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arXiv 2025年

作者： Gong, Zhu-Qing Zuo, Xi-Nian State Key Laboratory of Cognitive Neuroscience and Learning Faculty of Psychology Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing100875 China Department of Psychology University of Chinese Academy of Sciences No 19 Yuquan Road Shijingshan District Beijing100049 China Key Laboratory of Behavioural Sciences Institute of Psychology Chinese Academy of Sciences No 16 Lincui Road Chaoyang District Beijing100101 China National Basic Science Data Center No 2 Dongsheng South Road Haidian District Beijing100190 China Key Laboratory of Brain and Education School of Education Sciences Nanning Normal University No 175 Mingxiu East Road Mingxiu District Guangxi Nanning530001 China Research Center for Lifespan Development of Mind and Brain Institute of Psychology Chinese Academy of Sciences No 16 Lincui Road Chaoyang District Beijing100101 China Developmental Population Neuroscience Research Center IDG/McGovern Institute for Brain Research Beijing Normal University No 19 Xinjiekouwai Street Haidian District Beijing100875 China Engineering Center for Population Neuroimaging and Intellectual Technology Nanning Normal University No. 175 Mingxiu East Road Nanning530001 China

Neural oscillations facilitate the functioning of the human brain in spatial and temporal dimensions at various frequencies. These oscillations feature a universal frequency architecture that is governed by brain anatomy, ensuring frequency specificity remains invariant across diderent measurement techniques. Initial magnetic resonance imaging (MRI) methodology constrained functional MRI (fMRI) investigations to a singular frequency range, thereby neglecting the frequency characteristics inherent in blood oxygen level-dependent oscillations. With advancements in MRI technology, it has become feasible to decode intricate brain activities via multi-band frequency analysis (MBFA). During the past decade, the utilization of MBFA in fMRI studies has surged, unveiling frequency-dependent characteristics of spontaneous slow oscillations (SSOs) believed to base dark energy in the brain. There remains a dearth of conclusive insights and hypotheses pertaining to the properties and functionalities of SSOs in distinct bands. We surveyed the SSO MBFA studies during the past 15 years to delineate the attributes of SSOs and enlighten their correlated functions. We further proposed a model to elucidate the hierarchical organization of multi-band SSOs by integrating their function, aimed at bridging theoretical gaps and guiding future MBFA research endeavors. © 2025, CC BY-NC-SA.

关键词： Magnetic resonance imaging

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The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

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Health data science 2022年第1期2022卷 15-23页

作者： Senqi Zhang Li Sun Daiwei Zhang Pin Li Yue Liu Ajay Anand Zidian Xie Dongmei Li Goergen Institute for Data Science University of RochesterRochesterNew YorkUSA Department of Clinical&Translational Research University of Rochester Medical CenterRochesterNYUSA

*** the COVID-19 pandemic,mental health concerns(such as fear and loneliness)have been actively discussed on social *** aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health ***-19-related tweets from March 5th,2020,to January 31st,2021,were collected through Twitter streaming API using keywords(i.e.,“corona,”“covid19,”and“covid”).By further filtering using keywords(i.e.,“depress,”“failure,”and“hopeless”),we extracted mental health-related tweets from the *** modeling using the Latent Dirichlet Allocation model was conducted to monitor users’discussions surrounding mental health *** learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the *** observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the *** modeling showed that“stay-at-home,”“death poll,”and“politics and policy”were the most popular topics in COVID-19 mental health *** Twitter users who had mental health concerns during the pandemic,Males,White,and 30-49 age group people were more likely to express mental health *** addition,Twitter users from the east and west coast had more mental health *** COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the *** groups of people(such as Males and White)were more likely to have mental health concerns during the COVID-19 pandemic.

