveraging tokens from present modalities and merging them into a unified multi-modal representation to accommodate varying modality combinations. Extensive experimental results on the BraTS 2018 and 2020 datasets demonstrate the superiority and efficacy of TMFormer compared to state-of-the-art methods when dealing with missing modalities.

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JBHI Journal 2022 Journal Article

A Joint Constrained CCA Model for Network-Dependent Brain Subregion Parcellation

Qinrui Ling
Aiping Liu
Yu Li
Xueyang Fu
Xun Chen
Martin J. McKeown
Feng Wu

Connectivity-based brain region parcellation from functional magnetic resonance imaging (fMRI) data is complicated by heterogeneity among aged and diseased subjects, particularly when the data are spatially transformed to a common space. Here, we propose a group-guided functional brain region parcellation model capable of obtaining subregions from a target region with consistent connectivity profiles across multiple subjects, even when the fMRI signals are kept in their native spaces. The model is based on a joint constrained canonical correlation analysis (JC-CCA) method that achieves group-guided parcellation while allowing the data dimension of the parcellated regions for each subject to vary. We performed extensive experiments on synthetic and real data to demonstrate the superiority of the proposed model compared to other classical methods. When applied to fMRI data of subjects with and without Parkinson's disease (PD) to estimate the subregions in the Putamen, significant between-group differences were found in the derived subregions and the connectivity patterns. Superior classification and regression results were obtained, demonstrating its potential in clinical practice.

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AAAI Conference 2022 Conference Paper

RETRACTEPan-Sharpening with Customized Transformer and Invertible Neural Network

Man Zhou
Jie Huang
Yanchi Fang
Xueyang Fu
Aiping Liu

In remote sensing imaging systems, pan-sharpening is an important technique to obtain high-resolution multispectral images from a high-resolution panchromatic image and its corresponding low-resolution multispectral image. Owing to the powerful learning capability of convolution neural network (CNN), CNN-based methods have dominated this field. However, due to the limitation of the convolution operator, long-range spatial features are often not accurately obtained, thus limiting the overall performance. To this end, we propose a novel and effective method by exploiting a customized transformer architecture and information-lossless invertible neural module for long-range dependencies modeling and effective feature fusion in this paper. Specifically, the customized transformer formulates the PAN and MS features as queries and keys to encourage joint feature learning across two modalities while the designed invertible neural module enables effective feature fusion to generate the expected pan-sharpened results. To the best of our knowledge, this is the first attempt to introduce transformer and invertible neural network into pan-sharpening field. Extensive experiments over different kinds of satellite datasets demonstrate that our method outperforms state-of-the-art algorithms both visually and quantitatively with fewer parameters and flops. Further, the ablation experiments also prove the effectiveness of the proposed customized long-range transformer and effective invertible neural feature fusion module for pan-sharpening. Editorial Notes This article, which was published in Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), has been retracted by agreement between the authors and the journal.

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JBHI Journal 2021 Journal Article

Emotion Recognition From Multi-Channel EEG via Deep Forest

Juan Cheng
Meiyao Chen
Chang Li
Yu Liu
Rencheng Song
Aiping Liu
Xun Chen

Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks based on electroencephalography (EEG), and have achieved better performance than traditional algorithms. However, DNNs still have the disadvantages of too many hyperparameters and lots of training data. To overcome these shortcomings, in this article, we propose a method for multi-channel EEG-based emotion recognition using deep forest. First, we consider the effect of baseline signal to preprocess the raw artifact-eliminated EEG signal with baseline removal. Secondly, we construct 2$D$ frame sequences by taking the spatial position relationship across channels into account. Finally, 2$D$ frame sequences are input into the classification model constructed by deep forest that can mine the spatial and temporal information of EEG signals to classify EEG emotions. The proposed method can eliminate the need for feature extraction in traditional methods and the classification model is insensitive to hyperparameter settings, which greatly reduce the complexity of emotion recognition. To verify the feasibility of the proposed model, experiments were conducted on two public DEAP and DREAMER databases. On the DEAP database, the average accuracies reach to 97. 69% and 97. 53% for valence and arousal, respectively; on the DREAMER database, the average accuracies reach to 89. 03%, 90. 41%, and 89. 89% for valence, arousal and dominance, respectively. These results show that the proposed method exhibits higher accuracy than the state-of-art methods.

