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We investigated neural representations for visual perception of 10 handwritten digits and six visual objects from a convolutional neural network (CNN) and humans using functional magnetic resonance imaging (fMRI). Once our CNN model was fine-tuned using a pre-trained VGG16 model to recognize the visual stimuli from the digit and object categories, representational similarity analysis (RSA) was conducted using neural activations from fMRI and feature representations from the CNN model across all 16 classes. The encoded neural representation of the CNN model exhibited the hierarchical topography mapping of the human visual system. The feature representations in the lower convolutional (Conv) layers showed greater similarity with the neural representations in the early visual areas and parietal cortices, including the posterior cingulate cortex. The feature representations in the higher Conv layers were encoded in the higher-order visual areas, including the ventral/medial/dorsal stream and middle temporal complex. The neural representations in the classification layers were observed mainly in the ventral stream visual cortex (including the inferior temporal cortex), superior parietal cortex, and prefrontal cortex. There was a surprising similarity between the neural representations from the CNN model and the neural representations for human visual perception in the context of the perception of digits versus objects, particularly in the primary visual and associated areas. This study also illustrates the uniqueness of human visual perception. Unlike the CNN model, the neural representation of digits and objects for humans is more widely distributed across the whole brain, including the frontal and temporal areas., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The general psychopathology factor (p-factor) represents shared variance across mental disorders based on psychopathologic symptoms. The Adolescent Brain Cognitive Development (ABCD) Study offers an unprecedented opportunity to investigate functional networks (FNs) from functional magnetic resonance imaging (fMRI) associated with the psychopathology of an adolescent cohort (n > 10,000). However, the heterogeneities associated with the use of multiple sites and multiple scanners in the ABCD Study need to be overcome to improve the prediction of the p-factor using fMRI. We proposed a scanner-generalization neural network (SGNN) to predict the individual p-factor by systematically reducing the scanner effect for resting-state functional connectivity (RSFC). We included 6905 adolescents from 18 sites whose fMRI data were collected using either Siemens or GE scanners. The p-factor was estimated based on the Child Behavior Checklist (CBCL) scores available in the ABCD study using exploratory factor analysis. We evaluated the Pearson's correlation coefficients (CCs) for p-factor prediction via leave-one/two-site-out cross-validation (LOSOCV/LTSOCV) and identified important FNs from the weight features (WFs) of the SGNN. The CCs were higher for the SGNN than for alternative models when using both LOSOCV (0.1631 ± 0.0673 for the SGNN vs. 0.1497 ± 0.0710 for kernel ridge regression [KRR]; p < 0.05 from a two-tailed paired t-test) and LTSOCV (0.1469 ± 0.0381 for the SGNN vs. 0.1394 ± 0.0359 for KRR; p = 0.01). It was found that (a) the default-mode and dorsal attention FNs were important for p-factor prediction, and (b) the intra-visual FN was important for scanner generalization. We demonstrated the efficacy of our novel SGNN model for p-factor prediction while simultaneously eliminating scanner-related confounding effects for RSFC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Modulation of cigarette craving and neuronal activations from nicotine-dependent cigarette smokers using real-time functional MRI (rtfMRI)-based neurofeedback (rtfMRI-NF) has been previously reported., Objectives: The aim of this study was to evaluate the efficacy of rtfMRI-NF training in reducing cigarette cravings using fMRI data acquired before and after training., Methods: Treatment-seeking male heavy cigarette smokers (N = 14) were enrolled and randomly assigned to two conditions related to rtfMRI-NF training aiming at resisting the urge to smoke. In one condition, subjects underwent conventional rtfMRI-NF training using neuronal activity as the neurofeedback signal (activity-based) within regions-of-interest (ROIs) implicated in cigarette craving. In another condition, subjects underwent rtfMRI-NF training with additional functional connectivity information included in the neurofeedback signal (functional connectivity-added). Before and after rtfMRI-NF training at each of two visits, participants underwent two fMRI runs with cigarette smoking stimuli and were asked to crave or resist the urge to smoke without neurofeedback. Cigarette craving-related or resistance-related regions were identified using a general linear model followed by paired t-tests and were evaluated using regression analysis on the basis of neuronal activation and subjective craving scores (CRSs)., Results: Visual areas were mainly implicated in craving, whereas the superior frontal areas were associated with resistance. The degree of (a) CRS reduction and (b) the correlation between neuronal activation and CRSs were statistically significant (P < 0.05) in the functional connectivity-added neurofeedback group for craving-related ROIs., Conclusion: Our study demonstrated the feasibility of altering cigarette craving in craving-related ROIs but not in resistance-related ROIs via rtfMRI-NF training., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)

Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area of the input sample and moves across the sample to provide a feature map for the subsequent layers. In our study, we hypothesized that a 3D-CNN model with down-sampling operations such as pooling and/or stride would have the ability to extract robust feature maps from the shifted and scaled neuronal activations in a single functional MRI (fMRI) volume for the classification of task information associated with that volume. Thus, the 3D-CNN model would be able to ameliorate the potential misalignment of neuronal activations and over-/under-activation in local brain regions caused by imperfections in spatial alignment algorithms, confounded by variability in blood-oxygenation-level-dependent (BOLD) responses across sessions and/or subjects. To this end, the fMRI volumes acquired from four sensorimotor tasks (left-hand clenching, right-hand clenching, auditory attention, and visual stimulation) were used as input for our 3D-CNN model to classify task information using a single fMRI volume. The classification performance of the 3D-CNN was systematically evaluated using fMRI volumes obtained from various minimal preprocessing scenarios applied to raw fMRI volumes that excluded spatial normalization to a template and those obtained from full preprocessing that included spatial normalization. Alternative classifier models such as the 1D fully connected DNN (1D-fcDNN) and support vector machine (SVM) were also used for comparison. The classification performance was also assessed for several k-fold cross-validation (CV) schemes, including leave-one-subject-out CV (LOOCV). Overall, the classification results of the 3D-CNN model were superior to that of the 1D-fcDNN and SVM models. When using the fully-processed fMRI volumes with LOOCV, the mean error rates (± the standard error of the mean) for the 3D-CNN, 1D-fcDNN, and SVM models were 2.1% (± 0.9), 3.1% (± 1.2), and 4.1% (± 1.5), respectively (p = 0.041 from a one-way ANOVA). The error rates for 3-fold CV were higher (2.4% ± 1.0, 4.2% ± 1.3, and 10.1% ± 2.0; p < 0.0003 from a one-way ANOVA). The mean error rates also increased considerably using the raw fMRI 3D volume data without preprocessing (26.2% for the 3D-CNN, 75.0% for the 1D-fcDNN, and 75.0% for the SVM). Furthermore, the ability of the pre-trained 3D-CNN model to handle shifted and scaled neuronal activations was demonstrated in an online scenario for five-class classification (i.e., four sensorimotor tasks and the resting state) using the real-time fMRI of three participants. The resulting classification accuracy was 78.5% (± 1.4), 26.7% (± 5.9), and 21.5% (± 3.1) for the 3D-CNN, 1D-fcDNN, and SVM models, respectively. The superior performance of the 3D-CNN compared to the 1D-fcDNN was verified by analyzing the resulting feature maps and convolution filters that handled the shifted and scaled neuronal activations and by utilizing an independent public dataset from the Human Connectome Project., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest regarding this study, including financial, consultant, institutional, or other relationships. The sponsors had no involvement in the study design, data collection, analysis or interpretation of the data, manuscript preparation, or the decision to submit for publication., (Copyright © 2020. Published by Elsevier Inc.)

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background: Restricted Boltzmann machines (RBMs), including greedy layer-wise trained RBMs as part of a deep belief network (DBN), have the ability to identify spatial patterns (SPs; functional networks) in resting-state fMRI (rfMRI) data. However, there has been little research on (1) the reproducibility and test-retest reliability of SPs derived from RBMs and on (2) hierarchical SPs derived from DBNs., Methods: We applied a weight sparsity-controlled RBM and DBN to whole-brain rfMRI data from the Human Connectome Project. We evaluated the within-session reproducibility and between-session test-retest reliability of the SPs derived from the RBM approach and compared them both with those identified using independent component analysis (ICA) and with three voxel-wise statistical measures-the Hurst exponent, entropy, and kurtosis-of the rfMRI data. We also assessed the potential hierarchy of the SPs from the DBN., Results: An increase in the sparsity level of the RBM weights enhanced the reproducibility of the SPs. The SPs deriving from a stringent weight sparsity level were predominantly found in the cortical gray matter and substantially overlapped with the SPs obtained from the Hurst exponent. A hierarchical representation was shown by constructed using the default-mode network obtained from the DBN., Comparison With Existing Methods: The test-retest reliability of the SPs from the RBM was superior to that of the SPs from the voxel-wise statistics., Conclusions: The SPs from the RBM were reproducible within sessions and reliable across sessions. The hierarchically organized SPs from the DBN could possibly be applied to research based on rfMRI data., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The triple networks, namely the default-mode network (DMN), the central executive network (CEN), and the salience network (SN), play crucial roles in disorders of the brain, as well as in basic neuroscientific processes such as mindfulness. However, currently, there is no consensus on the underlying functional features of the triple networks associated with mindfulness. In this study, we tested the hypothesis that (a) the partial regression coefficient (i.e., slope): from the SN to the DMN, mediated by the CEN, would be one of the potential mindfulness features in the real-time functional magnetic resonance imaging (rtfMRI) neurofeedback (NF) setting, and (b) this slope level may be enhanced by rtfMRI-NF training. Sixty healthy mindfulness-naïve males participated in an MRI session consisting of two non-rtfMRI-runs, followed by two rtfMRI-NF runs and one transfer run. Once the regions-of-interest of each of the triple networks were defined using the non-rtfMRI-runs, the slope level was calculated by mediation analysis and used as neurofeedback information, in the form of a thermometer bar, to assist with participant mindfulness during the rtfMRI-NF runs. The participants were asked to increase the level of the thermometer bar while deploying a mindfulness strategy, which consisted of focusing attention on the physical sensations of breathing. rtfMRI-NF training was conducted as part of a randomized controlled trial design, in which participants were randomly assigned to either an experimental group or a control group. The participants in the experimental group received contingent neurofeedback information, which was obtained from their own brain signals, whereas the participants in the control group received non-contingent neurofeedback information that originated from matched participants in the experimental group. Our results indicated that the slope level from the SN to the DMN, mediated by the CEN, was associated with mindfulness score (rtfMRI-NF runs: r = 0.53, p = 0.007; p-value was corrected from 10,000 random permutations) and with task-performance feedback score (rtfMRI-NF run: r = 0.61, p = 0.001) in the experimental group only. In addition, during the rtfMRI-NF runs the level of the partial regression coefficient feature was substantially increased in the experimental group compared to the control group (p < 0.05 from the paired t-test; the p-value was corrected from 10,000 random permutations). To the best of our knowledge, this is the first study to demonstrate a partial regression coefficient feature of mindfulness in the rtfMRI-NF setting obtained by triple network mediation analysis, as well as the possibility of enhancement of the partial regression coefficient feature by rtfMRI-NF training., (Copyright © 2019 Elsevier Inc. All rights reserved.)

