Nan Lin

YNIMG Journal 2025 Journal Article

A simple clustering approach to map the human brain's cortical semantic network organization during task

• Yunhao Zhang
• Shaonan Wang
• Nan Lin
• Lingzhong Fan
• Chengqing Zong

Constructing task-state large-scale brain networks can enhance our understanding of the organization of brain functions during cognitive tasks. The primary goal of brain network partitioning is to cluster functionally homogeneous brain regions. However, a brain region often serves multiple cognitive functions, complicating the partitioning process. This study proposes a novel clustering method for partitioning large-scale brain networks based on specific cognitive functions, selecting semantic representation as the target cognitive function to evaluate the validity of the proposed method. Specifically, we analyzed functional magnetic resonance imaging (fMRI) data from 11 subjects, each exposed to 672 concepts, and correlated this with semantic rating data related to these concepts. We identified distinct semantic networks based on the concept comprehension task and validated the robustness of our network partitioning through multiple methods. We found that the semantic networks derived from multidimensional semantic activation clustering exhibit high reliability and cross-semantic model consistency (semantic ratings and word embeddings extracted from GPT-2), particularly in networks associated with high semantic functions. Moreover, these semantic networks exhibits significant differences from the resting-state and task-based brain networks obtained using traditional methods. Further analysis revealed functional differences between semantic networks, including disparities in their multidimensional semantic representation capabilities, differences in the information modalities they rely on to acquire semantic information, and varying associations with general cognitive domains. This study introduces a novel approach for analyzing brain networks tailored to specific cognitive functions, establishing a standard semantic parcellation with seven networks for future research, potentially enriching our understanding of complex cognitive processes and their neural bases.

AAAI Conference 2022 Conference Paper

Probing Word Syntactic Representations in the Brain by a Feature Elimination Method

• Xiaohan Zhang
• Shaonan Wang
• Nan Lin
• Jiajun Zhang
• Chengqing Zong

Neuroimaging studies have identified multiple brain regions that are associated with semantic and syntactic processing when comprehending language. However, existing methods cannot explore the neural correlates of fine-grained word syntactic features, such as part-of-speech and dependency relations. This paper proposes an alternative framework to study how different word syntactic features are represented in the brain. To separate each syntactic feature, we propose a feature elimination method, called Mean Vector Null space Projection (MVNP). This method can remove a specific feature from word representations, resulting in one-feature-removed representations. Then we respectively associate one-featureremoved and the original word vectors with brain imaging data to explore how the brain represents the removed feature. This paper for the first time studies the cortical representations of multiple fine-grained syntactic features simultaneously and suggests some possible contributions of several brain regions to the complex division of syntactic processing. These findings indicate that the brain foundations of syntactic information processing might be broader than those suggested by classical studies.

JBHI Journal 2021 Journal Article

ML-Net: Multi-Channel Lightweight Network for Detecting Myocardial Infarction

• Yangjie Cao
• Tingting Wei
• Bo Zhang
• Nan Lin
• Joel J. P. C. Rodrigues
• Jie Li
• Di Zhang

Due to the complexity of myocardial infarction (MI) waveform, most traditional automatic diagnosis models rarely detect it, while those able to detect MI often require high computing and storage capacity, rendering them unsuitable for portable devices. Therefore, in order for convenient real-time MI detection, it is essential to design lightweight models suitable for resource-limited portable devices. This paper proposes a novel multi-channel lightweight model (ML-Net), that provides a new solution for portable detection devices with limited resources. In ML-Net, each electrocardiogram (ECG) lead is assigned an independent channel, ensuring data independence and preserve the ECG characteristics of different angles represented by different leads. Moreover, convolution kernels of heterogeneous sizes are utilized to achieve accurate classification with only a small amount of lead data. Extensive experiments over actual ECG data from the PTB diagnostic database are conducted to evaluate ML-Net. The results show that ML-Net outperforms comparable schemes in diagnosing MI, and it requires lower computational cost and less memory, so that portable devices can be more widely used in the field of Internet of Medical Things(IoMT).

AAAI Conference 2020 Conference Paper

Probing Brain Activation Patterns by Dissociating Semantics and Syntax in Sentences

• Shaonan Wang
• Jiajun Zhang
• Nan Lin
• Chengqing Zong

The relation between semantics and syntax and where they are represented in the neural level has been extensively debated in neurosciences. Existing methods use manually designed stimuli to distinguish semantic and syntactic information in a sentence that may not generalize beyond the experimental setting. This paper proposes an alternative framework to study the brain representation of semantics and syntax. Specifically, we embed the highly-controlled stimuli as objective functions in learning sentence representations and propose a disentangled feature representation model (DFRM) to extract semantic and syntactic information in sentences. This model can generate one semantic and one syntactic vector for each sentence. Then we associate these disentangled feature vectors with brain imaging data to explore brain representation of semantics and syntax. Results have shown that semantic feature is represented more robustly than syntactic feature across the brain including the default-mode, frontoparietal, visual networks, etc.. The brain representations of semantics and syntax are largely overlapped, but there are brain regions only sensitive to one of them. For instance, several frontal and temporal regions are specific to the semantic feature; parts of the right superior frontal and right inferior parietal gyrus are specific to the syntactic feature.

