Internet gaming disorder and tobacco use disorder share neural connectivity patterns between the subcortical and the motor network.

Internet gaming disorder (IGD) and tobacco use disorder (TUD) are globally common, non-substance-related disorders and substance-related disorders worldwide, respectively. Recognizing the commonalities between IGD and TUD will deepen understanding of the underlying mechanisms of addictive behavior and excessive online gaming. Using node strength, 141 resting-state data were collected in this study to compute network homogeneity. The participants included participants with IGD (PIGD: n = 34, male = 29, age: 15-25 years), participants with TUD (PTUD: n = 33, male = 33, age: 19-42 years), and matched healthy controls (control-for-IGD: n = 41, male = 38, age: 17-32 years; control-for-TUD: n = 33, age: 21-27 years). PIGD and PTUD exhibited common enhanced node strength between the subcortical and motor networks. Additionally, a common enhanced resting-state functional connectivity (RSFC) was found between the right thalamus and right postcentral gyrus in PIGD and PTUD. Node strength and RSFC were used to distinguish PIGD and PTUD from their respective healthy controls. Interestingly, models trained on PIGD versus controls could classify PTUD versus controls and vice versa, suggesting that these disorders share common neurological patterns. Enhanced connectivity may indicate a greater association between rewards and behaviors, inducing addiction behaviors without flexible and complex regulation. This study discovered that the connectivity between the subcortical and motor networks is a potential biological target for developing addiction treatment in the future.

Shorter TR combined with finer atlas positively modulate topological organization of brain network: A resting state fMRI study.

BACKGROUND: The use of shorter TR and finer atlases in rs-fMRI can provide greater detail on brain function and anatomy. However, there is limited understanding of the effect of this combination on brain network properties. METHODS: A study was conducted with 20 healthy young volunteers who underwent rs-fMRI scans with both shorter (0.5s) and long (2s) TR. Two atlases with different degrees of granularity (90 vs 200 regions) were used to extract rs-fMRI signals. Several network metrics, including small-worldness, Cp, Lp, Eloc, and Eg, were calculated. Two-factor ANOVA and two-sample t-tests were conducted for both the single spectrum and five sub-frequency bands. RESULTS: The network constructed using the combination of shorter TR and finer atlas showed significant enhancements in Cp, Eloc, and Eg, as well as reductions in Lp and γ in both the single spectrum and subspectrum (p < 0.05, Bonferroni correction). Network properties in the 0.082-0.1 Hz frequency range were weaker than those in the 0.01-0.082 Hz range. CONCLUSION: Our findings suggest that the use of shorter TR and finer atlas can positively affect the topological characteristics of brain networks. These insights can inform the development of brain network construction methods.

Involvement of the dorsal and ventral attention networks in visual attention span.

Visual attention span (VAS), which refers to the window size of multielement parallel processing in a short time, plays an important role in higher-level cognition (e.g., reading) as required by encoding large amounts of information input. However, it is still a matter of debate about the underlying neural mechanism of VAS. In the present study, a modified visual 1-back task was designed by using nonverbal stimuli and nonverbal responses, in which possible influences of target presence and position were considered to identify more pure VAS processing. A task-driven functional magnetic resonance imaging (fMRI) experiment was then performed, and 30 healthy adults participated in this study. Results of confirmatory and exploratory analyses consistently revealed that both dorsal attention network (DAN) and ventral attention network (VAN) were significantly activated during this visual simultaneous processing. In particular, more significant activation in the left superior parietal lobule (LSPL), as compared to that in the bilateral inferior frontal gyrus (IFGs), suggested a greater involvement of DAN in VAS-related processing in contrast to VAN. In addition, it was also found that the activation in temporoparietal junctions (TPJs) were suppressed during multielement processing only in the target-absent condition. The current results suggested the recruitment of LSPL in covert attentional shifts and top-down control of VAS resources distribution during the rapid visual simultaneous processing, as well as the involvement of bilateral IFGs (especially RIFG) in both VAS processing and inhibitory control. The present findings might bring some enlightenments for diagnosis of the atypicality of attentional disorders and reading difficulties.