关键词： Twitter likely COV

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Multimodal Analysis Uncovers Links between Grey Matter Volume and both Low-and High-frequency Dynamic Connectivity States in Schizophrenia

Multimodal Analysis Uncovers Links between Grey Matter Volum...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Marlena Duda Ashkan Faghiri Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta GA USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Multimodal neuroimaging fusion studies of complex disorders like schizophrenia (SZ) have revealed disease-specific links between brain structure and function that unimodal analyses alone could not produce. Here, we utilize a multi-set CCA+ joint ICA fusion framework to study the connection between structural MRI and functional network connectivity (FNC) states derived via filter-banked connectivity (FBC), a unified method for estimating both static and dynamic FNC across the frequency spectrum. This joint analysis showed functional connections in both low-and high-frequency SZ-dominant states were significantly related to alterations in grey matter volume in several areas in the frontal and temporal cortices, which have formerly been implicated in SZ.

关键词： neuroimaging Brain Mental disorders Magnetic resonance imaging Grey matter Frequency estimation Bioinformatics

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Transcriptomics Reveals Distinct Molecular Features Of Endometrial Cancer In Obese And Non-Obese Subjects

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International Journal of Gynecological Cancer 2025年第2期35卷

作者： Xiaoman Jin Anouk Van Den Bosch Sofia Xanthouleas Bert Delvoux Tineke Van De Weijer Brecheisen Ralph Andrea Romano Henrica M.j. Werner Department of Obstetrics and Gynecology Maastricht University Medical Centre Maastricht the Netherlands； GROW – Research Institute for Oncology and Reproduction Maastricht University Maastricht The Netherlands Department of Radiology and Nuclear Medicine Maastricht University Medical Center 6200 MD Maastricht The Netherlands； NUTRIM Institute of Nutrition and Translational Research in Metabolism Maastricht The Netherlands Department of Surgery Maastricht University The Netherlands； NUTRIM Institute of Nutrition and Translational Research in Metabolism Maastricht The Netherlands Clinical Data Science Maastricht University The Netherlands； GROW - Research Institute for Oncology and Reproduction Maastricht The Netherlands

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Multi-Modal Deep Learning on Imaging Genetics for Schizophrenia Classification

Multi-Modal Deep Learning on Imaging Genetics for Schizophre...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Ayush Kanyal Srinivas Kandula Vince Calhoun Dong Hye Ye Department of Computer Science Georgia State University USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) USA

Schizophrenia (SZ) is a severe, chronic mental condition that impacts one’s capacity to think, act, and interact with others. It has been established that SZ patients have morphological changes in their brains, along with decreased hippocampal and thalamic volume. Also, it is known that patients with SZ have irregular functional brain connectivity. Furthermore, because SZ is a genetic illness, genetic markers such as single nucleotide polymorphisms (SNP) can be useful to characterize SZ patients. We propose an automatic method to detect changes in SZ patients’ brains considering its heterogeneous multi-modal nature. We present a novel deep-learning method to classify SZ subjects with morphological features from structural MRI (sMRI), brain connectivity features from functional MRI (fMRI), and genetic features from SNPs. For sMRI, we used a pre-trained DenseNet to extract convolutional features which encode the morphological changes induced by SZ. For fMRI, we choose the important connections in functional network connection (FNC) matrix by applying layer-wise relevance propagation (LRP). We also detect SZ-linked SNPs using LRP on a pre-trained 1-dimensional convolutional neural network. Combined features from these three modalities are then fed to an extreme gradient boosting (XGBoost) tree classifier for SZ diagnosis. The experiments using the clinical dataset have shown that our multi-modal approach significantly improved SZ classification accuracy compared with uni-modal deep learning methods.

