fMRI Task Research Articles

Meditation attenuates default-mode activity: A pilot study using ultra-high field 7 Tesla MRI

ObjectivesMapping the neurobiology of meditation has been bolstered by functional MRI (fMRI) research, with advancements in ultra-high field 7 Tesla fMRI further enhancing signal quality and neuroanatomical resolution. Here, we utilize 7 Tesla fMRI to examine the neural substrates of meditation and replicate existing widespread findings, after accounting for relevant physiological confounds. MethodsIn this feasibility study, we scanned 10 beginner meditators (N = 10) while they either attended to breathing (focused attention meditation) or engaged in restful thinking (non-focused rest). We also measured and adjusted the fMRI signal for key physiological differences between meditation and rest. Finally, we explored changes in state mindfulness, state anxiety and focused attention attributes for up to 2 weeks following the single fMRI meditation session. ResultsGroup-level task fMRI analyses revealed significant reductions in activity during meditation relative to rest in default-mode network hubs, i.e., antero-medial prefrontal and posterior cingulate cortices, precuneus, as well as visual and thalamic regions. These findings survived stringent statistical corrections for fluctuations in physiological responses which demonstrated significant differences (p < 0.05/n, Bonferroni controlled) between meditation and rest. Compared to baseline, State Mindfulness Scale (SMS) scores were significantly elevated (F(3,9) = 8.16, p < 0.05/n, Bonferroni controlled) following the fMRI meditation session, and were closely maintained at 2-week follow up. ConclusionsThis pilot study establishes the feasibility and utility of investigating focused attention meditation using ultra-high field (7 Tesla) fMRI, by supporting widespread evidence that focused attention meditation attenuates default-mode activity responsible for self-referential processing. Future functional neuroimaging studies of meditation should control for physiological confounds and include behavioural assessments.

Predicting an individual's cerebellar activity from functional connectivity fingerprints

The cerebellum is gaining scientific attention as a key neural substrate of cognitive function; however, individual differences in the cerebellar organization have not yet been well studied. Individual differences in functional brain organization can be closely tied to individual differences in brain connectivity. ‘Connectome Fingerprinting’ is a modeling approach that predicts an individual's brain activity from their connectome. Here, we extend ‘Connectome Fingerprinting’ (CF) to the cerebellum. We examined functional MRI data from 160 subjects (98 females) of the Human Connectome Project young adult dataset. For each of seven cognitive task paradigms, we constructed CF models from task activation maps and resting-state cortico-cerebellar functional connectomes, using a set of training subjects. For each model, we then predicted task activation in novel individual subjects, using their resting-state functional connectomes. In each cognitive paradigm, the CF models predicted individual subject cerebellar activity patterns with significantly greater precision than did predictions from the group average task activation. Examination of the CF models revealed that the cortico-cerebellar connections that carried the most information were those made with the non-motor portions of the cerebral cortex. These results demonstrate that the fine-scale functional connectivity between the cerebral cortex and cerebellum carries important information about individual differences in cerebellar functional organization. Additionally, CF modeling may be useful in the examination of patients with cerebellar dysfunction, since model predictions require only resting-state fMRI data which is more easily obtained than task fMRI.

Developmental Neuroimaging of Cognitive Flexibility: Update and Future Directions

Cognitive flexibility, or the ability to mentally switch between tasks according to changing environmental demands, supports optimal life outcomes, making it an important executive function to study across development. Here we review the literature examining the development of cognitive flexibility, with an emphasis on studies using task-based functional magnetic resonance imaging (fMRI). The neuroimaging literature suggests that key brain regions important for cognitive flexibility include the inferior frontal junction and regions within the midcingulo-insular network, including the insular and dorsal anterior cingulate cortices. We further discuss challenges surrounding studying cognitive flexibility during neurodevelopment, including inconsistent terminology, the diversity of fMRI task paradigms, difficulties with isolating cognitive flexibility from other executive functions, and accounting for developmental changes in cognitive strategy. Future directions include assessing how developmental changes in brain network dynamics enable cognitive flexibility and examining potential modulators of cognitive flexibility including physical activity and bilingualism.