Reproducibility of volume and asymmetry measurements of hippocampus, amygdala, and entorhinal cortex on traveling volunteers: a multisite MP2RAGE prospective study.

BACKGROUND: Multisite studies can considerably increase the pool of normally aging individuals with neurodegenerative disorders and thereby expedite the associated research. Understanding the reproducibility of the parameters of related brain structures-including the hippocampus, amygdala, and entorhinal cortex-in multisite studies is crucial in determining the impact of healthy aging or neurodegenerative diseases. PURPOSE: To estimate the reproducibility of the fascinating structures by automatic (FreeSurfer) and manual segmentation methods in a well-controlled multisite dataset. MATERIAL AND METHODS: Three traveling individuals were scanned at 10 sites, which were equipped with the same equipment (3T Prisma Siemens). They used the same scan protocol (two inversion-contrast magnetization-prepared rapid gradient echo sequences) and operators. Validity coefficients (intraclass correlations coefficient [ICC]) and spatial overlap measures (Dice Similarity Coefficient [DSC]) were used to estimate the reproducibility of multisite data. RESULTS: ICC and DSC values varied substantially among structures and segmentation methods, and values of manual tracing were relatively higher than the automated method. ICC and DSC values of structural parameters were greater than 0.80 and 0.60 across sites, as determined by manual tracing. Low reproducibility was observed in the amygdala parameters by automatic segmentation method (ICC = 0.349-0.529, DSC = 0.380-0.873). However, ICC and DSC scores of the hippocampus were higher than 0.60 and 0.65 by two segmentation methods. CONCLUSION: This study suggests that a well-controlled multisite study could provide a reliable MRI dataset. Manual tracing of volume assessments is recommended for low reproducibility structures that require high levels of precision in multisite studies.

Improved Gray Matter Atrophy Detection in Alzheimer Disease in Chinese Populations Using Chinese Brain Template.

OBJECTIVE: This study aimed to test the hypothesis that the statistical Chinese brain template would be more effective to detect gray matter (GM) changes in patients with Alzheimer disease (AD) in Chinese populations. MATERIALS AND METHODS: In total, 50 patients with AD and 50 sex-matched and age-matched healthy controls were included in this study. Chinese2020, a typical statistical Chinese brain template, and MNI152, a typical Caucasian template were used for spatial normalization respectively. The GM volume alterations in patients with AD were examined by using voxel-based morphometry with education level and total intracranial volume as nuisance variables. The GM proportions of the identified brain areas with group difference were compared. RESULTS: By using Chinese2020 and MNI152, significant GM atrophies in patients with AD were commonly detected in the bilateral medial temporal lobe, lateral temporal lobe, inferior/medial frontal cortex, as well as left thalamus. However, higher GM percentages of detected regions were acquired when Chinese2020 was used rather than MNI152. Furthermore, stronger statistical powers in the detected clusters were observed using Chinese2020 than MNI152. In addition, the laterality index analysis showed the bilateral atrophies with no hemispheric laterality in the para/hippocampus when using population-specific brain atlas (ie, Chinese2020). CONCLUSIONS: These findings indicated that applying the population-specific brain atlas to neuroimaging studies may achieve higher accuracy in activation detection. This may have implications to the imaging study of neurodegenerative diseases.

Robust multi-atlas label propagation by deep sparse representation.

Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of atlas image patches with similar anatomical structures. However, such assumption on image patch representation does not always hold in label fusion since (1) the image content within the patch may be corrupted due to noise and artifact; and (2) the distribution of morphometric patterns among atlas patches might be unbalanced such that the majority patterns can dominate label fusion result over other minority patterns. The violation of the above basic assumptions could significantly undermine the label fusion accuracy. To overcome these issues, we first consider forming label-specific group for the atlas patches with the same label. Then, we alter the conventional flat and shallow dictionary to a deep multi-layer structure, where the top layer (label-specific dictionaries) consists of groups of representative atlas patches and the subsequent layers (residual dictionaries) hierarchically encode the patchwise residual information in different scales. Thus, the label fusion follows the representation consensus across representative dictionaries. However, the representation of target patch in each group is iteratively optimized by using the representative atlas patches in each label-specific dictionary exclusively to match the principal patterns and also using all residual patterns across groups collaboratively to overcome the issue that some groups might be absent of certain variation patterns presented in the target image patch. Promising segmentation results have been achieved in labeling hippocampus on ADNI dataset, as well as basal ganglia and brainstem structures, compared to other counterpart label fusion methods.

Feature selection of fMRI data based on normalized mutual information and fisher discriminant ratio.

Pattern classification has been increasingly used in functional magnetic resonance imaging (fMRI) data analysis. However, the classification performance is restricted by the high dimensional property and noises of the fMRI data. In this paper, a new feature selection method (named as "NMI-F") was proposed by sequentially combining the normalized mutual information (NMI) and fisher discriminant ratio. In NMI-F, the normalized mutual information was firstly used to evaluate the relationships between features, and fisher discriminant ratio was then applied to calculate the importance of each feature involved. Two fMRI datasets (task-related and resting state) were used to test the proposed method. It was found that classification base on the NMI-F method could differentiate the brain cognitive and disease states effectively, and the proposed NMI-F method was prior to the other related methods. The current results also have implications to the future studies.

Disruption of cortical integration during midazolam-induced light sedation.

This work examines the effect of midazolam-induced light sedation on intrinsic functional connectivity of human brain, using a randomized, double-blind, placebo-controlled, cross-over, within-subject design. Fourteen healthy young subjects were enrolled and midazolam (0.03 mg/kg of the participant's body mass, to a maximum of 2.5 mg) or saline were administrated with an interval of one week. Resting-state fMRI was conducted before and after administration for each subject. We focus on two types of networks: sensory related lower-level functional networks and higher-order functions related ones. Independent component analysis (ICA) was used to identify these resting-state functional networks. We hypothesize that the sensory (visual, auditory, and sensorimotor) related networks will be intact under midazolam-induced light sedation while the higher-order (default mode, executive control, salience networks, etc.) networks will be functionally disconnected. It was found that the functional integrity of the lower-level networks was maintained, while that of the higher-level networks was significantly disrupted by light sedation. The within-network connectivity of the two types of networks was differently affected in terms of direction and extent. These findings provide direct evidence that higher-order cognitive functions including memory, attention, executive function, and language were impaired prior to lower-level sensory responses during sedation. Our result also lends support to the information integration model of consciousness.

Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions.

BACKGROUND: Intensity normalization is an important preprocessing step in brain magnetic resonance image (MRI) analysis. During MR image acquisition, different scanners or parameters would be used for scanning different subjects or the same subject at a different time, which may result in large intensity variations. This intensity variation will greatly undermine the performance of subsequent MRI processing and population analysis, such as image registration, segmentation, and tissue volume measurement. METHODS: In this work, we proposed a new histogram normalization method to reduce the intensity variation between MRIs obtained from different acquisitions. In our experiment, we scanned each subject twice on two different scanners using different imaging parameters. With noise estimation, the image with lower noise level was determined and treated as the high-quality reference image. Then the histogram of the low-quality image was normalized to the histogram of the high-quality image. The normalization algorithm includes two main steps: (1) intensity scaling (IS), where, for the high-quality reference image, the intensities of the image are first rescaled to a range between the low intensity region (LIR) value and the high intensity region (HIR) value; and (2) histogram normalization (HN),where the histogram of low-quality image as input image is stretched to match the histogram of the reference image, so that the intensity range in the normalized image will also lie between LIR and HIR. RESULTS: We performed three sets of experiments to evaluate the proposed method, i.e., image registration, segmentation, and tissue volume measurement, and compared this with the existing intensity normalization method. It is then possible to validate that our histogram normalization framework can achieve better results in all the experiments. It is also demonstrated that the brain template with normalization preprocessing is of higher quality than the template with no normalization processing. CONCLUSIONS: We have proposed a histogram-based MRI intensity normalization method. The method can normalize scans which were acquired on different MRI units. We have validated that the method can greatly improve the image analysis performance. Furthermore, it is demonstrated that with the help of our normalization method, we can create a higher quality Chinese brain template.

