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Mood and anxiety disorders typically begin in adolescence and have overlapping clinical features but marked inter-individual variation in clinical presentation. The use of multimodal neuroimaging data may offer novel insights into the underlying brain mechanisms. We applied Heterogeneity Through Discriminative Analysis (HYDRA) to measures of regional brain morphometry, neurite density, and intracortical myelination to identify subtypes of youth, aged 9–10 years, with mood and anxiety disorders (N = 1931) compared to typically developing youth (N = 2823). We identified three subtypes that were robust to permutation testing and sample composition. Subtype 1 evidenced a pattern of imbalanced cortical-subcortical maturation compared to the typically developing group, with subcortical regions lagging behind prefrontal cortical thinning and myelination and greater cortical surface expansion globally. Subtype 2 displayed a pattern of delayed cortical maturation indicated by higher cortical thickness and lower cortical surface area expansion and myelination compared to the typically developing group. Subtype 3 showed evidence of atypical brain maturation involving globally lower cortical thickness and surface coupled with higher myelination and neural density. Subtype 1 had superior cognitive function in contrast to the other two subtypes that underperformed compared to the typically developing group. Higher levels of parental psychopathology, family conflict, and social adversity were common to all subtypes, with subtype 3 having the highest burden of adverse exposures. These analyses comprehensively characterize pre-adolescent mood and anxiety disorders, the biopsychosocial context in which they arise, and lay the foundation for the examination of the longitudinal evolution of the subtypes identified as the study sample transitions through adolescence.

The Bivalent Fear of Evaluation (BFEO) model posits that the fear of positive evaluation (FPE) is a core feature of social anxiety. As such, high socially anxious individuals may show attention bias when faced with positive stimuli. However, most of the previous studies focused on the negative attention bias of social anxiety, and less on the attention bias of positive stimuli. Meanwhile, the effect of stimulus presentation time on the attention bias pattern was unclear. In order to investigate this question, we used a dot-probe paradigm with facial expressions (happy, fearful, angry, neutral) presented for 100 ms and 500 ms. The ERP results showed: (1) For high socially anxious group, happy faces elicited a larger N1 for valid than for invalid cued probes, whereas for healthy control group, angry faces elicited a larger N1 for valid than for invalid cued probes. (2) When valid cues following happy faces presented for 500 ms, the N1 amplitude was larger than that of invalid cues. However, when valid cues following angry and fear faces presented for 100 ms, the N1 amplitude was larger than that of invalid cues. The results showed difficulty in attention disengagement of high socially anxious individuals from positive stimuli, as reflected by N1, illustrating the positive attention bias in social anxiety. These results prove that FPE may contribute to maintaining social anxiety.

Background There is significant heterogeneity in cognitive function in patients with bipolar I disorder (BDI); however, there is a dearth of research into biological mechanisms that might underlie cognitive heterogeneity, especially at disease onset. To this end, this study investigated the association between accelerated or delayed age-related brain structural changes and cognition in early-stage BDI. Methods First episode patients with BDI (n = 80) underwent cognitive assessment to yield demographically normed composite global and domain-specific scores in verbal memory, non-verbal memory, working memory, processing speed, attention, and executive functioning. Structural magnetic resonance imaging data were also collected from all participants and subjected to machine learning to compute the brain-predicted age difference (brainPAD), calculated by subtracting chronological age from age predicted by neuroimaging data (positive brainPAD values indicating age-related acceleration in brain structural changes and negative values indicating delay). Patients were divided into tertiles based on brainPAD values, and cognitive performance compared amongst tertiles with ANCOVA. Results Patients in the lowest (delayed) tertile of brainPAD values (brainPAD range −17.9 to −6.5 years) had significantly lower global cognitive scores (p = 0.025) compared to patients in the age-congruent tertile (brainPAD range −5.3 to 2.4 yrs), and significantly lower verbal memory scores (p = 0.001) compared to the age-congruent and accelerated (brainPAD range 2.8 to 16.1 yrs) tertiles. Conclusion These results provide evidence linking cognitive dysfunction in the early stage of BDI to apparent delay in typical age-related brain changes. Further studies are required to assess how age-related brain changes and cognitive functioning evolve over time.

BackgroundThe brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data, and is considered a biomarker of brain health. Understanding sex-differences in brainAGE is a significant step toward precision medicine.MethodsGlobal and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex-differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions.ResultsL-brainAGE showed sex-specific differences in brain ageing. In females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex-differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception for poor sleep quality, which was common to both. The most important predictor of higher G-brainAGE was non-white race in males and systolic blood pressure in females.ConclusionsThe results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain ageing and provide a foundation for targeted interventions.

