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Introduction: Group information-guided independent component analysis (GIGICA) and independent vector analysis (IVA) are two methods that improve estimation of subject-specific independent components in neuroimaging studies. These methods have shown better performance than traditional group independent component analysis (GICA) with respect to intersubject variability (ISV). Methods: In this study, we compared the patterns of community structure, spatial variance, and prediction performance of GIG-ICA and IVA-GL, respectively. The dataset was obtained from the publicly available Autism Brain Imaging Data Exchange (ABIDE) database, comprising 75 healthy controls (HC) and 102 Autism Spectrum Disorder (ASD) participants. The greedy rule was used to match components from IVA-GL and GIG-ICA in order to compare the similarities between the two methods. Results: Robust correspondence was observed between the two methods the following networks: cerebellum network (CRN; |r| = 0.7813), default mode network (DMN; |r| = 0.7263), self-reference network (SRN; |r| = 0.7818), ventral attention network (VAN; |r| = 0.7574), and visual network (VSN; |r| = 0.7503). Additionally, the Sensorimotor Network demonstrated the highest similarity between IVA-GL and GIGICA (SOM: |r| = 0.8125). Our findings revealed a significant difference in the number of modules identified by the two methods (HC: p < 0.001; ASD: p < 0.001). GIG-ICA identified significant differences in FNC between HC and ASD compared to IVA-GL. However, in correlation analysis, IVA-GL identified a statistically negative correlation between FNC of ASD and the social total subscore of the classic Autism Diagnostic Observation Schedule (ADOS: pi = -0.26, p = 0.0489). Moreover, both methods demonstrated similar prediction performances on age within specific networks, as indicated by GIG-ICA-CRN (R2 = 0.91, RMSE = 3.05) and IVA-VAN (R2 = 0.87, RMSE = 3.21). Conclusion: In summary, IVA-GL demonstrated lower modularity, suggesting greater sensitivity in estimating networks with higher intersubject variability. The improved age prediction of cerebellar-attention networks underscores their importance in the developmental progression of ASD. Overall, IVA-GL may be appropriate for investigating disorders with greater variability, while GIG-ICA identifies functional networks with distinct modularity patterns. [ABSTRACT FROM AUTHOR]

Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized cortical parcellation based on graph neural networks (GNN) to learn the reliable functional characteristics of each brain parcel on a large fMRI dataset and to infer the areal probability of each vertex on unseen subjects. A subject-specific confidence mask was implemented in the GNN model to account for the tradeoff between the topographic alignment across subjects and functional homogeneity of brain parcels on individual brains. The individualized brain parcellation achieved better functional homogeneity at rest and during cognitive tasks compared with the group-registered atlas (p -values < 0.05). In addition, highly reliable and replicable parcellation maps were generated on multiple sessions of the same subject (intrasubject similarity = 0.89), while notable variations in the topographic organization were captured across subjects (intersubject similarity = 0.81). Moreover, the intersubject variability of brain parcellation indicated large variations in the association cortices while keeping a stable parcellation on the primary cortex. Such topographic variability was strongly associated with the functional connectivity variability, significantly predicted cognitive behaviors, and generally followed the myelination, cytoarchitecture, and functional organization of the human brain. This study provides new avenues to the precise individualized mapping of the cortical areas through deep learning and shows high potentials in the personalized localization diagnosis and treatment of neurological disorders. [ABSTRACT FROM AUTHOR]

Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized cortical parcellation based on graph neural networks (GNN) to learn the reliable functional characteristics of each brain parcel on a large fMRI dataset and to infer the areal probability of each vertex on unseen subjects. A subject-specific confidence mask was implemented in the GNN model to account for the tradeoff between the topographic alignment across subjects and functional homogeneity of brain parcels on individual brains. The individualized brain parcellation achieved better functional homogeneity at rest and during cognitive tasks compared with the group-registered atlas (p-values < 0.05). In addition, highly reliable and replicable parcellation maps were generated on multiple sessions of the same subject (intrasubject similarity = 0.89), while notable variations in the topographic organization were captured across subjects (intersubject similarity = 0.81). Moreover, the intersubject variability of brain parcellation indicated large variations in the association cortices while keeping a stable parcellation on the primary cortex. Such topographic variability was strongly associated with the functional connectivity variability, significantly predicted cognitive behaviors, and generally followed the myelination, cytoarchitecture, and functional organization of the human brain. This study provides new avenues to the precise individualized mapping of the cortical areas through deep learning and shows high potentials in the personalized localization diagnosis and treatment of neurological disorders.

Objective: This study explored the bioequivalence of a proposed biosimilar HOT-3010 vs. its reference product (adalimumab) among healthy Chinese male subjects. The study also investigated the tolerance, immunogenicity, and pharmacokinetics (PK).Methods: A randomized, double-blind, two-arm, parallel study was performed to examine the bioequivalence of HOT-3010 (40 mg) with that of adalimumab (Humira®, AbbVie) as a reference drug. The study subjects were followed up for 71 days.Results: PK properties exhibited by HOT-3010 (N = 66) and adalimumab (N = 68) groups were similar. The 90% confidence intervals of the ratios for Cmax, AUC0-t, and AUC0∞ were observed to be in the range 80–125% on comparing the two groups. For anti-drug antibodies (ADA), the number of subjects found to be positive in the HOT-3010 group and adalimumab group were 29 (43.94%) and 32 (47.06%), whereas 27 (40.91%) and 27 (39.71%) subjects were found to be positive for NAb, respectively. Treatment-related treatment-emergent adverse events (TEAEs) were recorded in 32 subjects each in both the groups, respectively.Conclusion: The PK characteristics and immunogenicity exhibited by HOT-3010 were similar to that of the reference product, adalimumab. The safety profiles were similar in both the treatment groups with mild-moderate adverse effects.

Objective: This study explored the bioequivalence of a proposed biosimilar HOT-3010 vs. its reference product (adalimumab) among healthy Chinese male subjects. The study also investigated the tolerance, immunogenicity, and pharmacokinetics (PK). Methods: A randomized, double-blind, two-arm, parallel study was performed to examine the bioequivalence of HOT-3010 (40 mg) with that of adalimumab (Humira®, AbbVie) as a reference drug. The study subjects were followed up for 71 days. Results: PK properties exhibited by HOT-3010 (N = 66) and adalimumab (N = 68) groups were similar. The 90% confidence intervals of the ratios for C max, AUC0- t , and AUC0∞ were observed to be in the range 80–125% on comparing the two groups. For anti-drug antibodies (ADA), the number of subjects found to be positive in the HOT-3010 group and adalimumab group were 29 (43.94%) and 32 (47.06%), whereas 27 (40.91%) and 27 (39.71%) subjects were found to be positive for NAb, respectively. Treatment-related treatment-emergent adverse events (TEAEs) were recorded in 32 subjects each in both the groups, respectively. Conclusion: The PK characteristics and immunogenicity exhibited by HOT-3010 were similar to that of the reference product, adalimumab. The safety profiles were similar in both the treatment groups with mild-moderate adverse effects. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including aspect of skill learning, explicit memory mechanisms and treatment of Huntington's disease.