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Ming Song

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9 papers

1 author row

EAAI Journal 2025 Journal Article

Enhanced pediatric pneumonia auxiliary diagnosis: Integrating optical fiber vibration sensing with machine learning

Pengfei Cao
Jiawei Xu
Yifan Zhao
Qian Ni
Yuxia Li
Hansen Chen
Ming Song
Jiqiang Shang

Childhood pneumonia remains a primary cause of death in children under five. Early detection is arduous due to its inconspicuous symptoms, and the existing radiological diagnostic techniques carry the risk of radiation - induced harm to young patients. To overcome these limitations, a groundbreaking study has proposed an innovative non - invasive diagnostic approach that integrates fiber optic vibration sensing technology with machine learning algorithms for pediatric pneumonia diagnosis. A novel fiber optic sensor is engineered to precisely capture respiratory vibration signals (RVS). These signals are then processed and analyzed using a Stacked - Grid Search Ensemble Learning Model (SGELM). In the experiment, respiratory vibration signals were gathered from 1649 pediatric patients aged between 3 and 14 who suffered from respiratory diseases. Through data balancing techniques, the dataset was expanded to 2184 samples. This dataset was partitioned into training, testing, and validation subsets. The developed system exhibited remarkable performance on the test dataset. It achieved high levels of accuracy, sensitivity, and specificity. Notably, it was also capable of classifying different pneumonia pathological types and statuses. This innovative method not only mitigates the radiation - related risks associated with traditional diagnostic methods but also holds great promise in revolutionizing the diagnosis of pediatric respiratory diseases. It could potentially improve the early - diagnosis rate and contribute to better treatment outcomes for children with pneumonia, thus playing a significant role in enhancing child health globally.

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YNIMG Journal 2024 Journal Article

Neuro-immune communication at the core of craving-associated brain structural network reconfiguration in methamphetamine users

Yanyao Du
Jiaqi Zhang
Dan Cao
Wenhan Yang
Jin Li
Deying Li
Ming Song
Zhengyi Yang

Methamphetamine (MA) use disorder is a chronic neurotoxic brain disease characterized by a high risk of relapse driven by intense cravings. However, the neurobiological signatures of cravings remain unclear, limiting the effectiveness of various treatment methods. Diffusion MRI (dMRI) scans from 62 MA users and 57 healthy controls (HC) were used in this study. The MA users were longitudinally followed up during their period of long-term abstinence (duration of long-term abstinence: 347. 52±99. 25 days). We systematically quantified the control ability of each brain region for craving-associated state transitions using network control theory from a causal perspective. Craving-associated structural alterations (CSA) were investigated through multivariate group comparisons and biological relevance analysis. The neural mechanisms underlying CSA were elucidated using transcriptomic and neurochemical analyses. We observed that long-term abstinence-induced structural alterations significantly influenced the state transition energy involved in the cognitive control response to external information, which correlated with changes in craving scores (r ∼ 0. 35, P <0. 01). Our causal network analysis further supported the crucial role of the prefrontal cortex (PFC) in craving mechanisms. Notably, while the PFC is central to the craving, the CSAs were distributed widely across multiple brain regions (PFDR <0. 05), with strong alterations in somatomotor regions (PFDR <0. 05) and moderate alterations in high-level association networks (PFDR <0. 05). Additionally, transcriptomic, chemical compounds, cell-type analyses, and molecular imaging collectively highlight the influence of neuro-immune communication on human craving modulation. Our results offer an integrative, multi-scale perspective on unraveling the neural underpinnings of craving and suggest that neuro-immune signaling may be a promising target for future human addiction therapeutics.

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YNICL Journal 2021 Journal Article

Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score

Ke Hu
Meng Wang
Yong Liu
Hao Yan
Ming Song
Jun Chen
Yunchun Chen
Huaning Wang

Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.