Stress-Induced Sensitization of Insula Activation Predicts Alcohol Craving and Alcohol Use in Alcohol Use Disorder

BackgroundStress and alcohol cues trigger alcohol consumption and relapse in alcohol use disorder. However, the neurobiological processes underlying their interaction are not well understood. Thus, we conducted a randomized, controlled neuroimaging study to investigate the effects of psychosocial stress on neural cue reactivity and addictive behaviors. MethodsNeural alcohol cue reactivity was assessed in 91 individuals with alcohol use disorder using a validated functional magnetic resonance imaging (fMRI) task. Activation patterns were measured twice, at baseline and during a second fMRI session, prior to which participants were assigned to psychosocial stress (experimental condition) or a matched control condition or physical exercise (control conditions). Together with fMRI data, alcohol craving and cortisol levels were assessed, and alcohol use data were collected during a 12-month follow-up. Analyses tested the effects of psychosocial stress on neural cue reactivity and associations with cortisol levels, craving, and alcohol use. ResultsCompared with both control conditions, psychosocial stress elicited higher alcohol cue–induced activation in the left anterior insula (familywise error–corrected p < .05) and a stress- and cue-specific dynamic increase in insula activation over time (F22,968 = 2.143, p = .007), which was predicted by higher cortisol levels during the experimental intervention (r = 0.310, false discovery rate–corrected p = .016). Cue-induced insula activation was positively correlated with alcohol craving during fMRI (r = 0.262, false discovery rate–corrected p = .032) and alcohol use during follow-up (r = 0.218, false discovery rate–corrected p = .046). ConclusionsResults indicate a stress-induced sensitization of cue-induced activation in the left insula as a neurobiological correlate of the effects of psychosocial stress on alcohol craving and alcohol use in alcohol use disorder, which likely reflects changes in salience attribution and goal-directed behavior.

Functional brain network identification and fMRI augmentation using a VAE-GAN framework

Recently, deep learning models have achieved superior performance for mapping functional brain networks from functional magnetic resonance imaging (fMRI) data compared with traditional methods. However, due to the lack of sufficient data and the high dimensionality of brain volume, deep learning models of fMRI tend to suffer from overfitting. In addition, existing methods rarely studied fMRI data augmentation and its application. To address these issues, we developed a VAE-GAN framework that combined a VAE (variational auto-encoder) with a GAN (generative adversarial net) for functional brain network identification and fMRI augmentation. As a generative model, the VAE-GAN models the distribution of fMRI so that it enables the extraction of more generalized features, and thus relieve the overfitting issue. The VAE-GAN is easier to train on fMRI than a standard GAN since it uses latent variables from VAE to generate fake data rather than relying on random noise that is used in a GAN, and it can generate higher quality of fake data than VAE since the discriminator can promote the training of the generator. In other words, the VAE-GAN inherits the advantages of VAE and GAN and avoids their limitations in modeling of fMRI data. Extensive experiments on task fMRI datasets from HCP have proved the effectiveness and superiority of the proposed VAE-GAN framework for identifying both temporal features and functional brain networks compared with existing models, and the quality of fake data is higher than those from VAE and GAN. The results on resting state fMRI of Attention Deficit Hyperactivity Disorder (ADHD)-200 dataset further demonstrated that the fake data generated by the VAE-GAN can help improve the performance of brain network modeling and ADHD classification.

Activation Map Reveals Language Impairment in Children with Benign Epilepsy with Centrotemporal Spikes (BECTS).