White matter atrophy in brain of neuromyelitis optica: a voxel-based morphometry study.

BACKGROUND: Recent studies have shown that tissue damage in neuromyelitis optica (NMO) is not limited to the spinal cord and optic nerve but can also appear in the brain. Previous magnetic resonance imaging (MRI) studies have reported controversial findings regarding the presence of white matter atrophy in NMO patients. PURPOSE: To investigate regional white matter changes in NMO using voxel-based morphometry (VBM). MATERIAL AND METHODS: Conventional MRI and T1-weighted three-dimensional MRI were performed on 20 patients with NMO and 20 age- and sex-matched normal controls (NCs). The data were analyzed by statistical parametric mapping 5 (SPM5) to generate white matter concentration maps, and regional white matter concentrations were compared between the two groups. Relationships between the white matter concentration in regions with significant group differences and the Expanded Disability Status Scale (EDSS) and disease duration were further explored. RESULTS: Compared to NCs, NMO patients had decreased white matter volumes in the right precentral gyrus, right postcentral gyrus, left middle and medial frontal gyrus, right superior frontal gyrus, bilateral inferior and superior parietal lobule, right angular gyrus, right middle occipital gyrus, and left precuneus. No significant correlation was found between white matter regions with volume reduction and the EDSS and disease duration in NMO. CONCLUSION: We found white matter atrophy in several brain regions in the frontal, parietal, and occipital lobes in NMO, suggesting that subtle white matter damage relevant to the motor, vision, and cognition systems exists in NMO patients. The pattern of white matter atrophy in NMO is different from that in multiple sclerosis (MS).

Is game-based therapy effective for treating cognitive deficits in adults with schizophrenia? Evidence from a randomized controlled trial.

Cognitive deficits in schizophrenia are a major contributor to poor functional outcomes and everyday functioning, making them a promising therapeutic target. Recent years have witnessed a dramatic increase in the use of digital interventions, such as game-based therapy, targeting various domains of cognition to treat mental disorders. Game-based digital interventions have been suggested to have therapeutic value in health care for people with schizophrenia. To support this idea, a novel, online training program (Komori Life) that targets cognitive deficits in schizophrenia was tested for feasibility of use and initial efficiency. Inpatients with schizophrenia were randomized to complete 20 sessions of either Komori Life (N = 40 completers) or treatment as usual (N = 40 completers). Cognitive and clinical assessments were performed at enrollment and after completion of the training intervention for all patients. In addition, 32 healthy volunteers were recruited as controls, and an eye-tracking paradigm was employed to assess attentional biases to emotional information before and after game intervention for all subjects. The results showed that there were no group differences in cognitive or clinical assessments at baseline between the two patient groups. After game training, there were still no group × time interactions on cognitive or clinical assessment scores. Regarding eye movement measurements, both patient groups showed increased attention to threatening stimuli compared to healthy controls in terms of attentional maintenance at baseline. After game training, the game training group revealed greater improvement in attentional bias towards threatening scenes (decreased percentage of total duration and percentage of total fixations towards threatening stimuli) relative to the treatment as usual group. Moreover, our results partially indicated that training effectiveness was associated with cognitive improvement and that heightened attentional maintenance to threats was associated with worse cognitive performance. This study provides initial evidence that a remote, online cognitive training program is feasible and effective in improving cognitive function in schizophrenia. This form of training may serve as a complementary therapy to existing psychiatric care. Clinical trial registration: the trial is registered at http://www.chictr.org.cn , identifier ChiCTR2100048403.