Making strategies to maximize returns has always been the biggest problem almost every investor faces in the process of investment. In this paper, we will establish a model to analyze the internal characteristics of gold and bitcoin price data for further price prediction, which is of great help in formulation of trading strategy for the future. In Section 2 we hope to analyze the price trend of gold and bitcoin. At the beginning we establish a model based on ARIMA and get a relatively good prediction result. However, considering the errors may occur when the backward prediction unit gets long in this model, we try to use LSTM algorithm to better extract the nonlinear characteristics of the two time series of price. Through LSTM algorithm we get a much better result, and thus we choose LSTM as our final prediction model after comprehensive comparison. In Section 3, we use the predicted results to make trading strategies. We first divide investors into reckless and prudent types and establish models under different assumptions respectively. Then by applying greedy algorithm and establishing risk quantification model we approach the global optimal solution respectively. In the end, after testing the model and confirming its excellent performance, we summarize the advantages and shortcomings about this model comprehensively. Moreover, much more conclusions are drawn to analyze the possible model update in future.Price Prediction

Schizophrenia patients often suffer from deficit in theory of mind (TOM). Prior neuroimaging studies revealed neuroimaging correlates of TOM deficit in adults with schizophrenia, neuroimaging correlates of TOM in adolescents is less well established. This study aimed to investigate gray matter volume (GMV) abnormalities and TOM deficits in schizophrenic adolescents, and examine the relationship between them. Twenty adolescent schizophrenic patients and 25 age, sex-matched healthy controls underwent T1-weighted magnetic resonance imaging (MRI) scans, and were examined for TOM based on the Reading the Mind in the Eyes test (RMET). Univariate voxel-based morphometry (VBM) and multivariate source-based morphometry (SBM) were employed to examine alterations of two GMV phenotypes in schizophrenic adolescents: voxel-wise GMV and covarying structural brain patterns (SBPs). Compared with controls, our results revealed a significant deficit in RMET performance of the patients, Voxel-wise VBM analysis revealed that patients exhibited decreased GMV in bilateral insula, orbitofrontal cortex, and right rolandic operculum, and GMV of these brain regions were positively correlated with RMET performance. Multivariate SBM analysis identified a significantly different between-group SBP comprising of bilateral insula and inferior frontal cortex, bilateral superior temporal cortex, and bilateral lateral parietal cortex and right rolandic operculum. The loading scores of this SBP was positively correlated with RMET performance. This study revealed impairment of TOM ability in schizophrenic adolescents and revealed an association between TOM deficit and decreased GMV in regions which are crucial for social cognition, thereby provided insight and possible target regions for understanding the neural pathology and normalizing TOM deficit in adolescent schizophrenia patients.

Background: Normative modeling is a statistical approach to quantify the degree to which a particular individual-level measure deviates from the pattern observed in a normative reference population. When applied to human brain morphometric measures it has the potential to inform about the significance of normative deviations for health and disease. Normative models can be implemented using a variety of algorithms that have not been systematically appraised. Methods: To address this gap, eight algorithms were compared in terms of performance and computational efficiency using brain regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) collated from 87 international MRI datasets. Performance was assessed with the mean absolute error (MAE) and computational efficiency was inferred from central processing unit (CPU) time. The algorithms evaluated were Ordinary Least Squares Regression (OLSR), Bayesian Linear Regression (BLR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Parametric Lambda, Mu, Sigma (LMS), Gaussian Process Regression (GPR), Warped Bayesian Linear Regression (WBLG), Hierarchical Bayesian Regression (HBR), and Multivariable Fractional Polynomial Regression (MFPR). Model optimization involved testing nine covariate combinations pertaining to acquisition features, parcellation software versions, and global neuroimaging measures (i.e., total intracranial volume, mean cortical thickness, and mean cortical surface area). Findings: Statistical comparisons across models at PFDR

Background: Social anxious individuals show attention bias towards emotional stimuli, this phenomenon is considered to be an important cause of anxiety generation and maintenance. Cognitive-behavioral therapy (CBT) is a standard psychotherapy for social anxiety disorder. CBT decreases attention biases by correcting the maladaptive beliefs of socially anxious individuals, but it is not clear whether CBT alters neurophysiological features of socially anxious individuals at early automatic and/or late cognitive strategy stage of attentional processing.Method: To address this knowledge gap, we collected pre-treatment event-related potential data of socially anxious individuals while they performed a dot-probe task. These participants then received eight weeks of CBT, and post-treatment ERP data were collected after completion of CBT treatment. Results: Participants’ social anxiety level was significantly alleviated with CBT. ERP results revealed that (1) compared to pre-treatment phase, P1 amplitudes induced by probes significantly decreased at post-treatment phase, whereas P3 amplitudes increased at post-treatment phase; (2) amplitude of components elicited by face pairs did not change significantly between pre-treatment and post-treatment phases; (3) changes of Liebowitz Social Anxiety Scale were positively correlated with changes of P1 amplitude, and negatively correlated with changes of N1 amplitude.Limitations: Our sample was university students, which limits the generalizability of the results.Conclusion: The present results demonstrated that CBT alters ERPs appeared in probe-presenting stage for social anxiety, which suggested that key to the effective intervention was the adjustment of cognitive strategies in the later stage of attentional processing.