Neuropsychological evidence revealed language impairment in children with benign epilepsy with centrotemporal spikes (BECTS). This study investigates language function using task-activated fMRI. We conducted a language task fMRI study on three groups on a 3.0T MRI scanner, including a new onset drug naïve group (NODN-BECTS, n=11, age=9.6±1.6), an established epilepsy with medication-treated group (Med-BECTS, n=17, age=10.7±2.2) and a healthy control group (HC, n=18, age=10.8±1.7). We use MATLAB14 and SPM12 to pre-process and analyze the data. A one-sample t-test was used to identify task-related brain activation changes in each group, based on the general linear model (GLM). And, then two sample t-test was performed to compare different activated regions between groups. In addition, scores on the most recent Mandarin school exams were acquired to examine and contrast extra-scanner language performance. Statistical results show that some language-related brain regions (such as the left superior frontal gyrus and cerebellar vermis) were additionally activated in the NODN-BECTS group compared with the HC group. Compared with NODN-BECTS and HC groups, decreased activations were found in language-related regions in the Med-BECTS group, including the left insula, superior and middle frontal gyri, and bilateral middle occipital gyri. On the Mandarin school exams, the average score for HC was 87.3±8.2, NODN was 84.8±7.8, and Med was 78.2±13.2. There was a trend toward statistical significance between the Med and the HC (p = 0.074) as well as NODN (p = 0.092) groups. No statistically significant differences were found between the HC and the NODN-BECTS groups. Language task fMRI reveals additional areas of activation in new onset BECTS compared to healthy controls which may be compensatory in nature. Antiseizure medications (ASMs) and/or longer duration of BECTS additionally appears to affect language-related regions and reduce their functional ability.

Brain Networks and Adolescent Alcohol Use

Alcohol misuse in adolescence is a leading cause of disability and mortality in youth and is associated with higher risk for alcohol use disorder. Brain mechanisms underlying risk of alcohol misuse may inform prevention and intervention efforts. To identify neuromarkers of alcohol misuse using a data-driven approach, with specific consideration of neurodevelopmental sex differences. Longitudinal multisite functional magnetic resonance imaging (fMRI) data collected at ages 14 and 19 years were used to assess whole-brain patterns of functional organization associated with current and future alcohol use risk as measured by the Alcohol Use Disorder Identification Test (AUDIT). Primary data were collected by the IMAGEN consortium, a European multisite study of adolescent neurodevelopment. Model generalizability was further tested using data acquired in a single-site study of college alcohol consumption conducted in the US. The primary sample was a developmental cohort of 1359 adolescents with neuroimaging, phenotyping, and alcohol use data. Model generalizability was further assessed in a separate cohort of 114 individuals. Brain-behavior model accuracy, as defined by the correspondence between model-predicted and actual AUDIT scores in held-out testing data, Bonferroni corrected across the number of models run at each time point, 2-tailed α < .008, as determined via permutation testing. Among 1359 individuals in the study, the mean (SD) age was 14.42 (0.40) years, and 729 individuals (54%) were female. The data-driven, whole-brain connectivity approach identified networks associated with vulnerability for future and current AUDIT-defined alcohol use risk (primary outcome, as specified above, future: ρ, 0.22; P < .001 and present: ρ, 0.27; P < .001). Results further indicated sex divergence in the accuracies of brain-behavior models, such that female-only models consistently outperformed male-only models. Specifically, female-only models identified networks conferring vulnerability for future and current severity using data acquired during both reward and inhibitory fMRI tasks. In contrast, male-only models were successful in accurately identifying networks using data acquired during the inhibitory control-but not reward-task, indicating domain specificity of alcohol use risk networks in male adolescents only. These data suggest that interventions focusing on inhibitory control processes may be effective in combating alcohol use risk in male adolescents but that both inhibitory and reward-related processes are likely of relevance to alcohol use behaviors in female adolescents. They further identify novel networks of alcohol use risk in youth, which may be used to identify adolescents who are at risk and inform intervention efforts.