An artificial neural network with multiple hidden layers (known as a deep neural network, or DNN) was employed as a predictive model (DNN p ) for the first time to predict emotional responses using whole-brain functional magnetic resonance imaging (fMRI) data from individual subjects. During fMRI data acquisition, 10 healthy participants listened to 80 International Affective Digital Sound stimuli and rated their own emotions generated by each sound stimulus in terms of the arousal, dominance, and valence dimensions. The whole-brain spatial patterns from a general linear model (i.e., beta-valued maps) for each sound stimulus and the emotional response ratings were used as the input and output for the DNN P , respectively. Based on a nested five-fold cross-validation scheme, the paired input and output data were divided into training (three-fold), validation (one-fold), and test (one-fold) data. The DNN P was trained and optimized using the training and validation data and was tested using the test data. The Pearson's correlation coefficients between the rated and predicted emotional responses from our DNN P model with weight sparsity optimization (mean ± standard error 0.52 ± 0.02 for arousal, 0.51 ± 0.03 for dominance, and 0.51 ± 0.03 for valence, with an input denoising level of 0.3 and a mini-batch size of 1) were significantly greater than those of DNN models with conventional regularization schemes including elastic net regularization (0.15 ± 0.05, 0.15 ± 0.06, and 0.21 ± 0.04 for arousal, dominance, and valence, respectively), those of shallow models including logistic regression (0.11 ± 0.04, 0.10 ± 0.05, and 0.17 ± 0.04 for arousal, dominance, and valence, respectively; average of logistic regression and sparse logistic regression), and those of support vector machine-based predictive models (SVM p s; 0.12 ± 0.06, 0.06 ± 0.06, and 0.10 ± 0.06 for arousal, dominance, and valence, respectively; average of linear and non-linear SVM p s). This difference was confirmed to be significant with a Bonferroni-corrected p-value of less than 0.001 from a one-way analysis of variance (ANOVA) and subsequent paired t-test. The weights of the trained DNN P s were interpreted and input patterns that maximized or minimized the output of the DNN P s (i.e., the emotional responses) were estimated. Based on a binary classification of each emotion category (e.g., high arousal vs. low arousal), the error rates for the DNN P (31.2% ± 1.3% for arousal, 29.0% ± 1.7% for dominance, and 28.6% ± 3.0% for valence) were significantly lower than those for the linear SVM P (44.7% ± 2.0%, 50.7% ± 1.7%, and 47.4% ± 1.9% for arousal, dominance, and valence, respectively) and the non-linear SVM P (48.8% ± 2.3%, 52.2% ± 1.9%, and 46.4% ± 1.3% for arousal, dominance, and valence, respectively), as confirmed by the Bonferroni-corrected p < 0.001 from the one-way ANOVA. Our study demonstrates that the DNN p model is able to reveal neuronal circuitry associated with human emotional processing - including structures in the limbic and paralimbic areas, which include the amygdala, prefrontal areas, anterior cingulate cortex, insula, and caudate. Our DNN p model was also able to use activation patterns in these structures to predict and classify emotional responses to stimuli., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Perceptual learning refers to improved perceptual performance after intensive training and was initially suggested to reflect long-term plasticity in early visual cortex. Recent behavioral and neurophysiological evidence further suggested that the plasticity in brain regions related to decision making could also contribute to the observed training effects. However, how perceptual learning modulates the responses of decision-related regions in the human brain remains largely unknown. In the present study, we combined psychophysics and functional magnetic resonance imaging (fMRI), and adopted a model-based approach to investigate this issue. We trained participants on a motion direction discrimination task and fitted their behavioral data using the linear ballistic accumulator model. The results from model fitting showed that behavioral improvement could be well explained by a specific improvement in sensory information accumulation. A critical model parameter, the drift rate of the information accumulation, was correlated with the fMRI responses derived from three spatial independent components: ventral premotor cortex (PMv), supplementary eye field (SEF), and the fronto-parietal network, including intraparietal sulcus (IPS) and frontal eye field (FEF). In this decision network, we found that the behavioral training effects were accompanied by signal enhancement specific to trained direction in PMv and FEF. Further, we also found direction-specific signal reduction in sensory areas (V3A and MT+), as well as the strengthened effective connectivity from V3A to PMv and from IPS to FEF. These findings provide evidence for the learning-induced decision refinement after perceptual learning and the brain regions that are involved in this process.