Background Magnetic Seizure therapy (MST) is an effective treatment for major depressive disorder (MDD) but its mechanism of action is not fully understood. The present study sought to characterize neuroimaging correlates of response and side effects of MST in a MDD cohort. Methods Fifteen severe MDD patients underwent a six-day course of MST treatment to the vertex. Before and after treatment, participants received rs-fMRI and structural MRI scans as well as assessments of depressive symptoms and neuropsychological functioning. 10 healthy volunteers received functional and structural MRI scans at similar time intervals. Results MST treatment was associated with increased functional connectivity between the subgenual anterior cingulate cortex (sgACC) and the parietal cortex, which positively correlated with clinical improvement. In contrast, greater decrease in functional connectivity between the right anterior hippocampus and the prefrontal cortex was correlated with lesser clinical and cognitive improvements. Changes in gray matter volume were evident in the bilateral parietal cortex, but were not associated with treatment outcomes. Limitations The sample size was small and results warrant replication. Conclusions This is the first quantitative fMRI study to investigate the neural correlates of MST treatment for MDD patients. While preliminary, these findings suggest that the modulation of sgACC activity is integral to the antidepressant mechanisms of MST. In contrast, changes in the hippocampus were not associated with symptom improvement, and appeared to contribute instead to side effects. Future studies in larger samples are warranted and explore the effect of e-electric field and correlates of response.

The study objective was to investigate the predictive value of functional connectivity changes induced by acute repetitive transcranial magnetic stimulation (rTMS) for clinical response in treatment-resistant depression.Cross-sectional changes in functional connectivity induced by a single concurrent rTMS-fMRI session were assessed in 38 outpatients with treatment-resistant depression (26 of them female; mean age, 41.87 years) who subsequently underwent a 4-week course of rTMS. rTMS was delivered at 1 Hz over the right dorsolateral prefrontal cortex. Acute rTMS-induced functional connectivity changes were computed and subjected to connectome-based predictive modeling to test their association with changes in score on the Montgomery-Åsberg Depression Rating Scale (MADRS) after rTMS treatment.TMS-fMRI induced widespread, acute, and transient alterations in functional connectivity. The rTMS-induced connectivity changes predicted about 30% of the variance of improvement in the MADRS score. The most robust predictive associations involved connections between prefrontal regions and motor, parietal, and insular cortices and between bilateral regions of the thalamus.Acute rTMS-induced connectivity changes in patients with treatment-resistant depression may index macro-level neuroplasticity, relevant to interindividual variability in rTMS treatment response. Large-scale network phenomena occurring during rTMS might be used to inform prospective clinical trials.

Background Cognitive impairment is a well-recognized symptom of major depressive disorder; however, contributing factors are not fully characterized. The present study examined the neurocognitive profiles and predictors of cognitive impairment in patients with treatment-resistant depression (TRD). Methods Moderate to severely depressed TRD patients were compared to matched healthy volunteers (HV) in verbal learning and recall and executive functions. Based on cognitive scores, cluster analysis was performed to identify subsets within the TRD sample. Predictors of cognitive impairment were also investigated. Results TRD patients showed worse performance in tests assessing verbal memory, executive attentional shifting, and inhibitory control. The cluster analysis revealed two groups: a cognitively impaired (CI) group that showed a generalized deficit across cognitive domains, and a relatively cognitively intact group that performed better than CI in all domains except attentional shifting. A logistic binomial regression of the two groups revealed three significant contributing risk factors for CI: 1) older age, 2) lower premorbid IQ, and 3) benzodiazepine use. Cognitive impairment and benzodiazepine use were associated with worse functioning. Conclusions Significant cognitive impairment is present in TRD and is associated with worse functioning. Age, lower premorbid IQ, and benzodiazepine use increased the likelihood of generalized cognitive impairment in TRD patients. The detrimental effect of benzodiazepine on cognitive impairment is independent of anxiety symptoms. Further research is needed to characterize the timeline of cognitive impairment in depression.