Prenatal Exposure to Maternal Mood Entropy Is Associated With a Weakened and Inflexible Salience Network in Adolescence

BackgroundFetal exposure to maternal mood dysregulation influences child cognitive and emotional development, which may have long-lasting implications for mental health. However, the neurobiological alterations associated with this dimension of adversity have yet to be explored. Here, we tested the hypothesis that fetal exposure to entropy, a novel index of dysregulated maternal mood, would predict the integrity of the salience network, which is involved in emotional processing. MethodsA sample of 138 child-mother pairs (70 females) participated in this prospective longitudinal study. Maternal negative mood level and entropy (an index of variable and unpredictable mood) were assessed 5 times during pregnancy. Adolescents engaged in a functional magnetic resonance imaging task that was acquired between 2 resting-state scans. Changes in network integrity were analyzed using mixed-effect and latent growth curve models. The amplitude of low frequency fluctuations was analyzed to corroborate findings. ResultsPrenatal maternal mood entropy, but not mood level, was associated with salience network integrity. Both prenatal negative mood level and entropy were associated with the amplitude of low frequency fluctuations of the salience network. Latent class analysis yielded 2 profiles based on changes in network integrity across all functional magnetic resonance imaging sequences. The profile that exhibited little variation in network connectivity (i.e., inflexibility) consisted of adolescents who were exposed to higher negative maternal mood levels and more entropy. ConclusionsThese findings suggest that fetal exposure to maternal mood dysregulation is associated with a weakened and inflexible salience network. More broadly, they identify maternal mood entropy as a novel marker of early adversity that exhibits long-lasting associations with offspring brain development.

Effect of Alzheimer's Pathology on Task-Related Brain Network Reconfiguration in Aging.

Large-scale brain networks undergo widespread changes with older age and in neurodegenerative diseases such as Alzheimer's disease (AD). Research in young adults (YA) suggest that the underlying functional architecture of brain networks remains relatively consistent between rest and task states. However, it remains unclear whether the same is true in aging and to what extent any changes may be related to accumulation of AD pathology such as β-amyloid (Aβ) and tau. Here, we examined age-related differences in functional connectivity (FC) between rest and an object-scene mnemonic discrimination task using fMRI in young and older adults (OA; both females and males). We used an a priori episodic memory network (EMN) parcellation scheme associated with object and scene processing, that included anterior-temporal regions and posterior-medial regions. We also used positron emission topography to measure Aβ and tau in older adults. The correlation between rest and task FC (i.e., FC similarity) was reduced in older compared with younger adults. Older adults with lower FC similarity in EMN had higher levels of tau in the same EMN regions and performed worse during object, but not scene, trials during the fMRI task. These findings link AD pathology, particularly tau, to a less stable functional architecture in memory networks. They also suggest that smaller changes in FC organization between rest and task states may facilitate better performance in older age. Interpretations are limited by methodological factors related to different acquisition directions and durations between rest and task scans.SIGNIFICANCE STATEMENT The brain's large-scale network organization is relatively consistent between rest and task states in young adults (YA). We found that memory networks in older adults (OA) were less correlated between rest and (memory) task states compared with young adults. Older adults with less correlated brain networks also had higher levels of Alzheimer's disease (AD) pathology in the same regions, suggesting that a less stable network architecture may reflect the early evolution of AD. Older adults with less correlated brain networks also performed worse during the memory task suggesting that more similar network organization between rest and task states may facilitate better performance in older age.

Independent component analysis: a reliable alternative to general linear model for task-based fMRI.