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation., Competing Interests: The authors have no conflicts of interest regarding this study, including financial, consultant, institutional, or other relationships., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Osteopontin (OPN) is involved in the regulation of calcium precipitation in the brain pathology including ischemia. A 3-Nitropropionic acid (3NP) irreversibly inhibits mitochondrial complex II in the electron transport chain, with subsequent loss of transmembrane potential and calcium entry into the mitochondria. The present study examined the 3NP-induced calcium elevation in mitochondria and OPN expression in the 3NP-lesioned striatum. Rats were subcutaneously injected 3NP (15 mg÷kg) every other day for six weeks. Histological analysis, including the Hematoxylin-Eosin, Nissl, and Alizarin Red S stainings, was performed to examine the neurotoxic effects of 3NP. The expression of OPN in the striatum of 3NP-treated rats was investigated with immunohistochemistry and immunoelectron microscopy. In the striatal lesions, extensive loss of neurons and white matter bundles was detected. OPN was mainly detected in the penumbra region of the 3NP lesion. Scattered OPN expression was colocalized in the striatal neurons. After Alizarin Red S staining, the increase of calcium deposition was detected in the striatal lesions. In the electron microscopic analysis, the localization of OPN was clearly observed in the ultrastructure of mitochondria by immunoperoxidase and immunogold-silver staining techniques. Taken together, present findings suggest that calcium-induced mitochondrial swelling is highly associated with OPN expression. Thus, striatal calcium accumulation may be derived from 3NP-induced alteration in mitochondrial calcium homeostasis and pathologically associated with the induction of OPN protein.

Background: Itch is one of the most prevalent and familiar nociceptions in humans and can result in impaired quality of life. The central processing of itch is known to be important in perceptions of itch and strategies for coping with it, and various triggers have been reported to be associated with patterns of brain activation in pruritus. Stress is known to be an important aggravating factor in itch, but the precise mechanism by which brain activation results in stress-induced pruritus remains elusive., Methods: Using functional magnetic resonance imaging (fMRI), we attempted to evaluate if stress-induced pruritus is associated with the activation of certain areas of the brain. Furthermore, we investigated whether sedating antihistamines are more effective than non-sedating antihistamines in decreasing stress-induced pruritus. Memories of stressful events followed by visual stimuli using a series of pictures depicting fearful and stressful conditions, mixed with neutralizing pictures, were used to evoke pruritus., Results: The hippocampus and subcortical structures were found to be involved in stress-associated pruritus. Interestingly, sedating antihistamines were involved in the deactivation of brain regions associated with the relief of itch by active scratching, which suggests that sedating antihistamines may mitigate pruritus by instigating patterns of brain activity similar to those induced by scratching behavior., Conclusions: Itch scratching reflects a complicated pattern of brain activity. Activation of the hippocampus appears to be involved in stress-associated pruritus, and sedating antihistamines may work in a manner similar to that of scratching behavior. To our knowledge, this is the first study to evaluate brain functioning in relation to stress-induced pruritus., (© 2015 The International Society of Dermatology.)