Background : Magnetic Seizure therapy (MST) is an emerging treatment for major depressive disorder (MDD) that is associated with fewer cognitive side effects compared to electroconvulsive therapy. The present pilot study sought to investigate whether daily MST treatments were associated to antidepressant effect and assess cognitive side effects associated with an accelerated MST (aMST) treatment schedule. Methods : Fifteen MDD patients underwent a six-day course of MST treatment to the vertex following assessment of symptom severity and neuropsychological testing. The primary outcome was severity on the Hamilton Rating Scale for Depression 17-item (HRSD-17). Patient also underwent neuropsychological assessment with the RBANS and Stroop Colour-Word test. Results : There were no instances of delirium or disturbance of consciousness following aMST sessions. Patients showed significant decreases on indices of depression and anxiety symptoms, with 9 (60%) patients showing a clinical response and 7 (47%) patients experiencing remission. Significant improvements were reported in RBANS total score, as well as indices of immediate memory and delayed memory. No changes at follow-up were reported for visuospatial/constructional, language, and attention RBANS indices, nor for Stroop Colour/Word performance. Limitations : The results should be interpreted with caution as they are part of a non-randomized, open-label pilot study. Further, the short duration of the study does not provide longitudinal follow-up to determine whether treatment response lasts a meaningful duration of time. Conclusions : aMST well tolerated without significant evidence of cognitive side effects and rapid improvement in symptoms. Further research is required to fully characterize these changes and replicate them in independent samples.

Background and objective Repetitive transcranial magnetic stimulation (rTMS) is a first-line treatment for treatment-resistant depression (TRD). The mechanisms of action of rTMS are not fully understood, and no biomarkers are available to assist in clinical practice to predict response to rTMS. This study aimed to demonstrate that after-rTMS clinical improvement is associated with functional connectivity (FC) changes of the subgenual cingulate cortex (sgACC) and rostral anterior cingulate (rACC), and FC of sgACC and rACC might serve as potential predictors for treatment response. Methods Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected within 1 week before rTMS initiation in 50 TRD patients to predict subsequent response to rTMS on the left dorsolateral prefrontal cortex (DLPFC). Follow-up rs-fMRI was obtained 12 weeks after completion of rTMS and neural correlates of rTMS in sgACC- and rACC-related FC patterns were compared to before rTMS data and with rs-fMRI from healthy participants. Results Treatment response was associated with lower FC of sgACC to right DLPFC and higher FC of rACC to left lateral parietal cortex (IPL) measured at baseline. Using sgACC-DLPFC and rACC-IPL connectivity as features, responder-nonresponder classification accuracies of 84% and 76% (end-of-treatment), 88% and 81% (3-month follow-up), respectively were achieved. Longitudinal rs-fMRI data analyses revealed that the hyperconnectivity between sgACC and visual cortex was normalized to a level which was comparable to that of healthy participants. Conclusions Brain activity patterns in depression are predictive of treatment response to rTMS, and longitudinal change of brain activity in relevant brain circuits after rTMS is associated with treatment response in depression. Target engagement paradigms may offer opportunities to increase the efficacy of rTMS in TRD by optimal selection of patients for treatment. Trial registration ClinicalTrials.gov Identifiers: NCT01887782 and NCT02800226 .

Understanding sex-related differences across the human cerebral cortex is an important step in elucidating the basis of psychological, behavioural and clinical differences between the sexes. Prior structural neuroimaging studies primarily focused on regional sex differences using univariate analyses. Here we focus on sex differences in cortical morphological networks (CMNs) derived using multivariate modelling of regional cortical measures of volume and surface from high-quality structural MRI scans from healthy participants in the Human Connectome Project (HCP) (n = 1,063) and the Southwest University Longitudinal Imaging Multimodal (SLIM) study (n = 549). The functional relevance of the CMNs was inferred using the NeuroSynth decoding function. Sex differences were widespread but not uniform. In general, females had higher volume, thickness and cortical folding in networks that involve prefrontal (both ventral and dorsal regions including the anterior cingulate) and parietal regions while males had higher volume, thickness and cortical folding in networks that primarily include temporal and posterior cortical regions. CMN loading coefficients were used as input features to linear discriminant analyses that were performed separately in the HCP and SLIM; sex was predicted with a high degree of accuracy (81%–85%) across datasets. The availability of behavioral data in the HCP enabled us to show that male-biased surface-based CMNs were associated with externalizing behaviors. These results extend previous literature on regional sex-differences by identifying CMNs that can reliably predict sex, are relevant to the expression of psychopathology and provide the foundation for the future investigation of their functional significance in clinical populations.

