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The human frontal pole (FP) approximately corresponds to Brodmann's area 10 and is a highly differentiated cortical area with unique cytoarchitectonic characteristics. However, its functional diversity is highly suggestive of the existence of functional subregions. Based on anatomical connection patterns derived from diffusion tensor imaging data, we applied a spectral clustering algorithm to parcellate the human right FP into orbital (FPo), lateral (FPl), and medial (FPm) subregions. This parcellation scheme was validated by corresponding analyses of the left FP and right FP in another independent dataset. Both visual observation and quantitative comparison of the anatomical connection patterns of the three FP subregions revealed that the FPo showed greater connection probabilities to brain regions of the social emotion network (SEN), including the orbitofrontal cortex, temporal pole, and amygdala, the FPl showed stronger connections to the dorsolateral prefrontal cortex of the cognitive processing network (CPN), and the FPm showed stronger connections to brain areas of the default mode network (DMN), including the anterior cingulate cortex and medial prefrontal cortex. We further analyzed the restingstate functional connectivity patterns of the three FP subregions. Consistent with the findings of anatomical connection analyses, the FPo was functionally correlated with the SEN, the FPl was correlated with the CPN, and the FPm was correlated with the DMN. These findings suggest that the human FP includes three separable subregions with different anatomical and functional connectivity patterns and that these subregions are involved in different brain functional networks and serve different functions.

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IntroductionHuman cingulate cortex (CC) has been implicated in many functions, which is highly suggestive of the existence of functional subregions.MethodsIn this study, we used resting‐state functional magnetic resonance imaging (rs‐fMRI) and diffusion tensor imaging (DTI) to parcellate the human cingulate cortex (CC) based on resting‐state functional connectivity (rsFC) patterns and anatomical connectivity (AC) patterns, to analyze the rsFC patterns and the AC patterns of different subregions, and to recognize whether the parcellation results obtained by the two different methods were consistent.ResultsThe CC was divided into six functional subregions, including the anterior cingulate cortex, dorsal anterior midcingulate cortex, ventral anterior midcingulate cortex, posterior midcingulate cortex, dorsal posterior cingulate cortex, and ventral posterior cingulate cortex. The CC was also divided into ten anatomical subregions, termed Subregion 1 (S1) to Subregion 10 (S10). Each subregion showed specific connectivity patterns, although the functional subregions and the anatomical subregions were internally consistent.ConclusionsUsing different model MRI images, we established a parcellation scheme, which is internally consistent for the human CC, which may provide an in vivo guide for subregion‐level studies and improve our understanding of this brain area at subregional levels.

Depression increases the conversion risk from amnestic mild cognitive impairment to Alzheimer’s disease with unknown mechanisms. We hypothesize that the cumulative genomic risk for major depressive disorder may be a candidate cause for the increased conversion risk. Here, we aimed to investigate the predictive effect of the polygenic risk scores of major depressive disorder-specific genetic variants (PRSsMDD) on the conversion from non-depressed amnestic mild cognitive impairment to Alzheimer’s disease, and its underlying neurobiological mechanisms. The PRSsMDD could predict the conversion from amnestic mild cognitive impairment to Alzheimer’s disease, and amnestic mild cognitive impairment patients with high risk scores showed 16.25% higher conversion rate than those with low risk. The PRSsMDD was correlated with the left hippocampal volume, which was found to mediate the predictive effect of the PRSsMDD on the conversion of amnestic mild cognitive impairment. The major depressive disorder-specific genetic variants were mapped into genes using different strategies, and then enrichment analyses and protein–protein interaction network analysis revealed that these genes were involved in developmental process and amyloid-beta binding. They showed temporal-specific expression in the hippocampus in middle and late foetal developmental periods. Cell type-specific expression analysis of these genes demonstrated significant over-representation in the pyramidal neurons and interneurons in the hippocampus. These cross-scale neurobiological analyses and functional annotations indicate that major depressive disorder-specific genetic variants may increase the conversion from amnestic mild cognitive impairment to Alzheimer’s disease by modulating the early hippocampal development and amyloid-beta binding. The PRSsMDD could be used as a complementary measure to select patients with amnestic mild cognitive impairment with high conversion risk to Alzheimer’s disease.

The cerebellum contains several cognitive-related subregions that are involved in different functional networks. The cerebellar crus II is correlated with the frontoparietal network (FPN), whereas the cerebellar IX is associated with the default-mode network (DMN). These two networks are anticorrelated and cooperatively implicated in cognitive control, which may facilitate the motor recovery in stroke patients. In the present study, we aimed to investigate the resting-state functional connectivity (rsFC) changes in 25 subcortical ischemic stroke patients with well-recovered global motor function. Consistent with previous studies, the crus II was correlated with the FPN, including the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex, and the cerebellar IX was correlated with the DMN, including the posterior cingulate cortex/precuneus (PCC/Pcu), medial prefrontal cortex (MPFC), DLPFC, lateral parietal cortices, and anterior temporal cortices. No significantly increased rsFCs of these cerebellar subregions were found in stroke patients, suggesting that the rsFCs of the cognitive-related cerebellar subregions are not the critical factors contributing to the recovery of motor function in stroke patients. The finding of the disconnection in the cerebellar-related cognitive control networks may possibly explain the deficits in cognitive control function even in stroke patients with well-recovered global motor function.

Extended Data Fig.7. Susceptibility analysis of individual satellite measures with brain (a-d) and behaviors (e-h) using distributed lag models in CHIMGEN.

The human brain is a highly connected and integrated system. Local stroke lesions can evoke reorganization in multiple functional networks. However, the temporally-evolving patterns in different functional networks after stroke remain unclear. Here, we aimed to investigate the dynamic evolutionary patterns of functional connectivity density (FCD) and strength (FCS) of the brain after subcortical stroke involving in the motor pathways. Eight male patients with left subcortical infarctions were longitudinally examined at five time points within a year. Voxel-wise FCD analysis was used to identify brain regions with significant dynamic changes. The temporally-evolving patterns in FCD and FCS in these regions were analyzed by a mixed-effects model. Associations between these measures and clinical variables were also explored in stroke patients. Voxel-wise analysis revealed dynamic FCD changes only in the sensorimotor and cognitive regions after stroke. FCD and FCS in the sensorimotor regions decreased initially, as compared to controls, remaining at lower levels for months, and finally returned to normal levels. In contrast, FCD and FCS in the cognitive regions increased initially, remaining at higher levels for months, and finally returned to normal levels. Most of these measures were correlated with patients’ motor scores. These findings suggest a network-specific dynamic functional reorganization after stroke. Besides the sensorimotor regions, the spared cognitive regions may also play an important role in stroke recovery.