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YNIMG Journal 2018 Journal Article

Connectome-based individualized prediction of temperament trait scores

Rongtao Jiang
Vince D. Calhoun
Nianming Zuo
Dongdong Lin
Jin Li
Lingzhong Fan
Shile Qi
Hailun Sun

Temperament consists of multi-dimensional traits that affect various domains of human life. Evidence has shown functional connectome-based predictive models are powerful predictors of cognitive abilities. Putatively, individuals' innate temperament traits may be predictable by unique patterns of brain functional connectivity (FC) as well. However, quantitative prediction for multiple temperament traits at the individual level has not yet been studied. Therefore, we were motivated to realize the individualized prediction of four temperament traits (novelty seeking [NS], harm avoidance [HA], reward dependence [RD] and persistence [PS]) using whole-brain FC. Specifically, a multivariate prediction framework integrating feature selection and sparse regression was applied to resting-state fMRI data from 360 college students, resulting in 4 connectome-based predictive models that enabled prediction of temperament scores for unseen subjects in cross-validation. More importantly, predictive models for HA and NS could be successfully generalized to two relevant personality traits for unseen individuals, i. e. , neuroticism and extraversion, in an independent dataset. In four temperament trait predictions, brain connectivities that show top contributing power commonly concentrated on the hippocampus, prefrontal cortex, basal ganglia, amygdala, and cingulate gyrus. Finally, across independent datasets and multiple traits, we show person's temperament traits can be reliably predicted using functional connectivity strength within frontal-subcortical circuits, indicating that human social and behavioral performance can be characterized by specific brain connectivity profile.

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YNICL Journal 2018 Journal Article

Subdivisions of the posteromedial cortex in disorders of consciousness

Yue Cui
Ming Song
Darren M. Lipnicki
Yi Yang
Chuyang Ye
Lingzhong Fan
Jing Sui
Tianzi Jiang

Evidence suggests that disruptions of the posteromedial cortex (PMC) and posteromedial corticothalamic connectivity contribute to disorders of consciousness (DOCs). While most previous studies treated the PMC as a whole, this structure is functionally heterogeneous. The present study investigated whether particular subdivisions of the PMC are specifically associated with DOCs. Participants were DOC patients, 21 vegetative state/unresponsive wakefulness syndrome (VS/UWS), 12 minimally conscious state (MCS), and 29 healthy controls. Individual PMC and thalamus were divided into distinct subdivisions by their fiber tractograpy to each other and default mode regions, and white matter integrity and brain activity between/within subdivisions were assessed. The thalamus was represented mainly in the dorsal and posterior portions of the PMC, and the white matter tracts connecting these subdivisions to the thalamus had less integrity in VS/UWS patients than in MCS patients and healthy controls. In addition, these tracts had less integrity in DOC patients who did not recover after 12 months than in patients who did. The structural substrates were validated by resting state fMRI finding impaired functional activity within these PMC subdivisions. This study is the first to show that tracts from dorsal and posterior subdivisions of the PMC to the thalamus contribute to DOCs.

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YNIMG Journal 2017 Journal Article

Cross-cultural consistency and diversity in intrinsic functional organization of Broca's Region

Yu Zhang
Lingzhong Fan
Svenja Caspers
Stefan Heim
Ming Song
Cirong Liu
Yin Mo
Simon B. Eickhoff

As a core language area, Broca's region was consistently activated in a variety of language studies even across different language systems. Moreover, a high degree of structural and functional heterogeneity in Broca's region has been reported in many studies. This raised the issue of how the intrinsic organization of Broca's region effects by different language experiences in light of its subdivisions. To address this question, we used multi-center resting-state fMRI data to explore the cross-cultural consistency and diversity of Broca's region in terms of its subdivisions, connectivity patterns and modularity organization in Chinese and German speakers. A consistent topological organization of the 13 subdivisions within the extended Broca's region was revealed on the basis of a new in-vivo parcellation map, which corresponded well to the previously reported receptorarchitectonic map. Based on this parcellation map, consistent functional connectivity patterns and modularity organization of these subdivisions were found. Some cultural difference in the functional connectivity patterns was also found, for instance stronger connectivity in Chinese subjects between area 6v2 and the motor hand area, as well as higher correlations between area 45p and middle frontal gyrus. Our study suggests that a generally invariant organization of Broca's region, together with certain regulations of different language experiences on functional connectivity, might exists to support language processing in human brain.