Repetitive transcranial magnetic stimulation (rTMS) is a first-line option for treatment-resistant depression (TRD), but prediction of treatment outcome remains a clinical challenge. The present study aimed to compare structural and functional covariance networks (SCNs and FCNs) between remitters and nonremitters. We determined the predictive capacities of SCNs and FCNs to discriminate the two groups. Fifty TRD patients underwent a course of rTMS to the left dorsolateral prefrontal cortex. They were categorized into remitters (n = 22) and nonremitters (n = 28) based on HDRS≤7 at the end of treatment. Baseline structural and functional magnetic imaging (sMRI and fMRI) of the patients and 42 healthy controls were collected. SCNs and FCNs were defined based on structural and functional covariance of gray mater volume (GMV) and fractional amplitude of low-frequency fluctuations (fALFF) from sMRI and fMRI, respectively. Structural/functional network integrity of these networks (default mode network [DMN], central executive network [CEN] and salience network [SN]) were compared between the three groups. In patients, associations between SCNs and FCNs with clinical improvements were studied using linear correlation analysis. Receiver-operating characteristic (ROC) analysis was conducted to confirm the utility of the SCNs and FCNs in classifying clinical sub-groups. Nonremitters exhibited lower structural integrity in CEN than remitters and controls. Higher structural integrity of CEN was related to clinical improvement (r = 0.423, p = .002), and structural integrity distinguished remitters and nonremitters with a fairly high accuracy (AUC = 0.71, p = .008). No group differences or correlation with clinical changes were found in FCNs. Results suggest the CEN may play a role mediating clinical improvement in rTMS for depression. Structural covariance networks may be features to consider in prediction of clinical improvement.

Transcranial magnetic stimulation (TMS) is a non-invasive and non-pharmacological intervention, approved for the treatment of individuals diagnosed with treatment-resistant depression. This well-tolerated approach uses magnetic pulses to stimulate specific brain regions and induce changes in brain networks at multiple levels of human functioning. Combining TMS with other neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), offers new insights into brain functioning, and allows to map out the causal alterations brought on by TMS interventions on neural network connectivity and behaviour. However, the implemention of concurrent TMS-fMRI brings on a number of technical challenges that must be overcome to ensure good quality of functional images. The goal of this study was thus to investigate the impact of TMS pulses in an MR-environment on the quality of BRAINO phantom images, in terms of the signal of the images, the temporal fluctuation noise, the spatial noise and the signal to fluctuation noise ratio, at the University of British Columbia (UBC) Neuroimaging facility. The results of our analyses replicated those of previous sites, and showed that the present set-up for concurrent TMS-fMRI ensures minimal noise artefact on functional images obtained through this multimodal approach. This step was a key stepping stone for future clinical trials at UBC.

Background and purpose Source-based morphometry(SBM) has been used in multicenter studies pooling magnetic resonance imaging data across different scanners to advance the reproducibility of neuroscience research. In the present study, we developed an analysis strategy for Scanner-Specific Detection (SS-Detect) of SBPs in multiscanner studies, and evaluated its performance relative to a conventional strategy. Methods In the first experiment, the SimTB toolbox was used to generate simulated datasets mimicking 20 different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical gray matter volume (GMV) datasets from two different scanners. Moreover, we applied two strategies to compare SBPs between schizophrenia patients' and healthy controls' GMV from two scanners. Results The outputs of the conventional strategy were limited to whole-sample-level results across all scanners; the outputs of SS-Detect included whole-sample-level and scanner-specific results. In the first simulation experiment, SS-Detect successfully estimated all simulated SBPs, including the common and scanner-specific SBPs, whereas the conventional strategy detected only some of the whole-sample SBPs. The second simulation experiment showed that both strategies could detect the simulated SBP. Quantitative evaluations of both experiments demonstrated greater accuracy of the SS-Detect in estimating spatial SBPs and subject-specific loading parameters. In the third experiment, SS-Detect detected more significant between-group SBPs, and these SBPs corresponded with the results from voxel-based morphometry analysis, suggesting that SS-Detect has higher sensitivity in detecting between-group differences. Conclusions SS-Detect outperformed the conventional strategy and can be considered advantageous when SBM is applied to a multiscanner study.

Background and PurposeSource-based morphometry (SBM) has been used in multi-centre studies pooling magnetic resonance imaging (MRI) data across different scanners to advance the reproducibility of neuroscience research. In the present study, we developed an analysis strategy for Scanner-Specific Detection (SS-Detect) of SBPs in multi-scanner studies, and evaluated its performance relative to a conventional strategy.MethodsIn the first experiment, the SimTB toolbox was used to generate simulated datasets mimicking twenty different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical gray matter volume (GMV) datasets from two different scanners. Moreover, we applied two strategies to compare SBPs between schizophrenia patients’ and healthy controls’ GMV from two different scanners.ResultsThe outputs of the conventional strategy were limited to whole-sample-level results across all scanners; the outputs of SS-Detect included whole-sample-level and scanner-specific results. In the first simulation experiment, SS-Detect successfully estimated all simulated SBPs, including the common and scanner-specific SBPs whereas the conventional strategy detected only some of the whole-sample SBPs. The second simulation experiment showed that both strategies could detect the simulated SBP. Quantitative evaluations of both experiments demonstrated greater accuracy of the SS-Detect in estimating spatial SBPs and subject-specific loading parameters. In the third experiment, SS-Detect detected more significant between-group SBPs, and these SBPs corresponded with the results from voxel-based morphometry analysis, suggesting that SS-Detect has higher sensitivity in detecting between-group differences.ConclusionsSS-Detect outperformed the conventional strategy and can be considered advantageous when SBM is applied to a multi-scanner study.