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JBHI Journal 2021 Journal Article

Striatal Subdivisions Estimated via Deep Embedded Clustering With Application to Parkinson's Disease

Yu Li
Aiping Liu
Taomian Mi
Runyu Yang
Piu Chan
Martin J. McKeown
Xun Chen
Feng Wu

Recent fMRI connectivity-based parcellation (CBP) methods have been developed to obtain homogeneous and functionally coherent brain parcels. However, most of these studies utilize traditional clustering methods that neglect hidden nonlinear features. To enhance parcellation performance, here we propose a deep embedded connectivity-based parcellation (DECBP) framework and apply it to determine functional subdivisions of the striatum in public resting state fMRI data sets. This framework integrates fMRI connectivity features into deep embedded clustering (DEC), a deep neural network based on a stacked autoencoder. Compared to three prevalent clustering methods and their combinations with principal component analysis (PCA), the DECBP exhibited a significantly higher similarity between scans, individuals, and groups, indicating enhanced reproducibility. The generated reliable parcellations were also largely consistent with other public atlases. We further explored the functional subunits in the striatum in a data set from 23 Parkinson's disease (PD) subjects and 27 age-matched healthy controls (HC). All putaminal subregions of PD demonstrated lower interhemispheric connectivity than those of HC, which might reflect imbalance in the pathological progression of PD. Such hypo-connectivity was also observed between putaminal subregions and other brain regions, reflecting neuroimaging manifestations of the altered cortico-striato-thalamo-cortical circuit. These observed weaker couplings were associated with PD severity and duration. Our results support the utilization of the DECBP framework and suggest that abnormal connectivity in putaminal subregions may be a potential indicator of PD.

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NeurIPS Conference 2021 Conference Paper

Unfolding Taylor's Approximations for Image Restoration

Man Zhou
Xueyang Fu
Zeyu Xiao
Gang Yang
Aiping Liu
Zhiwei Xiong

Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image. In practice, however, existing methods empirically construct encapsulated end-to-end mapping networks without deepening into the rationality, and neglect the intrinsic prior knowledge of restoration task. To solve the above problems, inspired by Taylor’s Approximations, we unfold Taylor’s Formula to construct a novel framework for image restoration. We find the main part and the derivative part of Taylor’s Approximations take the same effect as the two competing goals of high-level contextualized information and spatial details of image restoration respectively. Specifically, our framework consists of two steps, which are correspondingly responsible for the mapping and derivative functions. The former first learns the high-level contextualized information and the later combines it with the degraded input to progressively recover local high-order spatial details. Our proposed framework is orthogonal to existing methods and thus can be easily integrated with them for further improvement, and extensive experiments demonstrate the effectiveness and scalability of our proposed framework.

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AIIM Journal 2020 Journal Article

ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network

Jing Zhang
Aiping Liu
Min Gao
Xiang Chen
Xu Zhang
Xun Chen

Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods have been applied to arrhythmia detection and obtained great success. Among them, convolutional neural network (CNN) is an effective method for extracting features due to its local connectivity and parameter sharing. In addition, recurrent neural network (RNN) is another commonly used method, which is applied to process time-series signal. The stacking of both CNN and RNN has been proved to be more effective in multi-class arrhythmia detection. However, these networks ignored the fact that different channels and temporal segments of a feature map extracted from the 12-lead ECG signal contribute differently to cardiac arrhythmia detection, and thus, the classification performance could be greatly improved. To address this issue, spatio-temporal attention-based convolutional recurrent neural network (STA-CRNN) is proposed to focus on representative features along both spatial and temporal axes. STA-CRNN consists of CNN subnetwork, spatio-temporal attention modules and RNN subnetwork. The experiment result shows that, STA-CRNN reaches an average F 1 score of 0. 835 in classifying 8 types of arrhythmias and normal rhythm. Compared with the state-of-the-art methods based on the same public dataset, STA-CRNN achieves an obvious improvement on identifying most of arrhythmias. Also, it is demonstrated by visualization that the learned features through STA-CRNN are in line with clinical judgement. STA-CRNN provides a promising method for automatic arrhythmia detection, which has a potential to assist cardiologists in the diagnosis of arrhythmias.