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STATISTICAL DESIGN AND RATIONALE OF THE BIOMARKERS FOR EVALUATING SPINE TREATMENTS (BEST) TRIAL

arXiv

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arXiv 2025年

作者： Sperger, John Kidwell, Kelley M. Mauck, Matthew C. Zhao, Beibo Anstrom, Kevin J. Batorsky, Anna Carey, Timothy S. Clauw, Daniel J. Freeman, Nikki L.B. Greco, Carol M. Ivanova, Anastasia Berkeley, Sara Jones McLean, Samuel A. Psioda, Matthew A. Rowland, Bryce Sowa, Gwendolyn A. Wasan, Ajay D. Zitovsky, Joshua P. Kosorok, Michael R. Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Biostatistics School of Public Health University of Michigan United States Department of Anesthesiology University of North Carolina Chapel Hill United States Collaborative Studies Coordinating Center Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Medicine University of North Carolina Chapel Hill United States Department of Anesthesiology Chronic Pain & Fatigue Research Center University of Michigan Ann Arbor United States Department of Biostatistics and Bioinformatics Duke University United States Department of Psychiatry University of Pittsburgh School of Medicine United States Department of Epidemiology Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Psychiatry University of North Carolina Chapel Hill United States Statistics and Data Science Innovation Hub Biostatistics GSK Department of Physical Medicine and Rehabilitation University of Pittsburgh School of Medicine United States Departments of Anesthesiology and Perioperative Medicine and Psychiatry University of Pittsburgh School of Medicine United States Amazon Postdoctoral Scientist United States Ferguson Laboratory for Orthopaedic and Spine Research Bethel Musculoskeletal Research Center Department of Orthopaedic Surgery University of Pittsburgh School of Medicine United States Department of Bioengineering University of Pittsburgh Swanson School of Engineering United States Clinical and Translational Science Institute University of Pittsburgh United States Carolina Pragmatic Trials Laboratory North Carolina Translational and Clinical Sciences Institute UNC School of Medicine United States Duke Clinical Research Institute United States

Background/Aims: Chronic low back pain (cLBP) is a widely prevalent condition with potentially severe impacts on day-to-day functioning and quality of life. A variety of evidence-based treatment modalities exist, but all approaches have modest average treatment effects—potentially due to individual variation in treatment response and the diverse etiologies of cLBP. The Biomarkers for Evaluating Spine Treatments (BEST) trial was designed to investigate the potential for using patient features including biomarkers and phenotypic measurements to inform cLBP treatment. Methods: We provide an overview of the BEST trial and detail the statistical rationale for key study design decisions. We discuss the salient features of the scientific and clinical context of cLBP treatment that motivated the study design and planned analysis, describe modifications to existing methods that were needed because of the setting, and present simulation studies that were used to inform the study design. Results: The BEST trial was designed as a multi-site sequential, multiple-assignment randomized trial (SMART) of four cLBP treatment modalities. The primary objective was to inform a precision medicine approach to the treatment of cLBP by estimating the optimal treatment or combination of treatments based on patient features and individual patient’s response to the initial treatment. The primary analysis includes estimating an interpretable dynamic treatment regime for recommending treatments based on patient features and investigating the subgroups that respond best to each one of the treatment modalities. The trial aimed to enroll 630 protocol completers. Broad eligibility criteria were used to improve the generalizability of study results. Notably, participants could be excluded from one of the four interventions and still be eligible for the study. We modified the minimization method for reducing covariate imbalance to allow for these exclusions. Conclusion: The BEST trial may pave the way

关键词： Patient treatment

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HSPA2 influences the differentiation and production of immunomodulatory mediators in human immortalized epidermal keratinocyte lines

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Cell Death and Disease 2025年第1期16卷 1-15页

作者： Gogler, Agnieszka Wilk, Agata Małgorzata Sojka, Damian Robert Adamiec-Organiściok, Małgorzata Matysiak, Natalia Kania, Daria Wiecha, Klaudia Małusecka, Ewa Cortez, Alexander Jorge Zamojski, Dawid Marczyk, Michał Mazurek, Agnieszka Maria Oziębło, Sylwia Scieglinska, Dorota Center for Translational Research and Molecular Biology of Cancer Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze Armii Krajowej 15 Gliwice 44-102 Poland Department of Biostatistics and Bioinformatics Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze Armii Krajowej 15 Gliwice 44-102 Poland Department of Systems Biology and Engineering Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland Department of Histology and Cell Pathology Faculty of Medical Sciences in Zabrze Medical University of Silesia in Katowice Jordana 19 Zabrze 41-808 Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland Genetic Laboratory Gyncentrum Sp. z o.o. Sosnowiec 41-208 Poland Yale Cancer Center Yale School of Medicine New Haven CT United States