Background and methods Investigation of the insula may inform understanding of the etiopathogenesis of major depressive disorder (MDD). In the present study, we introduced a novel gray matter volume (GMV) based structural covariance technique, and applied it to a multi-centre study of insular subregions of 157 patients with MDD and 93 healthy controls from the Canadian Biomarker Integration Network in Depression (CAN-BIND, https://www.canbind.ca/ ). Specifically, we divided the unilateral insula into three subregions, and investigated their coupling with whole-brain GMV-based structural brain networks (SBNs). We compared between-group difference of the structural coupling patterns between the insular subregions and SBNs. Results The insula was divided into three subregions, including an anterior one, a superior-posterior one and an inferior-posterior one. In the comparison between MDD patients and controls we found that patients' right anterior insula showed increased inter-network coupling with the default mode network, and it showed decreased inter-network coupling with the central executive network; whereas patients' right ventral-posterior insula showed decreased inter-network coupling with the default mode network, and it showed increased inter-network coupling with the central executive network. We also demonstrated that patients' loading parameters of the right ventral-posterior insular structural covariance negatively correlated with their suicidal ideation scores; and controls' loading parameters of the right ventral-posterior insular structural covariance positively correlated with their motor and psychomotor speed scores, whereas these phenomena were not found in patients. Additionally, we did not find significant inter-network coupling between the whole-brain SBNs, including salience network, default mode network, and central executive network. Conclusions Our work proposed a novel technique to investigate the structural covariance coupling between large-scale structural covariance networks, and provided further evidence that MDD is a system-level disorder that shows disrupted structural coupling between brain networks.

Background Treatment resistant depression (TRD) remains a clinical challenge, and finding biomarkers that predict treatment response are a long sought goal to precisely indicate treatments. This pilot study aims to characterize brain dysfunction in TRD patients who underwent rTMS to define neuroimaging biomarkers that discriminate non-responders (NR) from responders (R). Methods 20 TRD patients who underwent a course of rTMS to the left DLPFC were categorized into R and NR groups based on a >50% reduction in HRSD scores. Utilizing resting-state fMRI and ICA techniques, this study compared baseline RSNs of R vs. NR as well as TRD vs. healthy volunteer group. Regression analysis was conducted to link regions with clinical improvements. ROC analysis was further conducted to confirm the utility of the identified regions in classifying the patients. Results Prior to treatment, non-responders displayed hyper-connectivity in ACC/VMPFC, PCC/pC, dACC and insula within RSNs that have been associated with MDD pathology. Regression results showed that regions associated with clinical improvements overlapped largely with regions that showed aberrant connectivity. ACC/VMPFC, dACC and left insula, which are hub regions of DMN and SN, exhibited excellent performance (highest sensitivity=100% and highest specificity=82%) in discriminating the response status of the patients. Limitations Relatively small sample size. Conclusions Our findings provide insight into fMRI predictive measures of treatment response to rTMS treatment, and demonstrate the potential of RSNs-based biomarkers in predicting response to rTMS treatment. Future studies are needed to validate the application of these measures to inform individual treatment indications.

Background and purpose Previous magnetic resonance spectroscopy (MRS) studies have concluded that hippocampal and parahippocampal metabolite concentrations remain stable during healthy adult aging. However, these studies used short repetition times (TR ≤ 2 seconds), which lead to incomplete longitudinal magnetization recovery, and thus, heavily T1 -weighted measurements. It is important to accurately characterize brain metabolites changes with age to enable appropriate interpretations of MRS findings in the context of neurodegenerative diseases. Our goal was to assess hippocampal brain metabolite concentrations in a large cohort of diversely aged healthy volunteers using a longer TR of 4 seconds. Methods Left hippocampal MR spectra were collected from 38 healthy volunteers at 3T. Absolute metabolite concentrations were determined for total N-acetyl-aspartate (tNAA), total creatine (tCr), total choline (tCho), glutamate and glutamine (Glx), and myoinositol (mI). Individual partial correlations between each metabolite with age were assessed using demographic information and voxel compartmentation as confounders. Results Hippocampal tNAA, tCr, tCho, and mI all increased with age (NAA: R2 = .17, P = .041; tCr: R2 = .45, P = .0002; tCho: R2 = .37, P = .001; mI: R2 = .44, P = .0003). There were no relationships between age and signal to noise ratio, linewidth, or scan date, indicating the correlations were not confounded by spectral quality. Furthermore, we did not observe a trend with age in the voxel tissue compartmentations. Conclusions We observed increases in hippocampal/parahippocampal metabolite concentrations with age, a finding that is in contrast to previous literature. Our findings illustrate the importance of using a sufficiently long TR in MRS to avoid T1 -relaxation effects influencing the measurement of absolute metabolite concentrations.