Never-Ending Learning for Explainable Brain Computing.

Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framework is proposed that employs the never-ending learning paradigm, integrating evidence combination and fusion computing within a Knowledge-Information-Data (KID) architecture. The framework supports continuous brain cognition investigation, utilizing joint knowledge-driven forward inference and data-driven reverse inference, bolstered by the pre-trained language modeling techniques and the human-in-the-loop mechanisms. In particular, it incorporates internal evidence learning through multi-task functional neuroimaging analyses and external evidence learning via topic modeling of published neuroimaging studies, all of which involve human interactions at different stages. Based on two case studies, the intricate uncertainty surrounding brain localization in human reasoning is revealed. The present study also highlights the potential of systematization to advance explainable brain computing, offering a finer-grained understanding of brain activity patterns related to human intelligence.

The influence of perceptual and semantic chunking on the neural mechanism of remote association.

Studies on remote association tests (RATs) have mainly focused on cognitive processes involved in searching for remote associations. However, factors affecting these search processes and remote associations remain unclear. In order to address this issue, this study compared non-chunking condition (e.g., "//") with perceptual chunking (two red color characters in the three-character item "//") and semantic chunking (high-frequency word-pair in the item; e.g., "//", "," literally "philosophy") conditions in the Chinese Remote Association Test (CRAT). The behavioral results on the CRAT found that the semantic ones resulted in significantly lower successful solving rates and longer response times than the other two conditions. The event-related potential (ERP) results showed that in contrast to the perceptual-chunking and the non-chunking condition, the semantic-chunking elicited enhanced P200, which might be related to the intuitive awareness of the mental fixation. However, relative to the non-chunking condition, the two chunking conditions evoked increased N400 and late positive component (LPC), indexing the late reflection and implementation of cognitive control. Our results suggest that it is the early awareness of the semantic chunk, rather than the general cognitive control process involved in representing and solving the semantically and perceptually chunk-induced interferences, that critically determines the final solving of RATs.

BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer's disease.

Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer's Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch training without the incremental learning capability. To address such limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing with multi-modal neuroimaging data through continuous learning. Specifically, we introduce the BNLoop-GAN (Loop-based Generative Adversarial Network for Brain Network) model, utilizing multiple techniques such as conditional generation, patch-based discrimination, and Wasserstein gradient penalty to learn the implicit distribution of brain networks. Moreover, a multiple-loop-learning algorithm is developed to combine evidence with better sample contribution ranking during training processes. The effectiveness of our approach is demonstrated through a case study on the classification of individuals with AD and healthy control groups using various experimental design strategies and multi-modal brain networks. The BNLoop-GAN model with multi-modal brain networks and multiple-loop-learning can improve classification performance.

Impulsivity-related right superior frontal gyrus as a biomarker of internet gaming disorder.

Background: Internet gaming disorder (IGD) is a mental health issue that affects individuals worldwide. However, the lack of knowledge about the biomarkers associated with the development of IGD has restricted the diagnosis and treatment of this disorder. Aims: We aimed to reveal the biomarkers associated with the development of IGD through resting-state brain network analysis and provide clues for the diagnosis and treatment of IGD. Methods: Twenty-six patients with IGD, 23 excessive internet game users (EIUs) who recurrently played internet games but were not diagnosed with IGD and 29 healthy controls (HCs) performed delay discounting task (DDT) and Iowa gambling task (IGT). Resting-state functional magnetic resonance imaging (fMRI) data were also collected. Results: Patients with IGD exhibited significantly lower hubness in the right medial orbital part of the superior frontal gyrus (ORBsupmed) than both the EIU and the HC groups. Additionally, the hubness of the right ORBsupmed was found to be positively correlated with the highest excessive internet gaming degree during the past year in the EIU group but not the IGD group; this might be the protective mechanism that prevents EIUs from becoming addicted to internet games. Moreover, the hubness of the right ORBsupmed was found to be related to the treatment outcome of patients with IGD, with higher hubness of this region indicating better recovery when undergoing forced abstinence. Further modelling analysis of the DDT and IGT showed that patients with IGD displayed higher impulsivity during the decision-making process, and impulsivity-related parameters were negatively correlated with the hubness of right ORBsupmed. Conclusions: Our findings revealed that the impulsivity-related right ORBsupmed hubness could serve as a potential biomarker of IGD and provide clues for the diagnosis and treatment of IGD.