Chaperone proteins constitute a molecular machinery that controls proteostasis. HSPA2 is a heat shock-non-inducible member of the human HSPA/HSP70 family, which includes several highly homologous chaperone proteins. HSPA2 exhibits a cell type-specific expression pattern in the testis, brain, and multilayered epithelia. It is a crucial male fertility-related factor, but its role in somatic cells is poorly understood. Previously, we found that HSPA2 deficiency can impair epidermal keratinocyte differentiation. In this study, we confirmed the crucial role of HSPA2 in keratinocyte differentiation by investigating immortalized keratinocytes cultured in a reconstructed human epidermis model. Moreover, we uncovered the influence of HSPA2 on immunomodulation. Transcriptomic analysis revealed that the total loss of HSPA2 affected the expression of genes related to keratinocyte differentiation and interleukin- and interferon-mediated signaling. The functional analysis confirmed bidirectional changes associated with the loss of HSPA2. The HSPA2 knockout in HaCaT and Ker-CT keratinocytes, but not HSPA2 overproduction, impaired granular layer development as evidenced by reduced levels of late keratinocyte differentiation markers, filaggrin and involucrin, along with structural abnormalities in the upper epidermal layer. Differentiation defects were accompanied by increased mRNA expression and extracellular secretion of keratinocyte-derived pro-inflammatory IL-6 cytokine and CCL2, CCL8, CXCL1, CXCL6, and CXCL10 chemokines. The loss of HSPA2 also led to increased expression of extracellular HSPA1 and interferon-stimulated genes and secretion of immune cell modulator SLAMF7. Knocking down HSPA1 expression in keratinocytes decreased the secretion of IL-6 and CCL5 release, suggesting extracellular HSPA1’s role in the HSPA2-regulated molecular network. To summarize, we uncovered the complex homeostatic role of HSPA2 in epidermal keratinocytes. Our results suggest that dysfunction in H

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Impact of germline variants on breast and ovarian cancer risk in Japanese women: an original cohort study and meta-analysis

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eBioMedicine 2025年 116卷 105758-105758页

