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In this study we used functional magnetic resonance imaging to investigate age-related changes in large-scale brain functional networks during memory encoding and recognition in 12 younger and 16 older adults. For each participant, functional brain networks were constructed by computing temporal correlation matrices of 90 brain regions and analyzed using graph theoretical approaches. We found the age-related changes mainly in the long-range connections with widespread reductions associated with aging in the fronto-temporal and temporo-parietal regions, and a few age-related increases in the posterior parietal regions. Graph theoretical analysis revealed that the older adults had longer path lengths linking different regions in the functional brain networks as compared to the younger adults. Further analysis indicated that the increases in shortest path length in the networks were combined with the loss of long-range connections. Finally, we showed that for older adults, frontal areas played reduced roles in the network (reduced regional centrality), whereas several default-mode regions played increased roles relative to younger subjects (increased regional centrality). Together, our results suggest that normal aging is associated with disruption of large-scale brain systems during the performance of memory tasks, which provides novel insights into the understanding of age-related decline in multiple cognitive functions.

Selective attention has traditionally been viewed as a sensory processing modulator that promotes cognitive processing efficiency by favoring relevant stimuli while inhibiting irrelevant stimuli. However, the cross-modal processing of irrelevant information during working memory (WM) has been rarely investigated. In this study, the modulation of irrelevant auditory information by the brain during a visual WM task was investigated. The N100 auditory evoked potential (N100-AEP) following an auditory click was used to evaluate the selective attention to auditory stimulus during WM processing and at rest. N100-AEP amplitudes were found to be significantly affected in the left-prefrontal, mid-prefrontal, right-prefrontal, left-frontal, and mid-frontal regions while performing a high WM load task. In contrast, no significant differences were found between N100-AEP amplitudes in WM states and rest states under a low WM load task in all recorded brain regions. Furthermore, no differences were found between the time latencies of N100-AEP troughs in WM states and rest states while performing either the high or low WM load task. These findings suggested that the prefrontal cortex (PFC) may integrate information from different sensory channels to protect perceptual integrity during cognitive processing.

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

Anatomical deficits and resting-state functional connectivity (FC) alterations in prefrontal-thalamic-cerebellar circuit have been implicated in the neurobiology of schizophrenia. However, the effect of structural deficits in schizophrenia on causal connectivity of this circuit remains unclear. This study was conducted to examine the causal connectivity biased by structural deficits in first-episode, drug-naive schizophrenia patients. Structural and resting-state functional magnetic resonance imaging (fMRI) data were obtained from 49 first-episode, drug-naive schizophrenia patients and 50 healthy controls. Data were analyzed by voxel-based morphometry and Granger causality analysis. The causal connectivity of the integrated prefrontal-thalamic (limbic)-cerebellar (sensorimotor) circuit was partly affected by structural deficits in first-episode, drug-naive schizophrenia as follows: (1) unilateral prefrontal-sensorimotor connectivity abnormalities (increased driving effect from the left medial prefrontal cortex [MPFC] to the sensorimotor regions); (2) bilateral limbic-sensorimotor connectivity abnormalities (increased driving effect from the right anterior cingulate cortex [ACC] to the sensorimotor regions and decreased feedback from the sensorimotor regions to the right ACC); and (3) bilateral increased and decreased causal connectivities among the sensorimotor regions. Some correlations between the gray matter volume of the seeds, along with their causal effects and clinical variables (duration of untreated psychosis and symptom severity), were also observed in the patients. The findings indicated the partial effects of structural deficits in first-episode, drug-naive schizophrenia on the prefrontal-thalamic (limbic)-cerebellar (sensorimotor) circuit. Schizophrenia may reinforce the driving connectivities from the left MPFC or right ACC to the sensorimotor regions and may disrupt bilateral causal connectivities among the sensorimotor regions.

Resting-state EEG reflects intrinsic brain activity and its alteration represents changes in cognition that are related to neuropathology. Thereby, it provides a way of revealing the neurocognitive mechanisms underpinning chronic substance use. In addition, it is documented that some neurocognitive functions can recover following sustained abstinence. We present a systematic review to synthesize how chronic substance use is associated with resting-state EEG alterations and whether these spontaneously recover from abstinence. A literature search in Medline, PsycINFO, Embase, CINAHL, Web of Science, and Scopus resulted in 4088 articles, of which 57 were included for evaluation. It covered the substance of alcohol (18), tobacco (14), cannabis (8), cocaine (6), opioids (4), methamphetamine (4), and ecstasy (4). EEG analysis methods included spectral power, functional connectivity, and network analyses. It was found that long-term substance use with or without substance use disorder diagnosis was associated with broad intrinsic neural activity alterations, which were usually expressed as neural hyperactivation and decreased neural communication between brain regions. Some studies found the use of alcohol, tobacco, cocaine, cannabis, and methamphetamine was positively correlated with these changes. These alterations can partly recover from abstinence, which differed between drugs and may reflect their neurotoxic degree. Moderating factors that may explain results inconsistency are discussed. In sum, resting-state EEG may act as a potential biomarker of neurotoxic effects of chronic substance use. Recovery effects awaits replication in larger samples with prolonged abstinence. Balanced sex ratio, enlarged sample size, advanced EEG analysis methods, and transparent reporting are recommended for future studies.

