Psychoradiology最新文献

{"title":"Transcriptomic and Neuroimaging Data Integration Enhances Machine Learning Classification of Schizophrenia","authors":"Mengya Wang, Shu Zhao, Di Wu, Ya-Hong Zhang, Yan-Kun Han, Kun Zhao, Ting Qi, Yong Liu, Long-Biao Cui, Yongbin Wei","doi":"10.1093/psyrad/kkae005","DOIUrl":"https://doi.org/10.1093/psyrad/kkae005","url":null,"abstract":"\u0000 \u0000 \u0000 Schizophrenia is a polygenetic disorder associated with changes in brain structure and function. Integrating macroscale brain features and microscale genetic data may provide a more complete overview of the disease etiology and may serve as potential diagnostic markers for schizophrenia.\u0000 \u0000 \u0000 \u0000 We aim to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models.\u0000 \u0000 \u0000 \u0000 we collected brain imaging data and blood RNA-seq data from 43 schizophrenia patients and 60 age-, gender-matched healthy controls, and we extracted multi-omics features of macroscale brain morphology, brain structural connectivity and functional connectivity, and gene transcription of schizophrenia risk genes. Multi-scale data fusion was performed using a machine learning integration framework, together with several conventional machine learning methods and neural networks for patient classification.\u0000 \u0000 \u0000 \u0000 We found that multi-omics data fusion in conventional machine learning models achieved the highest accuracy in contrast to the single-modality models, with AUC improvements of 8.88% to 22.64%. Similar findings were observed for the neural network, showing an increase of 16.57% for the multimodal classification accuracy compared to the single-modal average. In addition, we identified several brain regions in the left posterior cingulate and right frontal pole that contribute to disease classification.\u0000 \u0000 \u0000 \u0000 We provide empirical evidence for the increased accuracy achieved by imaging genetic data integration in schizophrenia classification. Multi-scale data fusion holds promise for enhancing diagnostic precision, facilitating early detection and personalizing treatment regimens in schizophrenia.\u0000","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"76 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140377958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early diagnosis of autism spectrum disorder using structural connectivity biomarker","authors":"W. K. Lau, Mei-Kei Leung, Kean Poon, Ruibin Zhang","doi":"10.1093/psyrad/kkae004","DOIUrl":"https://doi.org/10.1093/psyrad/kkae004","url":null,"abstract":"","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"156 7‐8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140228421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-objectification and Eating disorders: the psychopathological and neural processes from psychological distortion to psychosomatic illness","authors":"Yinying Hu, Yafeng Pan, Liming Yue, Xiangping Gao","doi":"10.1093/psyrad/kkae003","DOIUrl":"https://doi.org/10.1093/psyrad/kkae003","url":null,"abstract":"\u0000 Self-objectification, characterized by treating oneself primarily as a physical entity (A body) or a collection of body parts, has been linked to the development of eating disorders. Yet, the precise mechanisms underpinning this link have remained elusive. From a psychopathological perspective, this article proposes that both self-objectification and eating disorders can be seen as manifestations of self-rumination (repetitive, negative self-focus). While self-objectification involves psychological rumination, eating disorders encompass a complex interplay of psychological and physical (bodily) rumination. In addition, at the neural level, the underlying neural foundations underlying such self-rumination are likely rooted in brain activity and connectivity within networks associated with self-reference, cognitive control, and body perception. Collectively, these perspectives shed light on the psychopathological and neural processes that links self-objectification to the onset of eating disorders.","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140420868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Parkinson's Disease Subtypes with Distinct Brain Atrophy Progression and its Association with Clinical Progression","authors":"Guoqing Pan, Yuchao Jiang, Wei Zhang, Xuejuan Zhang, Linbo Wang, Wei Cheng","doi":"10.1093/psyrad/kkae002","DOIUrl":"https://doi.org/10.1093/psyrad/kkae002","url":null,"abstract":"\u0000 \u0000 \u0000 Studying of heterogeneity of Parkinson's disease (PD) is crucial for comprehending pathophysiological mechanisms underlying the disease. PD patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear.\u0000 \u0000 \u0000 \u0000 The objective of this study was to identify PD subtypes with different rates of GMV loss and to explore whether these subtypes were associated with clinical progression.\u0000 \u0000 \u0000 \u0000 Patients with PD (n = 107, mean age 60.06 ± 9.98 years, 70.09% male) who had baseline and at least three years of follow-up structural MRI scans were included in the study. Linear mixed-effects model (LME) was used to evaluate the rate of GMV loss for each patient at the regional level with adjusting for covariates. Hierarchical cluster analysis was applied to individual rate of GMV loss to test whether there exist different subtypes in PD. Longitudinal changes in clinical scores were compared between different subtypes.