Transcranial direct stimulation, a non-invasive neurostimulation technique for modulating cortical excitability, and yoga have both respectively been shown to positively affect cognition. While preliminary research has shown that combined transcranial direct stimulation and meditation may have synergistic effects on mood and cognition, this was the first study to explore the combination of transcranial direct stimulation and yoga. Twenty-two healthy volunteers with a regular yoga practice were randomized to receive either active transcranial direct stimulation (anodal left, cathodal right dorsolateral prefrontal cortex) followed by yoga intervention or sham transcranial direct stimulation followed by yoga intervention a double-blind, cross-over design over two separate intervention days. Outcome measures included working memory performance, measured with the n-back task and mindfulness state, measured with the Toronto Mindfulness Scale, and were conducted offline, with pre-post assessments. Twenty participants completed both days of the intervention. Active transcranial direct stimulation did not have a significant effect on working memory or levels of mindfulness. There was a significant placebo effect, with better performance on day 1 of the intervention, irrespective of whether participants received active or sham transcranial direct stimulation. There was no significant difference between active versus sham transcranial direct stimulation concerning working memory performance and mindfulness, which may be accounted by the small sample size, the transient nature of the intervention, the fact that yoga and transcranial direct stimulation concerning were not conducted simultaneously, and the specific site of stimulation.

Part 7: Fault Diagnosis; International audience; Accumulating evidence suggests that the brain state has time-varying transitions, potentially implying that the brain functional networks (BFNs) have spatial variability and power-spectra dynamics over time. Recently, ICA-based BFNs tracking models, i.e., SliTICA, real-time ICA, Quasi-GICA, etc., have been gained wide attention. However, how to distinguish the neurobiological BFNs from those representing noise and artifacts is not trivial in tracking process due to the random order of components generated by ICA. In this study, combining with our previous BFNs tracking model, i.e., Quasi-GICA, we proposed a novel spatial-spectra dynamics-based ranking method for sorting time-varying BFNs, called weighted BFNs ranking, which was based on the dynamical properties in both spatial and spectral domains of each BFN. This proposed weighted BFNs ranking model mainly consisted of two steps: first, the dynamic spatial reproducibility (DSR) and dynamic fraction of amplitude low-frequency fluctuations (DFALFF) for each BFN were calculated; then a weighted coefficients-based ranking strategy for merging the DSR and DFALFF of each BFN was proposed, to make the meaningful dynamic BFNs rank ahead. We showed the effective results by this ranking model on the simulated and real data, suggesting that the meaningful dynamical BFNs with both strong properties of DSR and DFALFF across the tracking process were ranked at the top.

Independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data analysis. Although ICA assumes that the sources underlying data are statistically independent, it usually ignores sources’ additional properties, such as sparsity. In this study, we propose a two-step super-GaussianICA (2SGICA) method that incorporates the sparse prior of the sources into the ICA model. 2SGICA uses the super-Gaussian ICA (SGICA) algorithm that is based on a simplified Lewicki-Sejnowski’s model to obtain the initial source estimate in the first step. Using a kernel estimator technique, the source density is acquired and fitted to the Laplacian function based on the initial source estimates. The fitted Laplacian prior is used for each source at the second SGICA step. Moreover, the automatic target generation process for initial value generation is used in 2SGICA to guarantee the stability of the algorithm. An adaptive step size selection criterion is also implemented in the proposed algorithm. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of 2SGICA and made a performance comparison between InfomaxICA, FastICA, mean field ICA (MFICA) with Laplacian prior, sparse online dictionary learning (ODL), SGICA and 2SGICA. Both simulated and real fMRI experiments showed that the 2SGICA was most robust to noises, and had the best spatial detection power and the time course estimation among the six methods.

Numerous studies provide evidences that motor skill learning changes the activity of some brain regions during task as well as some resting networks during rest. However, it is still unclear how motor learning affects the resting-state default-mode network (DMN). Using functional magnetic resonance imaging, this study investigated the alteration of the DMN after motor skill learning with mental imagery practice. Fourteen participants in the experimental group learned to imagine a sequential finger movement over a two-week period while twelve control participants did not undergo motor imagery learning. For the experimental group, interregional connectivity, estimated by the graph theory method, between the medial temporal lobe, lateral temporal, and lateral parietal cortex within the DMN was increased after learning, whereas activity of the DMN network, estimated by the independent component analysis method, remained stable. Moreover, the experimental group showed significant improvement in motor performance after learning and a negative correlation between the alteration of the execution rate and changes in activity in the lateral parietal cortex. These results indicate that the DMN could be sculpted by motor learning in a manner of altering interregional connectivity and may imply that the DMN plays a role in improving behavioral performance.