YNIMG Journal 2019 Journal Article

Uncovering cortical activations of discourse comprehension and their overlaps with common large-scale neural networks

• XiaoHong Yang
• Huijie Li
• Nan Lin
• XiuPing Zhang
• YinShan Wang
• Ying Zhang
• Qian Zhang
• XiNian Zuo

We conducted a meta-analysis of 78 task-based functional magnetic resonance imaging (fMRI) studies (1976 total participants) to reveal underlying brain activations and their overlap with large-scale neural networks in the brain during general discourse comprehension and its sub-processes. We found that discourse comprehension involved a neural system consisting of widely distributed brain regions that comprised not only the bilateral perisylvian language zones, but also regions in the superior and medial frontal cortex and the medial temporal lobe. Moreover, this neural system can be categorized into several sub-systems representing various sub-processes of discourse comprehension, with the left inferior frontal gyrus and middle temporal gyrus serving as core regions across all sub-processes. At a large-scale network level, we found that discourse comprehension relied most heavily on the default network, particularly on its dorsal medial subsystem. The pattern associated with large-scale network cooperation varied according to the respective sub-processes required. Our results reveal the functional dissociation within the discourse comprehension neural system and highlight the flexible involvements of large-scale networks.

AAAI Conference 2018 Conference Paper

Investigating Inner Properties of Multimodal Representation and Semantic Compositionality With Brain-Based Componential Semantics

• Shaonan Wang
• Jiajun Zhang
• Nan Lin
• Chengqing Zong

Multimodal models have been proven to outperform textbased approaches on learning semantic representations. However, it still remains unclear what properties are encoded in multimodal representations, in what aspects do they outperform the single-modality representations, and what happened in the process of semantic compositionality in different input modalities. Considering that multimodal models are originally motivated by human concept representations, we assume that correlating multimodal representations with brain-based semantics would interpret their inner properties to answer the above questions. To that end, we propose simple interpretation methods based on brain-based componential semantics. First we investigate the inner properties of multimodal representations by correlating them with corresponding brain-based property vectors. Then we map the distributed vector space to the interpretable brain-based componential space to explore the inner properties of semantic compositionality. Ultimately, the present paper sheds light on the fundamental questions of natural language understanding, such as how to represent the meaning of words and how to combine word meanings into larger units.

YNIMG Journal 2013 Journal Article

Selectivity for large nonmanipulable objects in scene-selective visual cortex does not require visual experience

• Chenxi He
• Marius V. Peelen
• Zaizhu Han
• Nan Lin
• Alfonso Caramazza
• Yanchao Bi

The principles that determine the organization of object representations in ventral temporal cortex (VTC) remain elusive. Here, we focus on the parahippocampal place area (PPA), a region in medial VTC that has been shown to respond selectively to pictures of scenes. Recent studies further observed that this region also shows a preference for large nonmanipulable objects relative to other objects, which might reflect the suitability of large objects for navigation. The mechanisms underlying this selectivity remain poorly understood. We examined the extent to which PPA selectivity requires visual experience. Fourteen congenitally blind and matched sighted participants were tested on an auditory size judgment experiment involving large nonmanipulable objects, small objects (tools), and animals. Sighted participants additionally participated in a picture-viewing experiment. Replicating previous work, we found that the PPA responded selectively to large nonmanipulable objects, relative to tools and animals, in the sighted group viewing pictures. Importantly, this selectivity was also observed in the auditory experiment in both sighted and congenitally blind groups. In both groups, selectivity for large nonmanipulable objects was additionally observed in the retrosplenial complex (RSC) and the transverse occipital sulcus (TOS), regions previously implicated in scene perception and navigation. Finally, in both groups the PPA showed resting-state functional connectivity with TOS and RSC. These results provide new evidence that large object selectivity in PPA, and the intrinsic connectivity between PPA and other navigation-relevant regions, do not require visual experience. More generally, they show that the organization of object representations in VTC can develop, at least partly, without visual experience.

YNIMG Journal 2011 Journal Article

Is the semantic category effect in the lateral temporal cortex due to motion property differences?

• Nan Lin
• Xueming Lu
• Fang Fang
• Zaizhu Han
• Yanchao Bi

Two specific areas within the posterior lateral temporal cortex (PLTC), the posterior superior temporal sulcus (pSTS) and the posterior middle temporal gyrus (pMTG), have been proposed to store different types of conceptual properties of motion: the pSTS encodes knowledge of articulated, biological motion, and the pMTG encodes knowledge about unarticulated, mechanical motion. We examined this hypothesis by comparing activation patterns evoked by verbs denoting biological motion (e. g. , walk), mechanical motion (e. g. , rotate), and low-motion events (e. g. , ferment). Classical noun categories with different motion types (animals, tools, and buildings) were also tested and compared with previous findings of the categorical effects in PLTC. Replicating previous findings of different types of nouns, we observed stronger activation for animals than tools in the pSTS and stronger activation for tools compared to other types of nouns in the pMTG. However, such motion-type specific activation patterns only partly extended to verbs. Whereas the pSTS showed preferences for biological-motion verbs, no region within the pMTG was sensitive to verbs denoting mechanical motion. We speculate that the pMTG preference for tools is driven by properties other than mechanical motion, such as strong mappings between the visual form and motor-related representations.

IS Journal 2002 Journal Article

Multidimensional data integration and relationship inference

• Kunbin Qu
• Nan Lin
• Yanmei Lu
• Donald Payan