\u0000 \u0000 \u0000 \u0000 Hierarchical cluster analysis classified patients into two clusters based on their individual atrophy rates. Subtype 1 (n = 63) had moderate levels of atrophy rates in the prefrontal lobe and lateral temporal lobe, while subtype 2 (n = 44) was characterized by faster atrophy in almost the entire brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS Part Ⅰ, β=1.26 ± 0.18, P = 0.016) and motor (MDS-UPDRS Part Ⅱ, β=1.34 ± 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, β=1.15 ± 0.22, P = 0.043), memory (HVLT-Retention, β=-0.02 ± 0.01, P = 0.016) and depression (GDS, β=0.26 ± 0.083, P = 0.019) compared to subtype 1.\u0000 \u0000 \u0000 \u0000 The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.\u0000","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"3 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140434785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of cortical midline structure in diagnoses and neuromodulation for major depressive disorder","authors":"Xinyuan Yan","doi":"10.1093/psyrad/kkae001","DOIUrl":"https://doi.org/10.1093/psyrad/kkae001","url":null,"abstract":"","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"280 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140484918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Alzheimer's disease: a comprehensive brain connectome-based survey.","authors":"Lu Zhang, Junqi Qu, Haotian Ma, Tong Chen, Tianming Liu, Dajiang Zhu","doi":"10.1093/psyrad/kkad033","DOIUrl":"10.1093/psyrad/kkad033","url":null,"abstract":"<p><p>Dementia is an escalating global health challenge, with Alzheimer's disease (AD) at its forefront. Substantial evidence highlights the accumulation of AD-related pathological proteins in specific brain regions and their subsequent dissemination throughout the broader area along the brain network, leading to disruptions in both individual brain regions and their interconnections. Although a comprehensive understanding of the neurodegeneration-brain network link is lacking, it is undeniable that brain networks play a pivotal role in the development and progression of AD. To thoroughly elucidate the intricate network of elements and connections constituting the human brain, the concept of the brain connectome was introduced. Research based on the connectome holds immense potential for revealing the mechanisms underlying disease development, and it has become a prominent topic that has attracted the attention of numerous researchers. In this review, we aim to systematically summarize studies on brain networks within the context of AD, critically analyze the strengths and weaknesses of existing methodologies, and offer novel perspectives and insights, intending to serve as inspiration for future research.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"4 ","pages":"kkad033"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10848159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139708716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex dimorphic cortical brain volumes associated with antisocial behavior in young adults.","authors":"Ke Ding, Miao Xu, Taicheng Huang, Yiying Song, Feng Kong, Zonglei Zhen","doi":"10.1093/psyrad/kkad031","DOIUrl":"https://doi.org/10.1093/psyrad/kkad031","url":null,"abstract":"<p><strong>Background: </strong>Although sex differences in antisocial behavior are well-documented, the extent to which neuroanatomical differences are related to sex differences in antisocial behavior is unclear. The inconsistent results from different clinical populations exhibiting antisocial behaviors are mainly due to the heterogeneity in etiologies, comorbidity inequality, and small sample size, especially in females.</p><p><strong>Objective: </strong>The study aimed to find sexual dimorphic brain regions associated with individual differences in antisocial behavior while avoiding the issues of heterogeneity and sample size.</p><p><strong>Methods: </strong>We collected structural neuroimaging data from 281 college students (131 males, 150 females) and analyzed the data using voxel-based morphometry.</p><p><strong>Results: </strong>The gray matter volume in three brain regions correlates with self-reported antisocial behavior in males and females differently: the posterior superior temporal sulcus, middle temporal gyrus, and precuneus. The findings have controlled for the total cortical gray matter volume, age, IQ, and socioeconomic status. Additionally, we found a common neural substrate of antisocial behavior in both males and females, extending from the anterior temporal lobe to the insula.</p><p><strong>Conclusion: </strong>This is the first neuroanatomical evidence from a large non-clinical sample of young adults. The study suggests that differences in males and females in reading social cues, understanding intentions and emotions, and responding to conflicts may contribute to the modulation of brain morphometry concerning antisocial behavior.