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Real-time fMRI (rtfMRI) neurofeedback (NF) facilitates volitional control over brain activity and the modulation of associated mental functions. The NF signals of traditional rtfMRI-NF studies predominantly reflect neuronal activity within ROIs. In this study, we describe a novel rtfMRI-NF approach that includes a functional connectivity (FC) component in the NF signal (FC-added rtfMRI-NF). We estimated the efficacy of the FC-added rtfMRI-NF method by applying it to nicotine-dependent heavy smokers in an effort to reduce cigarette craving. ACC and medial pFC as well as the posterior cingulate cortex and precuneus are associated with cigarette craving and were chosen as ROIs. Fourteen heavy smokers were randomly assigned to receive one of two types of NF: traditional activity-based rtfMRI-NF or FC-added rtfMRI-NF. Participants received rtfMRI-NF training during two separate visits after overnight smoking cessation, and cigarette craving score was assessed. The FC-added rtfMRI-NF resulted in greater neuronal activity and increased FC between the targeted ROIs than the traditional activity-based rtfMRI-NF and resulted in lower craving score. In the FC-added rtfMRI-NF condition, the average of neuronal activity and FC was tightly associated with craving score (Bonferroni-corrected p = .028). However, in the activity-based rtfMRI-NF condition, no association was detected (uncorrected p > .081). Non-rtfMRI data analysis also showed enhanced neuronal activity and FC with FC-added NF than with activity-based NF. These results demonstrate that FC-added rtfMRI-NF facilitates greater volitional control over brain activity and connectivity and greater modulation of mental function than activity-based rtfMRI-NF.

Electroencephalography (EEG) data simultaneously acquired with functional magnetic resonance imaging (fMRI) data are preprocessed to remove gradient artifacts (GAs) and ballistocardiographic artifacts (BCAs). Nonetheless, these data, especially in the gamma frequency range, can be contaminated by residual artifacts produced by mechanical vibrations in the MRI system, in particular the cryogenic pump that compresses and transports the helium that chills the magnet (the helium-pump). However, few options are available for the removal of helium-pump artifacts. In this study, we propose a recursive approach of EEG-segment-based principal component analysis (rsPCA) that enables the removal of these helium-pump artifacts. Using the rsPCA method, feature vectors representing helium-pump artifacts were successfully extracted as eigenvectors, and the reconstructed signals of the feature vectors were subsequently removed. A test using simultaneous EEG-fMRI data acquired from left-hand (LH) and right-hand (RH) clenching tasks performed by volunteers found that the proposed rsPCA method substantially reduced helium-pump artifacts in the EEG data and significantly enhanced task-related gamma band activity levels (p=0.0038 and 0.0363 for LH and RH tasks, respectively) in EEG data that have had GAs and BCAs removed. The spatial patterns of the fMRI data were estimated using a hemodynamic response function (HRF) modeled from the estimated gamma band activity in a general linear model (GLM) framework. Active voxel clusters were identified in the post-/pre-central gyri of motor area, only from the rsPCA method (uncorrected p<0.001 for both LH/RH tasks). In addition, the superior temporal pole areas were consistently observed (uncorrected p<0.001 for the LH task and uncorrected p<0.05 for the RH task) in the spatial patterns of the HRF model for gamma band activity when the task paradigm and movement were also included in the GLM., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Accumulating evidence suggests a potential link between influenza A virus infection and the occurrence of influenza-associated neurological disorders. As influenza infection is mediated by specific receptors on the host cell surface, it is important to understand the distribution patterns of influenza receptors in target organs. We carried out comprehensive experiments to localize influenza receptors in the brains of two different mouse strains and the human brain for comparison using lectin histochemistry. We further compared the brain regions in which influenza receptors were expressed and the regions in which experimental influenza infection was observed. Our results show that the expression patterns for influenza receptors in mouse and human brains are different. In the mouse brain, human influenza virus receptors (HuIV-R) were expressed in part of brainstem and cerebellar white matter while avian influenza virus receptors (AIV-R) were expressed in the cerebellar Purkinje neurons. In contrast, in the human brain, many neurons and glia in widespread regions, including the cerebral cortex, hippocampus, brainstem, and cerebellum, express both AIV-R and HuIV-R. Importantly, vascular endothelial cells, choroid plexus epithelial cells and ependymal cells in both mouse and human brains express high levels of HuIV-R and AIV-R. The regional reciprocity was not observed when comparing regions with influenza receptor expression and the regions of influenza infection within the mouse brain. Our results demonstrate a differential influenza receptor expression pattern in mouse and human brains, and a disparity between influenza receptor distribution and regions with actual influenza infection., (Copyright © 2013 Elsevier B.V. All rights reserved.)