Functional magnetic resonance imaging (fMRI) is a valuable tool for the presurgical evaluation of patients undergoing neurosurgeries. Although many pre-processing steps have been modified according to advances in recent years, statistical analysis has remained largely the same since the first days of fMRI. In this study, we examined the ability of Independent Component Analysis (ICA) to separate the activation of a language task in fMRI, and we compared it with the results of the General Lineal Model (GLM). Sixty patients undergoing evaluation for brain surgery due to various brain lesions and/or epilepsy and 20 control subjects completed an fMRI language mapping protocol that included three tasks, resulting in 259 fMRI scans. Depending on brain lesion characteristics, patients were allocated to (1) static/chronic not-expanding lesions (Group 1) and (2) progressive/expanding lesions (Group 2). GLM and ICA statistical maps were evaluated by fMRI experts to assess the performance of each technique. In the control group, ICA and GLM maps were similar without any superiority of either technique. In Group 1 and Group 2, ICA performed statistically better than GLM, with a p-value of < 0.01801 and < 0.0237, respectively. This indicated that ICA performs as well as GLM when the subjects are able to cooperate well (less movement, good task performance), but ICA could outperform GLM in the patient groups. When both techniques were combined, 240 out of 259 scans produced reliable results, showing that the sensitivity of task-based fMRI can be increased when both techniques are integrated with the clinical setup. ICA may be slightly more advantageous, compared to GLM, in patients with brain lesions, across the range of pathologies included in our population and independent of symptoms chronicity. Our findings suggest that GLM analysis may be more susceptible to brain activity perturbations induced by a variety of lesions or scanner-induced artifacts due to motion or other factors. In our research, we demonstrated that ICA is able to provide fMRI results that can be used in surgery, taking into account patient and task-wise aspects that differ from those when fMRI is used in research.

The sex differences in anhedonia in major depressive disorder: A resting-state fMRI study

ObjectiveThe external behavioural manifestations and internal neural mechanisms of anhedonia are sexually dimorphic. This study aimed to explore the sex differences in the regional brain neuroimaging features of anhedonia in the context of major depressive disorder (MDD). MethodThe resting-fMRI by applying amplitude of low-frequency fluctuation (ALFF) method was estimated in 414 patients with MDD (281 high anhedonia [HA], 133 low anhedonia [LA]) and 213 healthy controls (HC). The effects of two factors in patients with MDD were analysed using a 2 (sex: male, female) × 2 (group: HA, LA) ANOVA concerning the brain regions in which statistical differences were identified between patients with MDD and HC. We followed up with patients with HA at baseline, and 43 patients completed a second fMRI scan in remission. Paired t-test was performed to compare the ALFF values of anhedonia-related brain regions between the baseline and remission periods. ResultsFor the sex-by-group interaction, the bilateral insula, right hippocampus, right post cingulum cortex, and left putamen showed significant differences. Furthermore, the abnormally elevated ALFF values in anhedonia-related brain regions at baseline decreased in remission. ConclusionOur findings point to the fact that the females showed unique patterns of anhedonia-related brain activity compared to males, which may have clinical implications for interfering with the anhedonia symptoms in MDD. Using task fMRI, we can further examine the distinct characteristics between consumption anhedonia and anticipation anhedonia in MDD.

Neural Representation of Donating Time and Money.

Volunteering and charitable donations are two common forms of prosocial behavior, yet it is unclear whether these other-benefitting behaviors are supported by the same or different neurobiological mechanisms. During an fMRI task, 40 participants (20 female-identifying; age: mean = 18.92 years, range = 18.32-19.92 years) contributed their time (in minutes) and money (in dollars) to a variety of local charities. With the maximum amount of time and money that participants could spend on these charities, they did not differentially donate their time and money. At the neural level, donating time and money both showed activations in brain regions involved in cognitive control (e.g., dorsolateral PFC) and affective processing (e.g., dorsal anterior cingulate cortex), but donating time recruited regions involved in reward valuation (e.g., ventral striatum) and mentalizing (e.g., temporal pole) to a greater extent than donating money. Further, the precuneus, which is also a region involved in mentalizing, more strongly tracked the varying amount of money than time donated, suggesting that the precuneus may be more sensitive to the increasing magnitude of a nonsocial exchange (e.g., donating money is a financial exchange) than a social exchange (e.g., donating time is an interpersonal exchange). Our findings elucidate shared as well as distinct neurobiological properties of two prosocial behaviors, which have implications for how humans share different resources to positively impact their community.SIGNIFICANCE STATEMENT Prosocial behaviors broadly characterize how humans act to benefit others. Various prosocial behaviors, such as volunteering and charitable donations, share the goal of positively contributing to community. Our study identifies brain regions that may serve as ubiquitous neurobiological markers of community-based prosocial behaviors. Despite this shared goal, our study also shows that the human brain responds to donating time and money in diverging ways, such that brain regions associated with processing emotional reward and thinking about others are more strongly recruited for donating time than for money. Therefore, our study sheds light on how different personal resources, such as one's time and money, within a prosocial context are represented in the brain.