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"3 ","pages":"kkad031"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10917369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140869067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differences in olfactory functional connectivity in early-onset depression and late-onset depression","authors":"Ben Chen, Mingfeng Yang, Meiling Liu, Qiang Wang, Huarong Zhou, Min Zhang, L. Hou, Zhangying Wu, Si Zhang, Gaohong Lin, X. Zhong, Yuping Ning","doi":"10.1093/psyrad/kkad030","DOIUrl":"https://doi.org/10.1093/psyrad/kkad030","url":null,"abstract":"\u0000 \u0000 \u0000 Late-onset depression (LOD) and early-onset depression (EOD) exhibit different pathological mechanisms and clinical phenotypes, including different extents of olfactory dysfunction. However, the brain abnormalities underlying the differences in olfactory dysfunction between EOD and LOD remain unclear.\u0000 \u0000 \u0000 \u0000 The aim of this study was to compare the functional connectivity (FC) patterns of olfactory regions between EOD patients and LOD patients and examine their relationship with cognitive function.\u0000 \u0000 \u0000 \u0000 One hundred five patients with EOD, 101 patients with LOD and 160 normal controls (NCs) were recruited for the present study. Subjects underwent clinical assessment, olfactory testing, cognitive assessments and magnetic resonance imaging. Eight regions of the primary and secondary olfactory regions were selected to investigate olfactory FC.\u0000 \u0000 \u0000 \u0000 Patients with LOD exhibited decreased odor identification (OI) compared with patients with EOD and NCs. The LOD group exhibited decreased FC compared with the EOD and NC groups when primary and secondary olfactory regions were selected as the regions of interest (the piriform cortex, lateral entorhinal cortex and orbital-frontal cortex). Additionally, these abnormal olfactory FCs were associated with decreased cognitive function scores and OI, and the FC between the left orbital-frontal cortex and left amygdala was a partial mediator of the relationship between global cognitive scores and OI.\u0000 \u0000 \u0000 \u0000 Overall, patients with LOD exhibited decreased FC in both the primary and secondary olfactory cortices compared with patients with EOD, and abnormal olfactory FC was associated with OI dysfunction and cognitive impairment. The FC between the orbital-frontal cortex and amygdala mediated the relationship between global cognitive function and OI.\u0000","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"65 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138604860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The aspect of structural connectivity in relation to age-related gait performance","authors":"Cheol-woon Kim, Yechan Kim, Hyun-ho Kim, Joon Yul Choi","doi":"10.1093/psyrad/kkad028","DOIUrl":"https://doi.org/10.1093/psyrad/kkad028","url":null,"abstract":"","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139231500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Connectome-based prediction of the severity of autism spectrum disorder.","authors":"Xuefeng Ma, Weiran Zhou, Hui Zheng, Shuer Ye, Bo Yang, Lingxiao Wang, Min Wang, Guang-Heng Dong","doi":"10.1093/psyrad/kkad027","DOIUrl":"https://doi.org/10.1093/psyrad/kkad027","url":null,"abstract":"<p><strong>Background: </strong>Autism spectrum disorder (ASD) is characterized by social and behavioural deficits. Current diagnosis relies on behavioural criteria, but machine learning, particularly connectome-based predictive modelling (CPM), offers the potential to uncover neural biomarkers for ASD.</p><p><strong>Objective: </strong>This study aims to predict the severity of ASD traits using CPM and explores differences among ASD subtypes, seeking to enhance diagnosis and understanding of ASD.</p><p><strong>Methods: </strong>Resting-state functional magnetic resonance imaging data from 151 ASD patients were used in the model. CPM with leave-one-out cross-validation was conducted to identify intrinsic neural networks that predict Autism Diagnostic Observation Schedule (ADOS) scores. After the model was constructed, it was applied to independent samples to test its replicability (172 ASD patients) and specificity (36 healthy control participants). Furthermore, we examined the predictive model across different aspects of ASD and in subtypes of ASD to understand the potential mechanisms underlying the results.</p><p><strong>Results: </strong>The CPM successfully identified negative networks that significantly predicted ADOS total scores [<i>r</i> (df = 150) = 0.19, <i>P</i> = 0.008 in all patients; <i>r</i> (df = 104) = 0.20, <i>P</i> = 0.040 in classic autism] and communication scores [<i>r</i> (df = 150) = 0.22, <i>P</i> = 0.010 in all patients; <i>r</i> (df = 104) = 0.21, <i>P</i> = 0.020 in classic autism]. These results were reproducible across independent databases. The networks were characterized by enhanced inter- and intranetwork connectivity associated with the occipital network (OCC), and the sensorimotor network (SMN) also played important roles.</p><p><strong>Conclusions: </strong>A CPM based on whole-brain resting-state functional connectivity can predicted the severity of ASD. Large-scale networks, including the OCC and SMN, played important roles in the predictive model. These findings may provide new directions for the diagnosis and intervention of ASD, and maybe could be the targets in novel interventions.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"3 ","pages":"kkad027"},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10917386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140867024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}