The identification of mild cognitive impairments (MCI) via either structural magnetic resonance imaging (sMRI) or functional MRI (fMRI) has great potential due to the non-invasiveness of the techniques. Furthermore, these techniques allow longitudinal follow-ups of single subjects via repeated measurements. sMRI- or fMRI-based biomarkers have been adopted separately to diagnose MCI; however, there has not been a systematic effort to integrate sMRI- and fMRI-based features to increase MCI detection accuracy. This study investigated whether the detection of MCI can be improved via the integration of biomarkers identified from both sMRI and fMRI modalities. Regional volume sizes and neuronal activity levels of brains from MCI subjects were compared with those from healthy controls and used to identify biomarkers from sMRI and fMRI data, respectively. In the subsequent classification phase, MCI was automatically detected using a support vector machine algorithm that employed the identified sMRI- and fMRI-based biomarkers as an input feature vector. The results indicate that the fMRI-based biomarkers provided more information for detecting MCI than the sMRI-based biomarkers. Moreover, the integrated feature sets using the sMRI- and fMRI-based biomarkers consistently showed greater detection accuracy than the feature sets based only on the fMRI-based biomarkers. The results demonstrate that integration of sMRI and fMRI modalities can provide supplemental information to improve the diagnosis of MCI relative to either the sMRI or fMRI modalities alone., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Nonnegative matrix factorization (NMF) is a blind source separation (BSS) algorithm which is based on the distinct constraint of nonnegativity of the estimated parameters as well as on the measured data. In this study, according to the potential feasibility of NMF for fMRI data, the four most popular NMF algorithms, corresponding to the following two types of (1) least-squares based update [i.e., alternating least-squares NMF (ALSNMF) and projected gradient descent NMF] and (2) multiplicative update (i.e., NMF based on Euclidean distance and NMF based on divergence cost function), were investigated by using them to estimate task-related neuronal activities. These algorithms were applied firstly to individual data from a single subject and, subsequently, to group data sets from multiple subjects. On the single-subject level, although all four algorithms detected task-related activation from simulated data, the performance of multiplicative update NMFs was significantly deteriorated when evaluated using visuomotor task fMRI data, for which they failed in estimating any task-related neuronal activities. In group-level analysis on both simulated data and real fMRI data, ALSNMF outperformed the other three algorithms. The presented findings may suggest that ALSNMF appears to be the most promising option among the tested NMF algorithms to extract task-related neuronal activities from fMRI data., (Copyright © 2013 Elsevier Inc. All rights reserved.)

The comorbidity of major depressive disorder (MDD) and cardiovascular disease (CVD) is among the 10th leading cause of morbidity and mortality worldwide. Thus, understanding the co-occurrence of these disorders will have major public health significance. MDD is associated with an abnormal stress response, manifested in brain circuitry deficits, gonadal dysfunction, and autonomic nervous system (ANS) dysregulation. Contribution of the relationships between these systems to the pathophysiology of MDD is not well understood. The objective of this preliminary study was to investigate, in parallel, relationships between HPG-axis functioning, stress response circuitry activation, and parasympathetic reactivity in healthy controls and women with MDD. Using fMRI with pulse oximetry [from which we calculated the high frequency (HF) component of R-R interval variability (HF-RRV), a measure of parasympathetic modulation] and hormone data, we studied eight women with recurrent MDD in remission and six controls during a stress response paradigm. We demonstrated that hypoactivations of hypothalamus, amygdala, hippocampus, anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), and subgenual ACC were associated with lower parasympathetic cardiac modulation in MDD women. Estradiol and progesterone attenuated group differences in the effect of HF-RRV on hypoactivation in the amygdala, hippocampus, ACC, and OFC in MDD women. Findings have implications for understanding the relationship between mood, arousal, heart regulation, and gonadal hormones, and may provide insights into MDD and CVD risk comorbidity., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

In this study, we investigated the efficacy of a real-time functional magnetic resonance imaging (rtfMRI)-based neurofeedback method for the modulation of the effective connectivity (EC) of causality between attention-related neuronal activities. In participants who received the feedback of attention-related neuronal activity, the EC estimated from Granger causality analysis was reinforced within the task-related network, such as between the bilateral cingulate gyri and frontal cortices, whereas the EC between the task-related network and task-unrelated resting-state network, including the inferior parietal lobule, was diminished. On the other hand, only marginal changes were observed in participants who received "sham" feedback. This "dynamic" characteristic measure of EC based on causality may be useful for evaluating the efficacy of methods designed to modulate brain networks, including rtfMRI-based neurofeedback., (Copyright © 2012 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.)

Intrinsic brain activity known as default-mode networks (DMNs) has been observed predominantly within the medial/superior frontal areas, anterior/posterior cingulate gyri, and precuneus using blood-oxygenation-level-dependent (BOLD) functional MRI (fMRI). Despite anecdotal evidence of distinct spatial patterns reflecting neuropsychiatric conditions in these DMNs, rigorous analysis of the characteristic traits of DMNs has been limited in previous studies. In this letter, the reproducibility and potential variability of the anterior and posterior DMNs were evaluated based on individual-level variations in effect sizes, activated areas, and causal interactions. Our results indicated that the DMNs were indeed reproducible between sessions/subjects. Region-specific traits were also observed: the posterior DMN seemed more robust to individual-level variations than the anterior DMN. The proposed analytical methods and reported findings may be useful in the development of a wide range of applications, including those involving clinical populations, which utilize the characteristic traits of DMNs., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