Adjunctive cognitive training with exposure enhances fear and neural outcomes in social anxiety

Exposure-based cognitive behavioral therapy (CBT) is the gold standard for treating social anxiety disorder (SAD), yet response is not universal. CBT is thought to operate via extinction-related learning during exposure, which in turn relies on cognitive processes such as working memory. The present proof-of-concept study investigates the potential for training working memory to improve anxiety related outcomes following exposure. Thirty-three adults with elevated social anxiety were randomized to complete a working memory training or sham training condition. Post-training, participants completed a working memory assessment, speech exposure session, and two fMRI tasks. Participants who received working memory training demonstrated lower distress ratings by the end of the speech exposures and better performance on the fMRI working memory task than those in sham. Working memory training completers had greater neural activation in frontoparietal regions during an in-scanner working memory task and exhibited less neural activation in the fusiform gyrus in response to an emotional face processing task than those in sham. Adding working memory training to exposure procedures could strengthen functioning of frontoparietal regions and alter emotional processing - key mechanisms implicated in extinction learning. Findings provide preliminary evidence that training working memory in conjunction with exposure may enhance exposure success.

Multimodal neuroimaging data from a 5-week heart rate variability biofeedback randomized clinical trial

We present data from the Heart Rate Variability and Emotion Regulation (HRV-ER) randomized clinical trial testing effects of HRV biofeedback. Younger (N = 121) and older (N = 72) participants completed baseline magnetic resonance imaging (MRI) including T1-weighted, resting and emotion regulation task functional MRI (fMRI), pulsed continuous arterial spin labeling (PCASL), and proton magnetic resonance spectroscopy (1H MRS). During fMRI scans, physiological measures (blood pressure, pulse, respiration, and end-tidal CO2) were continuously acquired. Participants were randomized to either increase heart rate oscillations or decrease heart rate oscillations during daily sessions. After 5 weeks of HRV biofeedback, they repeated the baseline measurements in addition to new measures (ultimatum game fMRI, training mimicking during blood oxygen level dependent (BOLD) and PCASL fMRI). Participants also wore a wristband sensor to estimate sleep time. Psychological assessment comprised three cognitive tests and ten questionnaires related to emotional well-being. A subset (N = 104) provided plasma samples pre- and post-intervention that were assayed for amyloid and tau. Data is publicly available via the OpenNeuro data sharing platform.

Different Cortical Activation of Facial Synkinesis Patients with Different Course of Disease: A Task fMRI Study.

