Rumination is strongly and consistently correlated with depression. Although multiple studies have explored the neural correlates of rumination, findings have been inconsistent and the mechanisms ...underlying rumination remain elusive. Functional brain imaging studies have identified areas in the default mode network (DMN) that appear to be critically involved in ruminative processes. However, a meta-analysis to synthesize the findings of brain regions underlying rumination is currently lacking. Here, we conducted a meta-analysis consisting of experimental tasks that investigate rumination by using Signed Differential Mapping of 14 fMRI studies comprising 286 healthy participants. Furthermore, rather than treat the DMN as a unitary network, we examined the contribution of three DMN subsystems to rumination. Results confirm the suspected association between rumination and DMN activation, specifically implicating the DMN core regions and the dorsal medial prefrontal cortex subsystem. Based on these findings, we suggest a hypothesis of how DMN regions support rumination and present the implications of this model for treating major depressive disorder characterized by rumination.
•Rumination is strongly and consistently correlated with depression.•Meta-analyze the findings of brain regions regarding to rumination.•Specifically examined the contribution of three DMN subsystems to rumination.•Rumination is specifically correlated with the DMN core regions and the dorsal medial prefrontal cortex subsystem.
Rumination is a repetitive self-referential thinking style that is often interpreted as an expression of abnormalities of the default mode network (DMN) observed during “resting-state” in major ...depressive disorder (MDD). Recent evidence has demonstrated that the DMN is not unitary but can be further divided into 3 functionally heterogenous subsystems, although the subsystem mechanistically underlying rumination remains unclear. Due to the unconstrained and indirect correlational nature of previous resting-state fMRI studies on rumination's network underpinnings, a paradigm allowing direct investigation of network interactions during active rumination is needed. Here, with a modified continuous state-like paradigm, we induced healthy participants to ruminate or imagine objective scenarios (distraction, as a control condition) on 3 different MRI scanners. We compared functional connectivities (FC) of the DMN and its 3 subsystems between rumination and distraction states. Results yielded a highly reproducible and dissociated pattern. During rumination, within-DMN FC was generally decreased as compared to the distraction state. At the subsystem level, we found increased FC between the core and medial temporal lobe (MTL) subsystem as well as decreased FC between the core and dorsal medial prefrontal cortex (DMPFC) subsystem and within the MTL subsystem. Finally, subjects’ behavioral measures of rumination and brooding were negatively correlated with FC between the core and DMPFC subsystems. These results suggest active rumination involves enhanced constraint by the core subsystem on the MTL subsystem and decreased coupling between the core and DMPFC subsystem, allowing for more information exchange among those involved DMN components. Furthermore, the reproducibility of our findings provides a rigorous evaluation of their validity and significance.
The aim of the current study was to examine how reward-associated emotional facial distractors could capture attentional resources in a demanding visual task using event-related potentials (ERPs). In ...the learning phase, a high- or low-reward probability was paired with angry, happy, or neutral faces. Then, in the test phase, participants performed a face-irrelevant task with no reward at stake, in which they needed to discriminate the length of two lines presented in the center of the screen while faces that were taken from the learning phase were used as distractors presented in the periphery. The behavioral results revealed no effect of distractor emotional valence since the emotional information was task-irrelevant. The ERP results in the test phase revealed a significant main effect of distractor emotional valence for the parieto-occipital P200 (170-230 ms); the mean amplitudes in both the angry- and happy-face conditions were more positive than the neutral-face condition. Moreover, we found that the high-reward association enhanced both the N170 (140-180 ms) and EPN (260-330 ms) relative to the low-reward association condition. Finally, the N2pc (270-320 ms) also exhibited enhanced neural activity in the high-reward condition compared to the low-reward condition. The absence of emotional effects indicated that task-irrelevant emotional facial stimuli did not impact behavioral or neural responses in this highly demanding task. However, reward-associated information was processed when attention was directed elsewhere, suggesting that the processing of reward-associated information worked more in an automatic way, irrespective of the top-down task demand.
Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address ...this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.
Beyond detecting brain lesions or tumors, comparatively little success has been attained in identifying brain disorders such as Alzheimer’s disease (AD), based on magnetic resonance imaging (MRI). ...Many machine learning algorithms to detect AD have been trained using limited training data, meaning they often generalize poorly when applied to scans from previously unseen scanners/populations. Therefore, we built a practical brain MRI-based AD diagnostic classifier using deep learning/transfer learning on a dataset of unprecedented size and diversity. A retrospective MRI dataset pooled from more than 217 sites/scanners constituted one of the largest brain MRI samples to date (85,721 scans from 50,876 participants) between January 2017 and August 2021. Next, a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, was built as a sex classifier with high generalization capability. The sex classifier achieved 94.9% accuracy and served as a base model in transfer learning for the objective diagnosis of AD. After transfer learning, the model fine-tuned for AD classification achieved 90.9% accuracy in leave-sites-out cross-validation on the Alzheimer’s Disease Neuroimaging Initiative (ADNI, 6,857 samples) dataset and 94.5%/93.6%/91.1% accuracy for direct tests on three unseen independent datasets (AIBL, 669 samples / MIRIAD, 644 samples / OASIS, 1,123 samples). When this AD classifier was tested on brain images from unseen mild cognitive impairment (MCI) patients, MCI patients who converted to AD were 3 times more likely to be predicted as AD than MCI patients who did not convert (65.2% vs. 20.6%). Predicted scores from the AD classifier showed significant correlations with illness severity. In sum, the proposed AD classifier offers a medical-grade marker that has potential to be integrated into AD diagnostic practice.