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JBHI Journal 2019 Journal Article

Dynamic Graph Theoretical Analysis of Functional Connectivity in Parkinson's Disease: The Importance of Fiedler Value

Jiayue Cai
Aiping Liu
Taomian Mi
Saurabh Garg
Wade Trappe
Martin J. McKeown
Z. Jane Wang

Graph theoretical analysis is a powerful tool for quantitatively evaluating brain connectivity networks. Conventionally, brain connectivity is assumed to be temporally stationary, whereas increasing evidence suggests that functional connectivity exhibits temporal variations during dynamic brain activity. Although a number of methods have been developed to estimate time-dependent brain connectivity, there is a paucity of studies examining the utility of brain dynamics for assessing brain disease states. Therefore, this paper aims to assess brain connectivity dynamics in Parkinson's disease (PD) and determine the utility of such dynamic graph measures as potential components to an imaging biomarker. Resting-state functional magnetic resonance imaging data were collected from 29 healthy controls and 69 PD subjects. Time-varying functional connectivity was first estimated using a sliding windowed sparse inverse covariance matrix. Then, a collection of graph measures, including the Fiedler value, were computed and the dynamics of the graph measures were investigated. The results demonstrated that PD subjects had a lower variability in the Fiedler value, modularity, and global efficiency, indicating both abnormal dynamic global integration and local segregation of brain networks in PD. Autoregressive models fitted to the dynamic graph measures suggested that Fiedler value, characteristic path length, global efficiency, and modularity were all less deterministic in PD. With canonical correlation analysis, the altered dynamics of functional connectivity networks, and particularly dynamic Fiedler value, were shown to be related with disease severity and other clinical variables including age. Similarly, Fiedler value was the most important feature for classification. Collectively, our findings demonstrate altered dynamic graph properties, and in particular the Fiedler value, provide an additional dimension upon which to non-invasively and quantitatively assess PD.

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YNICL Journal 2018 Journal Article

Decreased subregional specificity of the putamen in Parkinson's Disease revealed by dynamic connectivity-derived parcellation

Aiping Liu
Sue-Jin Lin
Taomian Mi
Xun Chen
Piu Chan
Z. Jane Wang
Martin J. McKeown

Parkinson's Disease (PD) is associated with decreased ability to perform habitual tasks, relying instead on goal-directed behaviour subserved by different cortical/subcortical circuits, including parts of the putamen. We explored the functional subunits in the putamen in PD using novel dynamic connectivity features derived from resting state fMRI recorded from thirty PD subjects and twenty-eight age-matched healthy controls (HC). Dynamic functional segmentation of the putamina was obtained by determining the correlation between each voxel in each putamen along a moving window and applying a joint temporal clustering algorithm to establish cluster membership of each voxel at each window. Contiguous voxels that had consistent cluster membership across all windows were then considered to be part of a homogeneous functional subunit. As PD subjects robustly had two homogenous clusters in the putamina, we also segmented the putamina in HC into two dynamic clusters for a fair comparison. We then estimated the dynamic connectivity using sliding windowed correlation between the mean signal from the identified homogenous subunits and 56 other predefined cortical and subcortical ROIs. Specifically, the mean dynamic connectivity strength and connectivity deviation were then compared to evaluate subregional differences. HC subjects had significant differences in mean dynamic connectivity and connectivity deviation between the two putaminal subunits. The posterior subunit connected strongly to sensorimotor areas, the cerebellum, as well as the middle frontal gyrus. The anterior subunit had strong mean dynamic connectivity to the nucleus accumbens, hippocampus, amygdala, caudate and cingulate. In contrast, PD subjects had fewer differences in mean dynamic connectivity between subunits, indicating a degradation of subregional specificity. Overall UPDRS III and MoCA scores could be predicted using mean dynamic connectivity strength and connectivity deviation. Side of onset of the disease was also jointly related with functional connectivity features. Our results suggest a robust loss of specificity of mean dynamic connectivity and connectivity deviation in putaminal subunits in PD that is sensitive to disease severity. In addition, altered mean dynamic connectivity and connectivity deviation features in PD suggest that looking at connectivity dynamics offers an additional dimension for assessment of neurodegenerative disorders.

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