作者： Yazaki, Shu Hori, Megumi Aiba, Hisaki Momozawa, Yukihide Yoshida, Masayuki Shiino, Sho Harano, Kenichi Yamauchi, Chisako Yamanaka, Takashi Miwa, Maiko Matsuda, Koichi Kawai, Yosuke Kobayashi-Kato, Mayumi Kitagawa, Masakazu Saito, Motonobu Nakagomi, Hiroshi Tachibana, Kazunoshin Sakamoto, Ikuko Takahashi, Kazuaki Asami, Yuka Katanoda, Kota Kuchiba, Aya Yoshida, Hiroshi Ishikawa, Mitsuya Shimoi, Tastunori Sudo, Kazuki Shimizu, Chikako Shimomura, Akihiko Murata, Takeshi Yamashita, Yuji Kogawa, Takahiro Fujiwara, Saori Saji, Haruya Kato, Hisamori Miyagi, Etsuko Iwasaki, Yusuke Aoi, Tomomi Takata, Sadaaki Ogasawara, Aiko Ohtake, Tohru Fujimori, Keiya Hirotsu, Yosuke Nagashima, Minoru Komatsu, Masaaki Hamamoto, Ryuji Hirata, Makoto Yoshida, Teruhiko Honda, Takayuki Hiranuma, Kengo Matsuda, Maiko Shimada, Yoko Sunami, Kuniko Noiri, Eisei Omae, Yosuke Matsumoto, Koji Okamoto, Aikou Omata, Masao Watanabe, Takafumi Miyagi, Yohei Murakami, Yoshinori Tokunaga, Katsushi Hasegawa, Kosei Kato, Tomoyasu Onishi, Tatsuya Yamashita, Toshinari Naito, Yoichi Suto, Akihiko Yonemori, Kan Kohno, Takashi Shiraisihi, Kouya Matsuda, Koichi Iwasaki, Yusuke Aoi, Tomomi Takata, Sadaaki Murakami, Yoshinori Noiri, Eisei Omae, Yosuke Tokunaga, Katsushi Division of Genome Biology National Cancer Center Research Institute Tokyo Japan Department of Medical Oncology National Cancer Center Hospital Tokyo Japan Department of International Clinical Development National Cancer Center Hospital Tokyo Japan School of Nursing University of Shizuoka Shizuoka Japan Laboratory for Genotyping Development RIKEN Center for Integrative Medical Sciences (IMS) Yokohama Japan Department of Diagnostic Pathology National Cancer Center Hospital Tokyo Japan Department of Breast Surgery National Cancer Center Hospital Tokyo Japan Department of Experimental Therapeutics National Cancer Center Hospital East Chiba Japan Department of Medical Oncology National Cancer Center Hospital East Chiba Japan Department of Breast Surgery National Cancer Center Hospital East Chiba Japan Department of Breast Surgery and Oncology Kanagawa Cancer Center Yokohama Japan Department of Gynecologic Oncology Saitama Medical University International Medical Center Saitama Japan Laboratory of Clinical Genome Sequencing Department of Computational Biology and Medical Science of Frontier Sciences The University of Tokyo Tokyo Japan Genome Medical Science Project Research Institute National Center for Global Health and Medicine Tokyo Japan Department of Gynecology National Cancer Center Hospital Tokyo Japan Department of Gynecology Kanagawa Cancer Center Kanagawa Japan Department of Gastrointestinal Tract Surgery Fukushima Medical University School of Medicine Fukushima Japan Department of Surgery Yamanashi Central Hospital Yamanashi Japan Department of Breast Surgery Fukushima Medical University School of Medicine Fukushima Japan Department of Obstetrics and Gynecology Yamanashi Central Hospital Yamanashi Japan Department of Obstetrics and Gynecology The Jikei University School of Medicine Tokyo Japan Department of Obstetrics and Gynecology Showa Medical University Tokyo Japan Division of Population Data Science National Cancer Center Institute f

Background: Pathogenic variants (PVs) of BRCA1 and BRCA2 predispose individuals to a higher risk of breast and ovarian cancer;however, the precise risks posed by other cancer susceptibility genes remain unclear, particularly in Asian populations. Methods: We executed a case–control study of 11 and 26 genes associated with breast and ovarian cancer susceptibility, respectively, in 7220 women with breast cancer, 2464 women with ovarian cancer, and 4032 controls from a multicentre, hospital-based registry in Japan. Furthermore, we conducted a meta-analysis of 23,193 patients with breast and/or ovarian cancer and 31,190 controls from six other hospital-based studies. Findings: Overall, 395 (5.5%) patients with breast cancer and 331 (13.4%) patients with ovarian cancer harboured PVs. Meta-analyses revealed that PVs of BRCA1, BRCA2, CHEK2, PALB2, and TP53 were associated significantly with breast cancer risk (P < 0.001), while PVs of ATM, BRCA1, BRCA2, MSH6, and RAD51D were associated significantly with ovarian cancer risk (P < 0.001). PVs in the BRCA1 DNA-binding domain were associated with a younger age at diagnosis after adjusting for cancer type and family history (β = −3.79, 95% CI = −7.16 to −0.41;P = 0.028). Interpretation: These results provide information about genes associated with breast and ovarian cancer risk in Asian women, as well as guidance for management of PV carriers. Funding: The study was funded by AMED (JP15ck010609, 19cm0106605h0003, 23ama221520h0001, and JP19kk0305010), by a Health Labour sciences research Grant (202108001B), by JSPS KAKENHI (JP18K16292, 20H03668, 23H02955, 17H06162, 20H03695, and 16H06277), and by a Grant-in-Aid for the Genome research Project from Yamanashi Prefecture. © 2025 The Author(s)