Structural and functional neuroimaging have been widely used to track and predict demographic and clinical variables, including treatment outcomes. However, it is challenging to establish and compare ...the respective weights and contributions of brain structure and function in prediction studies. The present study aimed to directly investigate the respective roles of brain structural and functional indices, along with their contributions to the prediction of demographic variables (age/sex) and clinical changes in schizophrenia patients. The present study enrolled 492 healthy people from the Southwest University Adult Lifespan Dataset (SALD) for demographic variable analysis and 39 patients with schizophrenia from the West China Hospital for treatment analysis. We conducted a model fit test with two variables (one voxel-based structural metric and another voxel-based functional metric) and then performed variance partitioning on the voxels that could be predicted sufficiently. Permutation tests were applied to compare the difference in contribution between each pair of structural and functional measurements. We found that voxel-based structural indices had stronger predictive value for age and sex, while voxel-based functional metrics showed stronger predictive value for treatment. Therefore, through variance partitioning, we could clearly and directly explore and compare the voxel-based structural and functional indices with respect to particular variables. In sum, for the variables reflecting long-term changes (age) and constant biological features (sex), the voxel-based structural metrics would contribute more than voxel-based functional metrics, but for the variable reflecting short-term changes (schizophrenia treatment), the functional metrics could contribute more.
Arbuscular mycorrhizal fungi (AMF) have demonstrated the potential to enhance the saline-alkali tolerance in plants. Nevertheless, the extent to which AMF can ameliorate the tolerance of ...salt-sensitive plants to alkaline conditions necessitates further investigation. The current study is primarily centered on elucidating the impact of AMF on the growth of the Huayu22 (H22) when cultivated in saline-alkaline soil. We leveraged DNA of rhizosphere microorganisms extracted from saline-alkali soil subjected to AMF treatment and conducted high-throughput sequencing encompassing 16S rRNA gene and ITS sequencing. Our findings from high-throughput sequencing unveiled Proteobacteria and
as the prevailing phylum and genus within the bacterial population, respectively. Likewise, the predominant fungal phylum and genus were identified as Ascomycota and
. It is noteworthy that the relative abundance of Proteobacteria, Actinobacteria, Chloroflexi, Bacteroidetes, and Ascomycota exhibited significant increments subsequent to AMF inoculation. Our investigation into soil enzyme activity revealed a remarkable surge post-AMF inoculation. Notably, the amounts of pathogen growth inhibitory enzymes and organic carbon degrading enzymes rise, as predicted by the putative roles of microbial communities. In saline-alkali soil, inoculation of AMF did boost the yield of H22. Notable improvements were observed in the weight of both 100 fruits and 100 grains, which increased by 20.02% and 22.30%, respectively. Conclusively, this study not only provides a theoretical framework but also furnishes empirical evidence supporting the utilization of AMF as a viable strategy for augmenting the yield of salt-sensitive plants grown in alkaline conditions.
Heading date is an important agronomic trait in rice (Oryza sativa L.); it determines the geographical and seasonal adaptability of the crop. Single segment substitution lines (SSSLs) have become the ...preferred experimental materials in mapping functional genetic variations as the particular chromosome segments from donor genotypes can be evaluated for their impact on the phenotype in a recurrent recipient background. The phenotypic differences can be attributed to the control of quantitative trait loci (QTLs). Here, we evaluated a library consisting of 1,123 SSSLs in the same genetic background of an elite rice variety, Huajingxian74 (HJX74), and revealed four SSSLs, W05-1-11-2-7-6 (W05), W08-16-3-2 (W08), W12-28-58-03-19-1 (W12), and W22-9-5-2-4-9-3 (W22), which had a significantly different heading date compared to HJX74. To further genetically dissect the QTLs controlling heading date on chromosomes 3, 6, and 10, four SSSLs were used to develop 15 secondary SSSLs with the smaller substituted segments. The qHD-3 heading date QTL detected in W05 and W08 was delimited to an interval of 4.15 cM, whereas qHD-6-1 and qHD-6-2 heading date QTLs dissected from the substituted segments in W12 were mapped to the intervals of 2.25-cM and 2.55-cM, respectively. The qHD-10 QTL detected on the substituted segment in W22 was mapped to an interval of 6.85-cM. The nucleotide and amino acid sequence changes for those genes in the secondary SSSLs were also revealed. The allele variations of those genes might contribute to the heading date QTLs on chromosome 3 (DTH3, OsDof12, and EHD4), chromosome 6 (Hd3a, Hd17, and RFT1), and chromosome 10 (Ehd1 and Ehd2). These sequence variations in heading date genes would be useful resources for further studying the function of genes, and would be important for rice breeding. Overall, our results indicate that secondary SSSLs were powerful tools for genetic dissection of QTLs and identification of differentiation in the genes.
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