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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.

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 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.

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 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.

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.

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.

Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.