Reduced loss aversion in value-based decision-making and edge-centric functional connectivity in patients with internet gaming disorder.

Background and aims: Impaired value-based decision-making is a feature of substance and behavioral addictions. Loss aversion is a core of value-based decision-making and its alteration plays an important role in addiction. However, few studies explored it in internet gaming disorder patients (IGD). Methods: In this study, IGD patients (PIGD) and healthy controls (Con-PIGD) performed the Iowa gambling task (IGT), under functional magnetic resonance imaging (fMRI). We investigated group differences in loss aversion, brain functional networks of node-centric functional connectivity (nFC) and the overlapping community features of edge-centric functional connectivity (eFC) in IGT. Results: PIGD performed worse with lower average net score in IGT. The computational model results showed that PIGD significantly reduced loss aversion. There was no group difference in nFC. However, there were significant group differences in the overlapping community features of eFC1. Furthermore, in Con-PIGD, loss aversion was positively correlated with the edge community profile similarity of the edge2 between left IFG and right hippocampus at right caudate. This relationship was suppressed by response consistency3 in PIGD. In addition, reduced loss aversion was negatively correlated with the promoted bottom-to-up neuromodulation from the right hippocampus to the left IFG in PIGD. Discussion and conclusions: The reduced loss aversion in value-based decision making and their related edge-centric functional connectivity support that the IGD showed the same value-based decision-making deficit as the substance use and other behavioral addictive disorders. These findings may have important significance for understanding the definition and mechanism of IGD in the future.

Baseline brain activity changes in patients with clinically isolated syndrome revealed by resting-state functional MRI.

BACKGROUND: A clinically isolated syndrome (CIS) is the first manifestation of multiple sclerosis (MS). Previous task-related functional MRI studies demonstrate functional reorganization in patients with CIS. PURPOSE: To assess baseline brain activity changes in patients with CIS by using the technique of regional amplitude of low frequency fluctuation (ALFF) as an index in resting-state fMRI. MATERIAL AND METHODS: Resting-state fMRIs data acquired from 37 patients with CIS and 37 age- and sex-matched normal controls were compared to investigate ALFF differences. The relationships between ALFF in regions with significant group differences and the EDSS (Expanded Disability Status Scale), disease duration, and T2 lesion volume (T2LV) were further explored. RESULTS: Patients with CIS had significantly decreased ALFF in the right anterior cingulate cortex, right caudate, right lingual gyrus, and right cuneus (P < 0.05 corrected for multiple comparisons using Monte Carlo simulation) compared to normal controls, while no significantly increased ALFF were observed in CIS. No significant correlation was found between the EDSS, disease duration, T2LV, and ALFF in regions with significant group differences. CONCLUSION: In patients with CIS, resting-state fMRI demonstrates decreased activity in several brain regions. These results are in contrast to patients with established MS, in whom ALFF demonstrates several regions of increased activity. It is possible that this shift from decreased activity in CIS to increased activity in MS could reflect the dynamics of cortical reorganization.

Perceived danger associated with a property modulates cross category generalization.

