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Anti‐N‐methyl‐d‐aspartate receptor (NMDAR) encephalitis shows a predilection for affecting the limbic system, but structural MRI in most patients is usually unremarkable. However, the functional connectivity reorganization of limbic nodes remains unknown. Serum neurofilament light chains (sNfL) are clinically linked with the disease severity and neurological disability of anti‐NMDAR encephalitis. However, the relationship between sNfL and limbic‐based functional architecture has not been explored. We consecutively recruited 20 convalescent patients with anti‐NMDAR encephalitis and 24 healthy controls from March 2018 to March 2021. Resting‐state functional MRI metrics, including fractional amplitude of low‐frequency fluctuation (fALFF), regional homogeneity (ReHo), and atlas‐based seed functional connectivity, were analyzed to investigate regional activities and functional connectivity alterations. Correlation analysis among functional connectivity, sNfL, Mini‐Mental State Examination (MMSE), and Montreal cognitive assessment outcomes were explored in patients. Compared with those of healthy controls, the fALFF and ReHo were consistently increased in regions of the posterior default mode network (DMN) hub, mainly the bilateral supramarginal gyrus and precuneus, in patients with anti‐NMDAR encephalitis (FWE‐corrected p < 0.05). Patients demonstrated disturbed functional organization characterized by reduced connectivity of the posterior DMN hub with the sensorimotor cortex and hypoconnectivity of the parahippocampal gyrus (PHG) with the right fusiform gyrus but extensively enhanced thalamocortical connectivity (FWE‐corrected p < 0.05). Furthermore, convalescent sNfL showed a positive correlation with enhanced thalamocortical connectivity (r = 0.4659, p = 0.0384). Onset sNfL with an independent linear correlation to convalescent MMSE performance (B coefficient, −0.013, 95% CI, −0.025 ~ −0.002, p = 0.0260) was positively correlated with intra‐DMN connectivity (r = 0.8969, p < 0.0001) and limbic‐sensory connectivity (r = 0.4866, p = 0.0346 for hippocampus seed and r = 0.5218, p = 0.0220 for PHG seed). Patients with anti‐NMDAR encephalitis demonstrated disturbed functional organization with substantial thalamocortical hyperconnectivity, that was positively correlated with convalescent sNfL. Onset sNfL showed a positive correlation with intra‐DMN connectivity and limbic‐sensory connectivity. [ABSTRACT FROM AUTHOR]

An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field. [ABSTRACT FROM AUTHOR]

The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel‐wise amplitude of low‐frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross‐validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r =.402, p <.001), and identified features within the subcortical‐cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross‐validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down. [ABSTRACT FROM AUTHOR]

Aims: Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Previous studies have demonstrated abnormalities in functional connectivity (FC) of AD under the assumption that FC is stationary during scanning. However, studies on the FC dynamics of AD, which may provide more insightful perspectives in understanding the neural mechanisms of AD, remain largely unknown. Methods: Combining the sliding‐window approach and the k‐means algorithm, we identified three reoccurring dynamic FC states from resting‐state fMRI data of 26 AD and 26 healthy controls. The between‐group differences both in FC states and in regional temporal variability were calculated, followed by a correlation analysis of these differences with cognitive performances of AD patients. Results: We identified three reoccurring FC states and found abnormal FC mainly in the frontal and temporal cortices. The temporal properties of FC states were changed in AD as characterized by decreased dwell time in State I and increased dwell time in State II. Besides, we found decreased regional temporal variability mainly in the somatomotor, temporal and parietal regions. Disrupted dynamic FC was significantly correlated with cognitive performances of AD patients. Conclusion: Our findings suggest abnormal dynamic FC in AD patients, which provides novel insights for understanding the pathophysiological mechanisms of AD. [ABSTRACT FROM AUTHOR]

Recently, functional connectome studies based on resting‐state functional magnetic resonance imaging (R‐fMRI) and graph theory have greatly advanced our understanding of the topological principles of healthy and diseased brains. However, how different strategies for R‐fMRI data preprocessing and for connectome analyses jointly affect topological characterization and contrastive research of brain networks remains to be elucidated. Here, we used two R‐fMRI data sets, a healthy young adult data set and an Alzheimer's disease (AD) patient data set, and up to 42 analysis strategies to comprehensively investigate the joint influence of three key factors (global signal regression, regional parcellation schemes, and null network models) on the topological analysis and contrastive research of whole‐brain functional networks. At the global level, we first found that these three factors affected not only the quantitative values but also the individual variability profile in small‐world related metrics and modularity, wherein global signal regression exhibited the predominant influence. Moreover, strategies without global signal regression and with topological randomization null model enhanced the sensitivity of the detection of differences between AD and control groups in small‐worldness and modularity. At the nodal level, strategies of global signal regression dominantly influenced the spatial distribution of both hubs and between‐group differences in terms of nodal degree centrality. Together, we highlight the remarkable joint influence of global signal regression, regional parcellation schemes and null network models on functional connectome analyses in both health and diseases, which may provide guidance for the choice of analysis strategies in future functional network studies. [ABSTRACT FROM AUTHOR]

Many studies have suggested that childhood maltreatment increase risk of adulthood major depressive disorder (MDD) and predict its unfavorable treatment outcome, yet the neural underpinnings associated with childhood maltreatment in MDD remain poorly understood. Here, we seek to investigate the whole-brain functional connectivity patterns in MDD patients with childhood maltreatment. Resting-state functional magnetic resonance imaging was used to explore intrinsic or spontaneous functional connectivity networks of 18 MDD patients with childhood neglect, 20 MDD patients without childhood neglect, and 20 healthy controls. Whole-brain functional networks were constructed by measuring the temporal correlations of every pairs of brain voxels and were further analyzed by using graph-theory approaches. Relative to the healthy control group, the two MDD patient groups showed overlapping reduced functional connectivity strength in bilateral ventral medial prefrontal cortex/ventral anterior cingulate cortex. However, compared with MDD patients without a history of childhood maltreatment, those patients with such a history displayed widespread reduction of functional connectivity strength primarily in brain regions within the prefrontal-limbic-thalamic-cerebellar circuitry, and these reductions significantly correlated with measures of childhood neglect. Together, we showed that the MDD groups with and without childhood neglect exhibited overlapping and segregated functional connectivity patterns in the whole-brain networks, providing empirical evidence for the contribution of early life stress to the pathophysiology of MDD. Hum Brain Mapp 35:1154-1166, 2014. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]