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YNIMG Journal 2010 Journal Article

Haplotypes of catechol-O-methyltransferase modulate intelligence-related brain white matter integrity

Bing Liu
Jun Li
Chunshui Yu
Yonghui Li
Yong Liu
Ming Song
Ming Fan
Kuncheng Li

Twin studies have indicated a common genetic origin for intelligence and for variations in brain morphology. Our previous diffusion tensor imaging studies found an association between intelligence and white matter integrity of specific brain regions or tracts. However, specific genetic determinants of the white matter integrity of these brain regions and tracts are still unclear. In this study, we assess whether and how catechol-O-methyltransferase (COMT) gene polymorphisms affect brain white matter integrity. We genotyped twelve single nucleotide polymorphisms (SNPs) within the COMT gene and performed haplotype analyses on data from 79 healthy subjects. Our subjects had the same three major COMT haplotypes (termed the HPS, APS and LPS haplotypes) as previous studies have reported as regulating significantly different levels of enzymatic activity and dopamine. We used the mean fractional anisotropy (FA) values from four regions and five tracts of interest to assess the effect of COMT polymorphisms, including the well-studied val158met SNP and the three main haplotypes that we had identified, on intelligence-related white matter integrity. We identified an association between the mean FA values of two regions in the bilateral prefrontal lobes and the COMT haplotypes, rather than between them and val158met. The haplotype-FA value associations modulated nonlinearly and fit an inverted U-model. Our findings suggest that COMT haplotypes can nonlinearly modulate the intelligence-related white matter integrity of the prefrontal lobes by more significantly influencing prefrontal dopamine variations than does val158met.

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YNIMG Journal 2008 Journal Article

Brain spontaneous functional connectivity and intelligence

Ming Song
Yuan Zhou
Jun Li
Yong Liu
Lixia Tian
Chunshui Yu
Tianzi Jiang

Many functional imaging studies have been performed to explore the neural basis of intelligence by detecting brain activity changes induced by intelligence-related tasks, such as reasoning or working memory. However, little is known about whether the spontaneous brain activity at rest is relevant to the differences in intelligence. Here, 59 healthy adult subjects (Wechsler Adult Intelligence Scale score, 90–138) were studied with resting state fMRI. We took the bilateral dorsolateral prefrontal cortices (DLPFC) as the seed regions and investigated the correlations across subjects between individual intelligence scores and the strength of the functional connectivity (FC) between the seed regions and other brain regions. We found that the brain regions in which the strength of the FC significantly correlated with intelligence scores were distributed in the frontal, parietal, occipital and limbic lobes. Stepwise linear regression analysis also revealed that the FCs within the frontal lobe and between the frontal and posterior brain regions were both important predictive factors for the differences in intelligence. These findings support a network view of intelligence, as suggested in previous studies. More importantly, our findings suggest that brain activity may be relevant to the differences in intelligence even in the resting state and in the absence of an explicit cognitive demand. This could provide a new perspective for understanding the neural basis of intelligence.

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YNIMG Journal 2007 Journal Article

The relationship within and between the extrinsic and intrinsic systems indicated by resting state correlational patterns of sensory cortices

Lixia Tian
Tianzi Jiang
Yong Liu
Chunshui Yu
Kun Wang
Yuan Zhou
Ming Song
Kuncheng Li

Much Research has been done on extrinsic and intrinsic systems, which consist of brain regions associated with the processing of externally and internally oriented stimuli, respectively. However, understanding of the underlying relationships within and between these two systems is relatively limited. To improve our understanding of these underlying relationships, we investigated the positive and negative correlations of three regions of interest (ROIs) located in the auditory, visual and somatosensory systems by using resting state functional MRI (fMRI) with a large sample size. We found that all three sensory systems exhibited significant negative correlation with the intrinsic system. In contrast, positive correlations between these sensory cortices and brain regions outside their respective system were limited. The present study extended former findings by indicating that multiple subsystems rather than a single subsystem of the extrinsic system are inherently negatively correlated with the intrinsic system. We suggest that these negative correlations between the extrinsic and intrinsic systems may explain the phenomenon that externally and internally oriented processes can always disturb or even interrupt each other.

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