关键词： Breast cancer Cancer predisposition genes Cumulative cancer risk Ovarian cancer

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Risk of ischaemic stroke in patients with transient global amnesia: a propensity-matched cohort study

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Stroke & Vascular Neurology 2022年第2期7卷 101-107页

作者： Sang Hun Lee Keon-Yeup Kim Jeong-Woo Lee So-Jeong Park Jin-Man Jung Department of Neurology Korea University Ansan HospitalKorea University College of MedicineAnsanRepublic of Korea Data Science Team Hanmi Pharm.Co.LtdSeoulRepublic of Korea Korea University Zebrafish Translational Medical Research Center AnsanRepublic of Korea

Background The exact pathophysiological mechanism of transient global amnesia(TGA)is *** is debatable whether TGA is a risk factor for ***,here we investigated the possibility of TGA as a risk factor for stroke in a real-world setting using large-scale nationwide health claims *** We used health claims data from the Korean National Health Insurance Service(NHIS).Patients diagnosed with TGA between 2007 and 2013 were *** initially extracted patients without TGA who were preferentially matched for age and sex with the patients with TGA at a ratio of 10:1 from the whole ***,we performed 1:2 propensity score matching analysis to balance the baseline characteristics between the two *** the propensity score-matched dataset,we performed multivariable Cox regression analysis to investigate the association between TGA and stroke type,including ischaemic,haemorrhagic and all stroke *** Patients with TGA(n=14673)were selected from the NHIS *** extracting from the whole database(n=140486)and propensity score matching their data at a 1:2 ratio,a total of 10448 and 20442 patients were finally assigned to the TGA and control groups,*** multivariable Cox regression analysis demonstrated that the TGA group had a higher risk of ischaemic stroke and all types of stroke(adjusted HR=1.194;95%CI:1.043 to 1.368;and HR=1.197;95%CI:1.056 to 1.357,respectively).Conclusions Analysis of the nationwide claims database showed that TGA could be an important risk factor for stroke,especially for ischaemic stroke.

关键词： patients matched transient

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Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram data*

Evaluating Augmentation Approaches for Deep Learning-based M...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Charles A. Ellis Robyn L. Miller Vince D. Calhoun Center for Translational Research in Neuroimaging and Data Science Georgia State University Emory University Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their potential for clinical translation. data augmentation (DA) could address this limitation, but the utility of EEG DA methods remains relatively underexplored in neuropsychiatric disorder diagnosis. In this study, we train a model for major depressive disorder diagnosis. We then evaluate the utility of 6 EEG DA approaches. Importantly, to remove the bias that could be introduced by comparing performance for models trained on larger augmented training sets to models trained on smaller baseline sets, we also introduce a new baseline trained on duplicate training data. We lastly examine the effects of the DA approaches upon representations learned by the model with a pair of explainability analyses. We find that while most approaches boost model performance, they do not improve model performance beyond that of simply using a duplicate training set without DA. The exception to this is channel dropout augmentation, which does improve model performance. These findings suggest the importance of comparing EEG DA methods to a baseline with a duplicate training set of equal size to the augmented training set. We also found that some DA methods increased model robustness to frequency (Fourier transform surrogates) and channel (channel dropout) perturbation. While our findings on EEG DA efficacy are restricted to our dataset and model, we hope that future studies on deep learning for small EEG datasets and on new EEG DA methods will find our findings helpful.

关键词： Training Deep learning Analytical models Perturbation methods Training data Brain modeling Depression Electroencephalography data models Robustness

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