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Jun Pan

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2 papers

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YNIMG Journal 2026 Journal Article

Multidimensional characterization of structure aberrations for biotypes of major depressive disorder

Jiang Zhang
Heng Zhang
Hui Sun
Tianwei Qin
Jun Pan
Jin Chen
Wei Li
Meiling Chen

BACKGROUND: Major depressive disorder (MDD) is a heterogeneous clinical syndrome associated with brain structural abnormalities, yet the neurobiological heterogeneity and consistent neuroimaging findings underlying these alterations remain unclear. Multilevel and multidimensional analyses are therefore needed to identify reliable structural signatures of MDD biotypes. METHODS: K-means clustering was applied to identify biotypes in 387 drug-naive MDD patients, with gray matter volume (GMV) compared to 1104 healthy controls. Causal structural covariance network (CaSCN), individual differential structural covariance network (IDSCN), and graph theory-based single-subject morphological network analyses were performed to characterize subtype-specific causal influences, individual-level covariance, and network topology. Transcriptomic and neurotransmitter association analyses were further conducted to probe the biological mechanisms underlying each subtype. RESULTS: Subtype 1 showed predominant GMV alterations in the visual network, subtype 2 in somatomotor, default mode, and limbic networks, and subtype 3 in cerebellar-limbic regions. CaSCN revealed subtype-specific directed influences, indicating differential propagation of structural abnormalities. IDSCN identified distinct altered covariance patterns, highlighting subtype-dependent thalamo-cerebellar changes and selective links to depressive severity. Graph theory showed divergent global topology, with subtype 1 exhibiting higher network integration, whereas subtypes 2 and 3 showed reduced integration and efficiency. Each biotype showed distinct neurobiological profiles, with subtype 1 enriched in cellular functions, subtype 2 in metabolic regulation, and subtype 3 in neurodevelopmental genes, alongside distinct neurotransmitter associations. CONCLUSIONS: These findings advance the understanding of structural and individual-level network alterations underlying MDD biotypes and provide novel insights into the neurobiological mechanisms of MDD heterogeneity.

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AAAI Conference 2023 Conference Paper

PASS: Patch Automatic Skip Scheme for Efficient Real-Time Video Perception on Edge Devices

Qihua Zhou
Song Guo
Jun Pan
Jiacheng Liang
Zhenda Xu
Jingren Zhou

Real-time video perception tasks are often challenging over the resource-constrained edge devices due to the concerns of accuracy drop and hardware overhead, where saving computations is the key to performance improvement. Existing methods either rely on domain-specific neural chips or priorly searched models, which require specialized optimization according to different task properties. In this work, we propose a general and task-independent Patch Automatic Skip Scheme (PASS), a novel end-to-end learning pipeline to support diverse video perception settings by decoupling acceleration and tasks. The gist is to capture the temporal similarity across video frames and skip the redundant computations at patch level, where the patch is a non-overlapping square block in visual. PASS equips each convolution layer with a learnable gate to selectively determine which patches could be safely skipped without degrading model accuracy. As to each layer, a desired gate needs to make flexible skip decisions based on intermediate features without any annotations, which cannot be achieved by conventional supervised learning paradigm. To address this challenge, we are the first to construct a tough self-supervisory procedure for optimizing these gates, which learns to extract contrastive representation, i.e., distinguishing similarity and difference, from frame sequence. These high-capacity gates can serve as a plug-and-play module for convolutional neural network (CNN) backbones to implement patch-skippable architectures, and automatically generate proper skip strategy to accelerate different video-based downstream tasks, e.g., outperforming the state-of-the-art MobileHumanPose (MHP) in 3D pose estimation and FairMOT in multiple object tracking, by up to 9.43 times and 12.19 times speedups, respectively. By directly processing the raw data of frames, PASS can generalize to real-time video streams on commodity edge devices, e.g., NVIDIA Jetson Nano, with efficient performance in realistic deployment.

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