BACKGROUND: After the initial surge in COVID-19 cases, large numbers of patients were discharged from a hospital without assessment of recovery. Now, an increasing number of patients report postacute neurological sequelae, known as “long COVID” — even those without specific neurological manifestations in the acute phase. METHODS: Dynamic brain changes are crucial for a better understanding and early prevention of “long COVID.” Here, we explored the cross-sectional and longitudinal consequences of COVID-19 on the brain in 34 discharged patients without neurological manifestations. Gray matter morphology, cerebral blood flow (CBF), and volumes of white matter tracts were investigated using advanced magnetic resonance imaging techniques to explore dynamic brain changes from 3 to 10 months after discharge. RESULTS: Overall, the differences of cortical thickness were dynamic and finally returned to the baseline. For cortical CBF, hypoperfusion in severe cases observed at 3 months tended to recover at 10 months. Subcortical nuclei and white matter differences between groups and within subjects showed various trends, including recoverable and long-term unrecovered differences. After a 10-month recovery period, a reduced volume of nuclei in severe cases was still more extensive and profound than that in mild cases. CONCLUSION: Our study provides objective neuroimaging evidence for the coexistence of recoverable and long-term unrecovered changes in 10-month effects of COVID-19 on the brain. The remaining potential abnormalities still deserve public attention, which is critically important for a better understanding of “long COVID” and early clinical guidance toward complete recovery. FUNDING: National Natural Science Foundation of China.

Introduction: Heterogeneous MRI manifestations restrict the efficiency and consistency of neuroradiologists in diagnosing hypoxic-ischemic encephalopathy (HIE) due to complex injury patterns. This study aimed to develop and validate an intelligent HIE identification model (termed as DLCRN, deep learning clinical-radiomics nomogram) based on conventional structural MRI and clinical characteristics. Methods: In this retrospective case-control study, full-term neonates with HIE and healthy controls were collected in two different medical centers from January 2015 to December 2020. Multivariable logistic regression analysis was implemented to establish the DLCRN model based on conventional MRI sequences and clinical characteristics. Discrimination, calibration, and clinical applicability were used to evaluate the model in the training and validation cohorts. Grad-class activation map algorithm was implemented to visualize the DLCRN. Results: 186 HIE patients and 219 healthy controls were assigned to the training, internal validation, and independent validation cohorts. Birthweight was incorporated with deep radiomics signatures to create the final DLCRN model. The DLCRN model achieved better discriminatory power than simple radiomics models, with an area under the curve (AUC) of 0.868, 0.813, and 0.798 in the training, internal validation, and independent validation cohorts, respectively. The DLCRN model was well calibrated and has clinical potential. Visualization of the DLCRN highlighted the lesion areas that conformed to radiological identification. Conclusion: Visualized DLCRN may be a useful tool in the objective and quantitative identification of HIE. Scientific application of the optimized DLCRN model may save time for screening early mild HIE, improve the consistency of HIE diagnosis, and guide timely clinical management.

Purpose: To investigate the effective connectivity (EC) changes after multisite repetitive transcranial magnetic stimulation (rTMS) combined with cognitive training (COG).Method: We selected 51 patients with mild or moderate Alzheimer's disease (AD) and delivered 10 Hz rTMS over the left dorsal lateral prefrontal cortex (DLPFC) and the lateral temporal lobe (LTL) combined with COG or sham stimulation for 4 weeks. The selected AD patients were divided into real (real rTMS+COG, n = 11) or sham (sham rTMS+COG, n = 8) groups to undergo neuropsychological assessment, resting-state fMRI, and 3D brain structural imaging before (T0), immediately at the end of treatment (T4), and 4 weeks after treatment (T8). A 2 × 3 factorial design with “time” as the within-subjects factor (three levels: T0, T4, and T8) and “group” as the between-subjects factor (two levels: real and sham) was used to investigate the EC changes related to the stimulation targets in the rest of the brain, as well as the causal interactions among seven resting-state networks based on Granger causality analysis (GCA).Results: At the voxel level, the EC changes from the left DLPFC out to the left inferior parietal lobe and the left superior frontal gyrus, as well as from the left LTL out to the left orbital frontal cortex, had a significant group × time interaction effect. At the network level, a significant interaction effect was identified in the increase in EC from the limbic network out to the default mode network. The decrease in EC at the voxel level and the increase in EC at the network level were both associated with the improved ability to perform activities of daily living and cognitive function.Conclusion: Multisite rTMS combined with cognitive training can modulate effective connectivity in patients with AD, resulting in improved ability to perform activities of daily living and cognitive function.

Experience-dependent associations between distinct subtypes of childhood trauma and brain function and architecture Jia Li, Guiling Zhang, Jian Wang, Dong Liu, Changhua Wan, Jicheng Fang, Di Wu, Yiran Zhou, Tian Tian, Wenzhen Zhu

Purpose This study aims to investigate the potential effect of compromised structural integrity on cerebral aging and cognitive function in cerebral small vessel disease (CSVD). Methods Fifty-five CSVD patients and 42 controls underwent three-dimensional T1-weighted imaging and diffusion tensor imaging. Relative brain age (RBA) was computed to assess cerebral aging. Variables of structural integrity included cortical thickness, cortical volume, white matter hyperintensity (WMH) volume, peak width of skeletonized mean diffusivity (PSMD), ventricular volume, and choroid plexus volume. Mini-Mental State Examination (MMSE) was conducted to assess general cognition. Trail Making Test (TMT) and Auditory Verbal Learning Test were administered to evaluate executive function and episodic memory, respectively. Mediation analysis and multivariate linear regression with interaction terms were performed to explore the differential impacts of RBA on cognitive function and structural integrity between CSVD patients and controls. Results RBA was significantly increased in CSVD patients compared to controls (p < 0.001). White matter injuries as assessed with PSMD (mediation magnitude: 41.1%) and WMH volume (mediation magnitude: 56.9%) significantly mediated the relationship between CSVD pathologies and RBA (p < 0.001). Higher RBA was significantly correlated with poorer scores of MMSE, TMT-A, and TMT-B in CSVD patients (p < 0.01). Additionally, PSMD (mediation magnitude: 57.8% in MMSE, 48.3% in TMT-A, and 28.8% in TMT-B) and WMH volume (mediation magnitude: 55.1% in MMSE) significantly mediated the relationship between RBA and cognitive function (p < 0.05). Conclusion White matter injuries play a critical role in the cerebral aging and cognitive decline in CSVD patients.