Facial synkinesis is a disease characterized by unintentional activation of facial muscles, which causes that the patients cannot control their facial expressions independently. Previous studies have shown that its pathogenesis is related to the reorganization of cerebral cortex, but it remains unclear what brain changes the patients have at different stage of the disease. For this study, we recruited 30 patients with facial synkinesis and 19 healthy control subjects from Shanghai Huashan Hospital. All participants completed bilateral blinking and grinning tasks while functional magnetic resonance imaging (fMRI) data was collected. We measured the brain activation strength of each task and observed the activation similarity of the ipsilateral tasks. Then we explored the correlation between activation pattern and clinical scale. Results showed different activation pattern along the courses of disease for blinking and grinning task, which may be due to the inconsistent process of cortical reorganization. The late stage group activated more in blinking task, but the least in grinning tasks, especially on the affected side (p<0.001 at voxel level, p<0.05 at cluster level, FWE corrected). Compared with healthy controls, the activation of patients between tasks on the affected side is more similar(p<0.05). There was a negative correlation in right postcentral gyrus between activation similarity and scale scores (symmetry of voluntary movement scores: R = -0.469, p = 0.009). This could be attributed to the rearrangement of the nervous system following facial nerve injury, leading to incorrect connections between nerves and muscles. Our study may be helpful for understanding mechanism of facial synkinesis and provide basis for the stage-dependent diagnosis and treatment.

Functional magnetic resonance imaging study during resting state and visual oddball task in mild cognitive impairment.

Amnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and dementia, and identifying early biomarkers is crucial for disease detection and intervention. Functional magnetic resonance imaging (fMRI) has the potential to identify changes in neural activity in MCI. We investigated neural activity changes in the visual network of the aMCI patients (n:20) and healthy persons (n:17) using resting-state fMRI and visual oddball task fMRI. We used independent component analysis to identify regions of interest and compared the activity between groups using a false discovery rate correction. Resting-state fMRI revealed increased activity in the areas that have functional connectivity with the visual network, including the right superior and inferior lateral occipital cortex, the right angular gyrus and the temporo-occipital part of the right middle temporal gyrus (p-FDR = 0.008) and decreased activity in the bilateral thalamus and caudate nuclei, which are part of the frontoparietal network in the aMCI group (p-FDR = 0.002). In the visual oddball task fMRI, decreased activity was found in the right frontal pole, the right frontal orbital cortex, the left superior parietal lobule, the right postcentral gyrus, the right posterior part of the supramarginal gyrus, the right superior part of the lateral occipital cortex, and the right angular gyrus in the aMCI group. Our results suggest the alterations in the visual network are present in aMCI patients, both during resting-state and task-based fMRI. These changes may represent early biomarkers of aMCI and highlight the importance of assessing visual processing in cognitive impairment. However, future studies with larger sample sizes and longitudinal designs are needed to confirm these findings.

Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence.

Neuroimaging-based prediction methods for intelligence have seen a rapid development. Among different neuroimaging modalities, prediction using functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, with limited investigations on the merits of such analysis compared to prediction using dynamic FC or region-level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI, we propose a bi-directional long short-term memory (bi-LSTM) approach that incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient algorithm and applied for predicting intelligence using region-level time series and dynamic FC. We compare the prediction performance using different fMRI features acquired from the Adolescent Brain Cognitive Development (ABCD) study involving nearly 7000 individuals. Our detailed analysis illustrates the consistently inferior performance of static FC compared to region-level time series or dynamic FC for single and combined rest and task fMRI experiments. The joint analysis of task and rest fMRI leads to improved intelligence prediction under all models compared to using fMRI from only one experiment. In addition, the proposed bi-LSTM pipeline based on region-level time series identifies several shared and differential important brain regions across fMRI experiments that drive intelligence prediction. A test-retest analysis of the selected regions shows strong reliability across cross-validation folds. Given the large sample size of ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.

A deep learning approach to estimating initial conditions of Brain Network Models in reference to measured fMRI data.