We demonstrated the in vivo feasibility of using focused ultrasound (FUS) to transiently modulate (through either stimulation or suppression) the function of regional brain tissue in rabbits. FUS was delivered in a train of pulses at low acoustic energy, far below the cavitation threshold, to the animal's somatomotor and visual areas, as guided by anatomical and functional information from magnetic resonance imaging (MRI). The temporary alterations in the brain function affected by the sonication were characterized by both electrophysiological recordings and functional brain mapping achieved through the use of functional MRI (fMRI). The modulatory effects were bimodal, whereby the brain activity could either be stimulated or selectively suppressed. Histological analysis of the excised brain tissue after the sonication demonstrated that the FUS did not elicit any tissue damages. Unlike transcranial magnetic stimulation, FUS can be applied to deep structures in the brain with greater spatial precision. Transient modulation of brain function using image-guided and anatomically-targeted FUS would enable the investigation of functional connectivity between brain regions and will eventually lead to a better understanding of localized brain functions. It is anticipated that the use of this technology will have an impact on brain research and may offer novel therapeutic interventions in various neurological conditions and psychiatric disorders., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The present study examined the effects of human umbilical cord blood-derived mesenchymal stem cells (HUCB-derived MSCs) delivered through the basilar artery in a canine thromboembolic brain ischemia model. Cerebral ischemia was induced through occlusion of the middle cerebral artery by injecting thrombus emboli into 10 beagles. In the HUCBC group (n = 5), 1 x 10(6) HUCB-derived MSCs were transplanted through the basilar artery 1 day after ischemic induction using an endovascular interventional approach. In the control group (n = 5), phosphate-buffered saline (PBS) was injected in the same manner in as the HUCBC group. Upon neurobehavioral examination, earlier recovery was observed in the HUCBC group. The HUCBC group showed a decrease in the infarction volume at 1 week after cerebral ischemic induction, whereas the control group showed an increase in the infarction volume at 1 week, by magnetic resonance image analysis. Transplanted cells had differentiated into neurons and astrocytes and were observed in and around endothelial cells that were positive for von Willebrand factor (vWF). HUCB-derived MSCs expressed neuroprotective factors, such as brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor (VEGF), at 4 weeks after the transplantation. The transplanted cells demonstrated their efficacy by reducing the infarction lesion volume and through earlier recovery from the neurological deficit. These results suggest that intraarterial transplantation of HUCB-derived MSCs could be useful in clinical treatment of cerebral ischemia., (Copyright 2009 Wiley-Liss, Inc.)

Automated interpretation and classification of functional MRI (fMRI) data is an emerging research field that enables the characterization of underlying cognitive processes with minimal human intervention. In this work, we present a method for the automated classification of human thoughts reflected on a trial-based paradigm using fMRI with a significantly shortened data acquisition time (less than one minute). Based on our preliminary experience with various cognitive imagery tasks, six characteristic thoughts were chosen as target tasks for the present work: right-hand motor imagery, left-hand motor imagery, right foot motor imagery, mental calculation, internal speech/word generation, and visual imagery. These six tasks were performed by five healthy volunteers and functional images were obtained using a T(*)(2)-weighted echo planar imaging (EPI) sequence. Feature vectors from activation maps, necessary for the classification of neural activity, were automatically extracted from the regions that were consistently and exclusively activated for a given task during the training process. Extracted feature vectors were classified using the support vector machine (SVM) algorithm. Parameter optimization, using a k-fold cross validation scheme, allowed the successful recognition of the six different categories of administered thought tasks with an accuracy of 74.5% (mean)+/-14.3% (standard deviation) across all five subjects. Our proposed study for the automated classification of fMRI data may be utilized in further investigations to monitor/identify human thought processes and their potential link to hardware/computer control.

We report an automated method to simultaneously monitor blood-oxygenation-level-dependent (BOLD) MR signals from multiple cortical areas in real-time. Individual brain anatomy was normalized and registered to a pre-segmented atlas in standardized anatomical space. Subsequently, using real-time fMRI (rtfMRI) data acquisition, localized BOLD signals were measured and displayed from user-selected areas labeled with anatomical and Brodmann's Area (BA) nomenclature. The method was tested on healthy volunteers during the performance of hand motor and internal speech generation tasks employing a trial-based design. Our data normalization and registration algorithm, along with image reconstruction, movement correction and a data display routine were executed with enough processing and communication bandwidth necessary for real-time operation. Task-specific BOLD signals were observed from the hand motor and language areas. One of the study participants was allowed to freely engage in hand clenching tasks, and associated brain activities were detected from the motor-related neural substrates without prior knowledge of the task onset time. The proposed method may be applied to various applications such as neurofeedback, brain-computer-interface, and functional mapping for surgical planning where real-time monitoring of region-specific brain activity is needed.

