https://t.co/2qNGX5YXUm Background: Altered connectivity patterns in socio-emotional brain networks are characteristic of individuals with ASD. Its specific effects on the functional connectivity network topology remain underexplored. #Autistic_traits #oxytocin #psychoradiology

https://t.co/2qNGX5YXUm Results (1) The findings revealed significantly different effects of oxytocin in local but not global graph metrics in individuals with higher autistic traits compared to those with lower ones, across multiple brain regions.#fMRI #graph_theory

https://t.co/2qNGX5YXUm Methods 🧩Data: 250 neurotypical adult male subjects with either high or low autistic traits. 🕸️Resting-state functional connectivity data were analyzed using network-based statistical methods and graph theoretical approaches.#Autistic_traits #oxytocin

🌟「Editor’s Pick of the Week」 New study🥳 in @psychoradiology explored whether Intranasal oxytocin may particularly influence neural network processing in individuals with higher autistic traits.🕸️🧠 Full paper:   https://t.co/iE4fVMzhUg #Autistic_traits #oxytocin #graph_theory

https://t.co/LC7toyfBZR Conclusions： The inhibitory connectivity from the hippocampus to the superior temporal gyrus may serve as a potential biomarker for personalized diagnosis, offering new insights into the underlying pathological mechanisms of anti-LGI1 encephalitis.

https://t.co/LC7toyfBZR (2) Crucially, inhibitory EC from the right hippocampus to the left superior temporal gyrus correlated inversely with symptom severity and positively with cognitive performance. #effective_connectivity #spectral_dynamic_causal_modeling #fMRI

https://t.co/LC7toyfBZR Results: (1) Distinct EC patterns were found in patients vs controls. Inhibitory EC was observed from the hippocampus to the superior temporal gyrus, while excitatory EC was noted in the reverse direction. #Anti_LGI1_encephalitis #fMRI

✨Highlight from Psychoradiology: 📄This study analyzed functional MRI data from participants watching a cartoon video, mapping hierarchical features of the video to different levels of brain activation using a pre-trained VGG-16 network. Full paper: https://t.co/8LnWOHB5L6 #AI

https://t.co/LC7toyfBZR Methods： Data: 27 anti-LGI1 encephalitis patients and 28 HC. ALFF analysis identified altered brain regions. SpDCM then assessed EC between these regions. Relationships between EC strength and both clinical severity and cognitive function were analyzed.

✨Highlight from Psychoradiology: 📄 In the interview, Professor Benjamin Becker emphasized the significant role of interdisciplinary collaboration for a deeper understanding of the brain and psychiatric disorders. The full text: https://t.co/jepFK6MhMO #Interview #MentalHealth

https://t.co/LC7toyfBZR Background： Despite advances in understanding the effective connectivity (EC) of brain networks in leucine-rich glioma-inactivated 1 (LGI1) antibody encephalitis, the specific cause and underlying mechanisms of LGI1 encephalitis remain unclear.#fMRI

🧠New research in @psychoradiology! One study revealed specific cause and underlying mechanisms🔬 of LGI1 encephalitis. This fMRI study offered new insights into the underlying pathological mechanisms of this disorder🌍 Full paper:   https://t.co/ZEnI4FsSlX #Anti_LGI1_encephalitis

https://t.co/RGwpChfcw6 This study provided 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 in schizophrenia.#schizophrenia

✨Hot topic in Psychoradiology: 💡The brain's "self-related" hub, the cortical midline structure, plays a key role in the negative self-beliefs seen in MDD. This commentary explores its importance for diagnosis and potential neuromodulation targets: https://t.co/TGPh5VJIc3 #MDD

Several brain regions in the left posterior cingulate and right frontal pole made a major contribution to disease classification.

https://t.co/RGwpChfcw6 Results： Multi-omics data fusion in conventional machine learning models achieved the highest accuracy (AUC ~0.76–0.92). The neural network showed an increase of 16.57% for the multimodal classification model compared to the single-modal average.

🚨Our Nov issue is live - https://t.co/24kddKdvj9 📊Articles: #breastcancer, #NSCLC, #prostatecancer, #melanoma 📰 Reviews: Tumour-infiltrating lymphocyte therapy in the era of genetic engineering AND AI for Response Assessment in Pediatric Neuro-Oncology (AI-RAPNO)

https://t.co/RGwpChfcw6 Methods： Data: imaging blood RNA sequencing data from 43 schizophrenia patients and 60 HC. Multi-omics features of macroscale brain morphology, SC, FC, and related gene transcription were extracted. Machine learning methods was performed.#transcriptomics

https://t.co/RGwpChfcw6 Background： Schizophrenia is associated with structure and function changes. Integrating macroscale brain features with microscale genetic data may provide a more complete overview of the disease etiology and may serve as potential diagnostic markers.

🌟Editor’s Pick of the Week 🤯This study aims to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models. Full paper:   https://t.co/ZEnI4FsSlX #schizophrenia #machine_learning #genomics #transcriptomics