Brain Network Models (BNMs) are mathematical models that simulate the activity of the entire brain. These models use neural mass models to represent local activity in different brain regions that interact with each other via a global structural network. Researchers have been interested in using these models to explain measured brain activity, particularly resting state functional magnetic resonance imaging (rs-fMRI). BNMs have shown to produce similar properties as measured data computed over longer periods of time such as average functional connectivity (FC), but it is unclear how well simulated trajectories compare to empirical trajectories on a timepoint-by-timepoint basis. During task fMRI, the relevant processes pertaining to task occur over the time frame of the hemodynamic response function, and thus it is important to understand how BNMs capture these dynamics over these short periods. To test the nature of BNMs' short-term trajectories, we used a deep learning technique called Neural ODE to simulate short trajectories from estimated initial conditions based on observed fMRI measurements. To compare to previous methods, we solved for the parameterization of a specific BNM, the Firing Rate Model, using these short-term trajectories as a metric. Our results show an agreement between parameterization of using previous long-term metrics with the novel short term metrics exists if also considering other factors such as the sensitivity in accuracy with relative to changes in structural connectivity, and the presence of noise. Therefore, we conclude that there is evidence that by using Neural ODE, BNMs can be simulated in a meaningful way when comparing against measured data trajectories, although future studies are necessary to establish how BNM activity relate to behavioral variables or to faster neural processes during this time period.

The mediating role of default mode network during meaning-making aroused by mental simulation between stressful events and stress-related growth: a task fMRI study

BackgroundStressful events and meaning-making toward them play an important role in adolescents’ life and growth. However, ignoring positive stressful events leads to negativity bias; further, the neural mechanisms of meaning-making are unclear. We aimed to verify the mediating role of meaning-making in stressful events and stress-related growth and the function of the default mode network (DMN) during meaning-making in this functional magnetic resonance imaging (fMRI) study.MethodsParticipants comprised 59 university students. Stressful life events, meaning-making, and stress-related growth were assessed at baseline, followed by fMRI scanning during a meaning-making task aroused by mental simulation. General linear modeling and psychophysiological interaction (PPI) analyses were used to explore the activation and functional connectivity of DMN during meaning-making.ResultsMental simulation triggered meaning-making, and DMN activity decreased during meaning-making. Activation of the DMN was negatively correlated with coping flexibility, an indicator of stress-related growth. PPI analysis showed that meaning-making was accompanied by diminished connectivity in the DMN. DMN activation during meaning-making can mediate the relationship between positive stressful events and coping flexibility.ConclusionsDecreased DMN activity and diminished functional connectivity in the DMN occurred during meaning-making. Activation of the DMN during meaning-making could mediate the relationship between positive stressful events and stress-related growth, which provides a cognitive neural basis for the mediating role of meaning-making in the relationship between stressful events and indicators of stress-related growth.ImplicationsThis study supports the idea that prosperity makes heroes, expands the meaning-making model, and suggests the inclusion of enhancing personal resources and meaning-making in education. This study was the first to validate the activation pattern and functional connectivity of the DMN during meaning-making aroused by mental simulation using an fMRI task-state examination, which can enhance our sense of meaning and provide knowledge that can be used in clinical psychology interventions.Trial RegistrationThe study protocol was pre-registered in Open Science Framework (see osf.io/ahm6e for details).

More proactive but less efficient: The effect of trait self-control on emotion regulation and its neural mechanisms

Self-control is important for maintaining good health, acquiring achievement and happiness, and improving environmental adaptability. Trait self-control can affect the processing of emotional conflict in daily life and is associated with successful emotional regulation. In this study, the task functional magnetic resonance imaging (fMRI) technology was adopted to explore the emotion regulation processing and neural mechanism of individuals with different trait self-control levels. The results showed that: (a) Individuals with high self-control experienced the lower intensity of negative emotion when viewing negative emotional pictures than individuals with low self-control, showing spontaneous emotion regulation and significantly increased activity of executive control and emotion regulation networks of the brain; (b) Individuals with low self-control were more sensitive to negative emotion, and their emotion regulation effect under external instructions was better than that of individuals with high self-control. This suggests that individuals with high trait self-control were adept at using proactive control strategies to spontaneously regulate emotional conflict and experienced less emotional conflict accordingly. However, they were less effective than low self-control individuals in resolving emotional conflicts. These findings provide an important basis for our understanding of the nature and neural mechanism of self-control.