In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity. The convolutional LSTM and 3D convolution are employed as network units to capture the long-term and short-term 3D properties respectively. To assemble these two kinds of spatial-temporal information and refine the deep learning outcomes, we further introduce an efficient graph-based node selection and label inference method. Experiments have been carried out on two publicly available databases and results demonstrate that the proposed method can obtain competitive performances as compared with other state-of-the-art methods.

BACKGROUND AND PURPOSE: Asymmetric hypointensity of cerebral veins on susceptibility-weighted imaging has been shown to indirectly reflect tissue hypoxia after cerebral ischemia. We therefore investigated whether patients with prominent asymmetry of the cerebral veins on SWI and a relatively small diffusion-weighted imaging lesion (SWI-DWI mismatch), representing the presence of salvageable tissue, were more likely to benefit from thrombolytic therapy. MATERIALS AND METHODS: We conducted a retrospective study of the anterior circulation of patients with ischemic stroke with SWI/DWI acquired before thrombolysis. The asymmetry index was defined as the ratio of cerebral vein voxel count between the ischemic and normal hemisphere on the SWI phase map. We defined SWI-DWI mismatch as an asymmetry index score of ≥1.75 with a DWI lesion volume of ≤25 mL. Favorable outcome was defined as modified Rankin Scale 0–2 at 3 months. Univariate and multivariate logistic regression analyses were used to examine the association between the mismatch profile and favorable outcome. RESULTS: Fifty-four patients undergoing thrombolytic treatment were enrolled in this study. The rate of favorable outcome was significantly higher among patients with baseline SWI-DWI mismatch compared with those without (78% versus 44%; adjusted odds ratio, 6.317; 95% CI, 1.12–35.80; P = .037). Patients with SWI-DWI mismatch were also more likely to have a favorable outcome from reperfusion (91% versus 43%, P = .033) or recanalization (100% versus 40%, P = .013). The accuracy of SWI-DWI mismatch for predicting favorable outcome was higher than that of perfusion-diffusion mismatch (63% versus 48.1%). CONCLUSIONS: The presence of SWI-DWI mismatch may identify patients with ischemia who would benefit from early reperfusion therapy.

Abstract Background Mesencephalic astrocyte-derived neurotrophic factor (MANF), a 20 kDa secreted protein, was originally derived from a rat mesencephalic type-1 astrocyte cell line. MANF belongs to a novel evolutionally conserved family of neurotrophic factors along with conserved dopamine neurotrophic factor. In recent years, ever-increasing evidence has shown that both of them play a remarkable protective role against various injuries to neurons in vivo or in vitro. However, the characteristics of MANF expression in the different types of glial cells, especially in astrocytes, remain unclear. Methods The model of focal cerebral ischemia was induced by rat middle cerebral artery occlusion. Double-labeled immunofluorescent staining was used to identify the types of neural cells expressing MANF. Primarily cultured glial cells were used to detect the response of glial cells to endoplasmic reticulum stress stimulation. Propidium iodide staining was used to determine dead cells. Reverse transcription PCR and western blotting were used to detect the levels of mRNA and proteins. Results We found that MANF was predominantly expressed in neurons in both normal and ischemic cortex. Despite its name, MANF was poorly expressed in glial cells, including astrocytes, in normal brain tissue. However, the expression of MANF was upregulated in the glial cells under focal cerebral ischemia, including the astrocytes. This expression was also induced by several endoplasmic reticulum stress inducers and nutrient deprivation in cultured primary glial cells. The most interesting phenomenon observed in this study was the pattern of MANF expression in the microglia. The expression of MANF was closely associated with the morphology and state of microglia, accompanied by the upregulation of BIP/Grp78. Conclusions These results indicate that MANF expression was upregulated in the activated glial cells, which may contribute to the mechanism of ischemia-induced neural injury.