Background Parcellating brain regions into functionally homogeneous subdivisions is critical for understanding normal and abnormal brain functions. New method In this study, we developed a new sparse representation-based parcellation method for functional magnetic resonance imaging (fMRI) data, and applied the new method to investigate functional insular subdivisions in treatment-resistant major depressive disorder (MDD). Realistic simulations were implemented to demonstrate the feasibility of the method. Subsequently, the method was used to parcellate the insula in a sample of fifty-six MDD patients and thirty-six healthy volunteers (HVs). The optimal number of clusters was determined by an independent test-retest dataset. Finally, differences of the functional connectivity profiles of each insular subdivision between patients and HVs were inspected. Results The results from both simulated and test-retest fMRI datasets demonstrated the feasibility of the proposed elastic net-based (EN) method. With the proposed method, the insula was parcellated into four subdivisions (dorsal anterior dAI; ventral anterior vAI; middle, MI and posterior, PI). Whereas patients showed hypo-connectivity between vAI and right medial temporal lobe, there were no functional volumetric differences in insular subdivisions between MDD patients and HVs. Comparison with existing method Results from both simulated and real fMRI datasets showed that the proposed EN method achieved higher accuracy than least absolute shrinkage and selection operator-based (LASSO) method. Conclusions These findings suggest that EN-based parcellation has the potential to be a useful addition to the parcellation techniques for fMRI data, and provide evidence of decreased functional connectivity without functional volumetric changes of the insula in treatment-resistant MDD.

Although the neurofeedback of real-time fMRI can reportedly enable people to gain control of the activity in the premotor cortex (PMA) during motor imagery, it is unclear how the neurofeedback training of PMA affect the motor network engaged in the motor execution (ME) and imagery (MI) task. In this study, we investigated the changes in the motor network engaged in both ME and MI task induced by real-time neurofeedback training of the right PMA. The neurofeedback training induced changes in activity of the ME-related motor network as well as alterations in the functional connectivity of both the ME-related and MI-related motor networks. Especially, the percent signal change of the right PMA in the last training run was found to be significantly correlated with the connectivity between the right PMA and the left posterior parietal lobe (PPL) during the pre-training MI run, post-training MI run and the last training run. Moreover, the increase in the tapping frequency was significantly correlated with the increase of connectivity between the right cerebellum and the primary motor area/primary sensory area (M1/S1) of the ME-related motor network after neurofeedback training. These findings show the importance of the connectivity between the right PMA and left PPL of the MI network for the up-regulation of the right PMA as well as the critical role of connectivity between the right cerebellum and M1/S1 of the ME network in improving the behavioral performance.

Independent component analysis (ICA) has been widely applied to reveal brain networks from fMRI data. Because the ICA method is based on a complete linear model, it may not work well for the fMRI data with very few time points. Lewicki and Sejnowski (Neural Comput 12:337–365, 2000 [2]) proposed an algorithm for learning an over-complete basis. However, the over-complete model has not been applied to the fMRI data analysis. In this study, we investigate the feasibility of the over-complete analysis method in resting fMRI data. Our results demonstrate that the resting brain networks can be obtained from the resting fMRI data with short time points via the over-complete analysis method.

Background As a blind source separation technique, independent component analysis (ICA) has many applications in functional magnetic resonance imaging (fMRI). Although either temporal or spatial prior information has been introduced into the constrained ICA and semi-blind ICA methods to improve the performance of ICA in fMRI data analysis, certain types of additional prior information, such as the sparsity, has seldom been added to the ICA algorithms as constraints. New method In this study, we proposed a SparseFastICA method by adding the source sparsity as a constraint to the FastICA algorithm to improve the performance of the widely used FastICA. The source sparsity is estimated through a smoothed l 0 norm method. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of SparseFastICA and made a performance comparison between SparseFastICA, FastICA and Infomax ICA. Results Results of the simulated and real fMRI data demonstrated the feasibility and robustness of SparseFastICA for the source separation in fMRI data. Comparison with existing methods Both the simulated and real fMRI experimental results showed that SparseFastICA has better robustness to noise and better spatial detection power than FastICA. Although the spatial detection power of SparseFastICA and Infomax did not show significant difference, SparseFastICA had faster computation speed than Infomax. Conclusions SparseFastICA was comparable to the Infomax algorithm with a faster computation speed. More importantly, SparseFastICA outperformed FastICA in robustness and spatial detection power and can be used to identify more accurate brain networks than FastICA algorithm.