Background: Brain activation differs according to lesion location in functional magnetic resonance imaging (fMRI) studies, but lesion location-dependent electroencephalographic (EEG) alterations are unclear. Because of the increasing use of EEG-based brain-computer-interface rehabilitation, we examined lesion location-dependent EEG patterns in patients with stroke while they performed motor tasks.Methods: Twelve patients with chronic stroke were divided into three subgroups according to their lesion locations: supratentorial lesions that included M1 (SM1+), supratentorial lesions that excluded M1 (SM1-), and infratentorial (INF) lesions. Participants performed three motor tasks [active, passive, and motor imagery (MI)] with supination and grasping movements. The hemispheric asymmetric indexes, which were calculated with laterality coefficients (LCs), the temporal changes in the event-related desynchronization (ERD) patterns in the bilateral motor cortex, and the topographical distributions in the 28-channel EEG patterns around the supplementary motor area and bilateral motor cortex of the three participant subgroups were compared with those of the 12 age-matched healthy controls.Results: The SM1+ group exhibited negative LC values in the active and MI motor tasks, while the other patient subgroups exhibited positive LC values. Negative LC values indicate that the ERD/ERS intensity of the ipsilateral hemisphere is higher than the contralateral hemisphere, whereas positive LC values indicate that the ERD/ERS intensity of the contralateral hemisphere is higher than the ipsilateral hemisphere. The LC values of SM1+ and healthy controls differed significantly (rank-sum test, p < 0.05) in both the supination and grasping movements in the active task. The three patient subgroups differed distinctly from each other in the topography analysis.Conclusions: The hemispheric asymmetry and topographic characteristics of the beta band power patterns in the patients with stroke differed according to the location of the lesion, which suggested that EEG analyses of neurorehabilitation should be implemented according to lesion location. [ABSTRACT FROM AUTHOR]

The investigation of the reproducibility in functional MRI (fMRI) is an important step in the quantification and analysis of paradigm-related brain activation. This article reports on reproducibility of cortical activation characterized by repeated fMRI runs (10 times) during the performance of a motor imagery and a passive auditory stimulation as a control task. Two parameters, the size of activation and BOLD signal contrast, were measured from regions-of-interest for 10 subjects across different threshold conditions. The variability of these parameters was normalized with respect to the mean obtained from 10 runs, and represented as the intrasession variability. It was found that the variability was significantly lower in the measurement of BOLD signal contrast as compared to the measurement of the size of activation. The variability of the activation volume measurement was greater in the motor imagery task than in the auditory tasks across all thresholds. This task-dependent difference was not apparent from the measurement of the BOLD signal contrast. The presence of threshold dependence in the variability measurement was also examined, but no such dependency was found. The results suggest that a measurement of BOLD signal itself is a more reliable indicator of paradigm-related brain activation during repeated fMRI scans. [ABSTRACT FROM AUTHOR]

Abstract: Ethnopharmacological relevance: The fruits of Schisandra chinensis (Trucz.) Baill. (Schisandraceae) which have been used as a tonic especially for kidney yin deficiency in Chinese traditional medicine are recently receiving attention for its preventive activity on age-related neurodegenerative diseases. A variety of studies demonstrated the cognitive-enhancing effects of Schisandra chinensis through animal tests and also in clinical trials. Aim of study: In this study, we attempted to investigate the effects of the lignan-riched extract of Schisandra chinensis fruits (ESP-806) on neurotoxicity and memory impairment induced by Aβ1–42 injection in mice. Materials and methods: The fruits of Schisandra chinensis were extracted with the mixture of n-hexane:ethanol (9:1), which is riched with bioactive dibenzocyclooctadiene lignans, schizandrin, gomisin N, wuweigisu C. After oral treatment of ESP-806 (100mg/kg body weight) followed by injection of Aβ1–42 (2μg/mouse, i.c.v.), novel object recognition and passive avoidance tests were evaluated. To verify the cognition enhancing effects of ESP-806, we examined the effects of ESP-806 on the activities of β-secretase and acetylcholinesterase, and the contents of Aβ and the reduced glutathione within the cortex and hippocampus of Aβ-injected mice. Results: Oral treatment of ESP-806 (100mg/kg body weight) significantly attenuated Aβ1–42-induced memory impairment evaluated by behavioral tests. Furthermore, the treatment of ESP-806 attenuated the elevation of β-secretase activity accompanying the reduced level of Aβ1–42 in the cortex and hippocampus of the brain. ESP-806 also significantly inhibited the acetylcholinesterase activity in the hippocampus and increased the content of the reduced glutathione in the cortex and hippocampus of mouse brain. Conclusions: These data suggested that the extract of Schisandra chinensis fruits riched with dibenzocyclooctadiene lignans may be useful in the prevention and treatment of Alzheimer''s disease. [Copyright &y& Elsevier]