Identifying projectable predicates is a key issue in understanding inductive inference. It is proposed that looking into the evolutionary psychology literature for adaptive properties may be one useful approach. One hypothesis that emerges from this literature is that properties that signal danger or harm should be more salient than properties that do not. Two studies are carried out to test this hypothesis. In study 1 participants were presented with a scenario involving the discovery of novel animals, for which there was incomplete information. Three types of properties (a harmful property, a neutral property, a beneficial property) were associated with animals in one (base) category and participants were asked to indicate strength of generalization of the property to a target within the category, and to a target across to another category. In the second experiment, the procedure was repeated, but in addition, subjects were also explicitly asked to indicate whether the base and target belonged to the same or different categories. Study 1 showed that the harmful property was more projectable compared to the beneficial and neutral properties. Study 2 reconfirmed this and further showed that it also promoted excessive generalization across categories. The results suggest that examination of adaptations identified by evolutionary psychologists may be a useful source of insight in the study of inductive inference. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09793-3.

Systematic Fusion of Multi-Source Cognitive Networks With Graph Learning - A Study on Fronto-Parietal Network.

Cognitive tasks induce fluctuations in the functional connectivity between brain regions which constitute cognitive networks in the human brain. Although several cognitive networks have been identified, consensus still cannot be achieved on the precise borders and distribution of involved brain regions for each network, due to the multifarious use of diverse brain atlases in different studies. To address the problem, the current study proposed a novel approach to generate a fused cognitive network with the optimal performance in discriminating cognitive states by using graph learning, following the synthesization of one cognitive network defined by different brain atlases, and the construction of a hierarchical framework comprised of one main version and other supplementary versions of the specific cognitive network. As a result, the proposed method demonstrated better results compared with other machine learning methods for recognizing cognitive states, which was revealed by analyzing an fMRI dataset related to the mental arithmetic task. Our findings suggest that the fused cognitive network provides the potential to develop new mind decoding approaches.

Improving segmentation reliability of multi-scanner brain images using a generative adversarial network.

Background: Magnetic resonance (MR) images generated by different scanners generally have inconsistent contrast properties, making it difficult to perform a combined quantitative analysis of images from a range of scanners. In this study, we aimed to develop an automatic brain image segmentation model to provide a more reliable analysis of MR images taken with different scanners. Methods: The spatially localized atlas network tiles-27 (SLANT-27) deep learning model was used to train the automatic segmentation module, based on a multi-center dataset of 1,917 three-dimensional (3D) T1-weighted MR images. Subsequently, a framework called Qbrain, consisting of a new generative adversarial network (GAN) image transfer module and the SLANT-27 segmentation module, was developed. Another 3D T1-weighted MRI interscan dataset of 48 participants who were scanned in 3 MRI scanners (1.5T Siemens Avanto, 3T Siemens Trio Tim, and 3T Philips Ingenia) on the same day was used to train and test the Qbrain model. Volumetric T1-weighted images were processed with Qbrain, SLANT-27, and FreeSurfer (FS). The automatic segmentation reliability across the scanners was assessed using test-retest variability (TRV). Results: The reproducibility of different segmentation methods across scanners showed a consistent trend in the greater reliability and robustness of QBrain compared to SLANT-27 which, in turn, showed greater reliability and robustness compared to FS. Furthermore, when the GAN image transfer module was added, the mean segmentation error of the TRV of the 3T Siemens vs. 1.5T Siemens, the 3T Philips vs. 1.5T Siemens, and the 3T Siemens vs. 3T Philips scanners was reduced by 1.57%, 2.01%, and 0.56%, respectively. In addition, the segmentation model improved intra-scanner variability (0.9-1.67%) compared with that of FS (2.47-4.32%). Conclusions: The newly developed QBrain method combined with GAN image transfer module and a SLANT-27 segmentation module was shown to improve the reliability of whole-brain automatic structural segmentation results across multiple scanners, thus representing a suitable alternative quantitative method of comparative brain tissue analysis for individual patients.