We examined adaptations in nucleus accumbens (NAc) neurons in mouse and rat peripheral nerve injury models of neuropathic pain. Injury selectively increased excitability of NAc shell indirect pathway spiny projection neurons (iSPNs) and altered their synaptic connectivity. Moreover, injury-induced tactile allodynia was reversed by inhibiting and exacerbated by exciting iSPNs, indicating that they not only participated in the central representation of pain, but gated activity in ascending nociceptive pathways.

Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual’s tendency toward balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain’s functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition.

In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or “topics,” provide a sparse summary of the generative process behind the features for each individual. Topic modeling, an unsupervised generative model, has been used to map seemingly disparate features to a common domain. We use Non-Negative Matrix Factorization (NMF) to infer the latent structure of multimodal ADHD data containing fMRI, MRI, phenotypic and behavioral measurements. We compare four different NMF algorithms and find the sparsest decomposition is also the most differentiating between ADHD and healthy patients. We identify dimensions that map to interpretable, recognizable dimensions such as motion, default mode network activity, and other such features of the input data. For example, structural and functional graph theory features related to default mode subnetworks clustered with the ADHD inattentive diagnosis. Structural measurements of the default mode network (DMN) regions such as the posterior cingulate, precuneus, and parahippocampal regions were all related to the ADHD-Inattentive diagnosis. Ventral DMN subnetworks may have more functional connections in ADHD-I, while dorsal DMN may have less. We also find that ADHD topics may be dependent upon diagnostic site, raising the possibility of the diagnostic differences across geographic locations. We assess our findings in light of the ADHD-200 classification competition, and contrast our unsupervised, nominated topics with previously published supervised learning methods. Finally, we demonstrate the validity of these latent variables as biomarkers by using them for classification of ADHD in 730 patients. Cumulatively, this manuscript addresses how multi-modal data in ADHD can be interpreted by latent dimensions.

The notification process appears to achieve most of its aims in the majority of donors. Nevertheless, some donors did not understand that they were ineligible for future donation, and many donors were confused and upset. These data indicate that the adverse impact of notifying donors about abnormal test results needs to be considered when new blood donor screening tests and confirmatory algorithms are being licensed and implemented. Further studies of the effectiveness of newer revised donor notification materials are needed.

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Background Dexmedetomidine induces a sedative response that is associated with rapid arousal. To elucidate the underlying mechanisms, the authors hypothesized that dexmedetomidine increases the activity of dopaminergic neurons in the ventral tegmental area, and that this action contributes to the unique sedative properties of dexmedetomidine. Methods Only male mice were used. The activity of ventral tegmental area dopamine neurons was measured by a genetically encoded Ca2+ indicator and patch-clamp recording. Dopamine neurotransmitter dynamics in the medial prefrontal cortex and nucleus accumbens were measured by a genetically encoded dopamine sensor. Ventral tegmental area dopamine neurons were inhibited or activated by a chemogenetic approach, and the depth of sedation was estimated by electroencephalography. Results Ca2+ signals in dopamine neurons in the ventral tegmental area increased after intraperitoneal injection of dexmedetomidine (40 μg/kg; dexmedetomidine, 16.917 [14.882; 21.748], median [25%; 75%], vs. saline, –0.745 [–1.547; 0.359], normalized data, P = 0.001; n = 6 mice). Dopamine transmission increased in the medial prefrontal cortex after intraperitoneal injection of dexmedetomidine (40 μg/kg; dexmedetomidine, 10.812 [9.713; 15.104], median [25%; 75%], vs. saline, –0.498 [–0.664; –0.355], normalized data, P = 0.001; n = 6 mice) and in the nucleus accumbens (dexmedetomidine, 8.543 [7.135; 11.828], median [25%; 75%], vs. saline, –0.329 [–1.220; –0.047], normalized data, P = 0.001; n = 6 mice). Chemogenetic inhibition or activation of ventral tegmental area dopamine neurons increased or decreased slow waves, respectively, after intraperitoneal injection of dexmedetomidine (40 μg/kg; delta wave: two-way repeated measures ANOVA, F[2, 33] = 8.016, P = 0.002; n = 12 mice; theta wave: two-way repeated measures ANOVA, F[2, 33] = 22.800, P < 0.0001; n = 12 mice). Conclusions Dexmedetomidine activates dopamine neurons in the ventral tegmental area and increases dopamine concentrations in the related forebrain projection areas. This mechanism may explain rapid arousability upon dexmedetomidine sedation. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Neuropathic pain (NP) is a type of chronic pain that is different from the common type of pain. The mechanisms of NP are still poorly understood. Exploring the key genes and neurobiological changes in NP could provide important diagnostic and treatment tools for clinicians. GSE24982 is an mRNA-seq dataset that we downloaded from the Gene Expression Omnibus database to identify key genes in NP. Differentially expressed genes (DEGs) were identified using the BRB-ArrayTools software and R. Functional and pathway enrichment analyses of the DEGs were performed using Metascape. A protein–protein interaction network was created and visualized using Cytoscape. A total of 123 upregulated DEGs were obtained. Among these genes, p53 was the node with the highest degree; hence, we validated it experimentally using a chronic constriction injury mouse model. Our results showed that overexpression of the p53 gene, and the subsequent increase in caspase-3 expression, in dorsal root ganglion neurons led to increased apoptotic changes in these neurons. p53 may therefore be partly responsible for the development of chronic constriction injury-induced NP.

Objective. This study is aimed at investigating differences in local brain activity and functional connectivity (FC) between children with unilateral amblyopia and healthy controls (HCs) by using resting state functional magnetic resonance imaging (rs-fMRI). Methods. Local activity and FC analysis methods were used to explore the altered spontaneous brain activity of children with unilateral amblyopia. Local brain function analysis methods included the amplitude of low-frequency fluctuation (ALFF). FC analysis methods consisted of the FC between the primary visual cortex (PVC-FC) and other brain regions and the FC network between regions of interest (ROIs-FC) selected by independent component analysis. Results. The ALFF in the bilateral frontal, temporal, and occipital lobes in the amblyopia group was lower than that in the HCs. The weakened PVC-FC was mainly concentrated in the frontal lobe and the angular gyrus. The ROIs-FC between the default mode network, salience network, and primary visual cortex network (PVCN) were significantly reduced, whereas the ROIs-FC between the PVCN and the high-level visual cortex network were significantly increased in amblyopia. Conclusions. Unilateral amblyopia may reduce local brain activity and FC in the dorsal and ventral visual pathways and affect the top-down attentional control. Amblyopia may also alter FC between brain functional networks. These findings may help understand the pathological mechanisms of children with amblyopia.