Explore the vast range of titles available.

•Prediction of task activations from rest-fMRI is successful across datasets.•Minimal requirements for successful predictions are described.•Number of fMRI timepoints affects prediction success more than sample size.•Model generalizability is shown across sites, MRI vendors and age groups. Predictions of task-based functional magnetic resonance imaging (fMRI) from task-free resting-state (rs) fMRI have gained popularity over the past decade. This method holds a great promise for studying individual variability in brain function without the need to perform highly demanding tasks. However, in order to be broadly used, prediction models must prove to generalize beyond the dataset they were trained on. In this work, we test the generalizability of prediction of task-fMRI from rs-fMRI across sites, MRI vendors and age-groups. Moreover, we investigate the data requirements for successful prediction. We use the Human Connectome Project (HCP) dataset to explore how different combinations of training sample sizes and number of fMRI datapoints affect prediction success in various cognitive tasks. We then apply models trained on HCP data to predict brain activations in data from a different site, a different MRI vendor (Phillips vs. Siemens scanners) and a different age group (children from the HCP-development project). We demonstrate that, depending on the task, a training set of approximately 20 participants with 100 fMRI timepoints each yields the largest gain in model performance. Nevertheless, further increasing sample size and number of timepoints results in significantly improved predictions, until reaching approximately 450–600 training participants and 800–1000 timepoints. Overall, the number of fMRI timepoints influences prediction success more than the sample size. We further show that models trained on adequate amounts of data successfully generalize across sites, vendors and age groups and provide predictions that are both accurate and individual-specific. These findings suggest that large-scale publicly available datasets may be utilized to study brain function in smaller, unique samples.

•Hippocampal-cortical connectivity gradients change during naturalistic memory tasks.•Familiar cues accentuate the hippocampal anterior-posterior functional transition.•This transition is more posterior in the left hippocampus of subjects with MCI or AD.•Gradients of the anterior hippocampus map onto the default mode network. The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.

The relationship between neural social cognition patterns and performance on social cognition tasks in daily life is a topic of debate, with key consideration given to the extent to which theory of mind (ToM) brain circuits share properties reflecting everyday social functioning. To test the efficacy of ecological stimuli in eliciting brain activation within the ToM brain circuits, we adapted the Edinburgh Social Cognition test social scenarios, consisting of dynamic ecological contextually embedded social stimuli, to a fMRI paradigm. Forty-two adults (21 men, mean age ± SD = 34.19 years ±12.57) were enrolled and underwent an fMRI assessment which consisted of a ToM task using the Edinburgh Social Cognition test scenarios. We used the same stimuli to prompt implicit ( ) and explicit ( and ) reasoning on cognitive and affective mental states. The fMRI analysis was based on the classical random effect analysis. Group inferences were complemented with supplemental analyses using overlap maps to assess inter-subject variability. We found that explicit mentalizing reasoning yielded wide neural activations when two-choice answers were used. We also observed that the nature of ToM reasoning, that is, affective or cognitive, played a significant role in activating different neural circuits. The ESCoT stimuli were particularly effective in evoking ToM core neural underpinnings and elicited executive frontal loops. Future work may employ the task in a clinical setting to investigate ToM network reorganization and plasticity.

•Spatial activity patterns evoked by the observation of natural movements occur at rest in the absence of any visual stimulus.•Although not represented at rest, uncommon movements evoke enhanced activation in parietal and premotor areas.•Spontaneous activity maintains cognitive representations for motor planning of frequent behaviors. Spontaneous brain activity forms correlated networks resembling task-evoked activation patterns, yet its functional relevance remains debated. The representational hypothesis suggests that resting-state networks (RSNs) encode frequent behaviors, but whether these representations are motor-based or cognitive is unclear. Here, we used fMRI to examine RSNs activity during the observation of reach-to-grasp movements with either regular (common) or perturbed (uncommon) kinematics. We found that the dorsal attention network (DAN) exhibited greater similarity between rest and task patterns for common movements, whereas sensory networks showed no significant effects. While DAN is classically associated with attention mechanisms, these results suggest that it may also contribute to tracking the location or motion of the hand. Furthermore, uncommon movements elicited stronger activation in parietal and premotor areas, likely reflecting adaptive updating of internal models. Our findings support the role of spontaneous brain activity in maintaining cognitive representations of frequent behaviors, optimizing motor planning and perception.

Whether antagonistic brain states constitute a fundamental principle of human brain organization has been debated over the past decade. Some argue that intrinsically anti-correlated brain networks in resting-state functional connectivity are an artifact of preprocessing. Others argue that anti-correlations are biologically meaningful predictors of how the brain will respond to different stimuli. Here, we investigated the co-activation patterns across the whole brain in various tasks and test whether brain regions demonstrate anti-correlated activity similar to those observed at rest. We examined brain activity in 47 task contrasts from the Human Connectome Project (N = 680) and found robust antagonistic interactions between networks. Regions of the default network exhibited the highest degree of cortex-wide negative connectivity. The negative co-activation patterns across tasks showed good correspondence to that derived from resting-state data processed with global signal regression (GSR). Interestingly, GSR-processed resting-state data was a significantly better predictor of task-induced modulation than data processed without GSR. Finally, in a cohort of 25 patients with depression, we found that task-based anti-correlations between the dorsolateral prefrontal cortex (DLPFC) and subgenual anterior cingulate cortex were associated with clinical efficacy of transcranial magnetic stimulation therapy targeting the DLPFC. Overall, our findings indicate that anti-correlations are a biologically meaningful phenomenon and may reflect an important principle of functional brain organization.

The investigation of functional brain networks (FBNs) using task-based functional magnetic resonance imaging (tfMRI) has gained significant attention in the field of neuroimaging. Despite the availability of several methods for constructing FBNs, including traditional methods like GLM and deep learning methods such as spatiotemporal self-attention mechanism (STAAE), these methods have design and training limitations. Specifically, they do not consider the intrinsic characteristics of fMRI data, such as the possibility that the same signal value at different time points could represent different brain states and meanings. Furthermore, they overlook prior knowledge, such as task designs, during training. This study aims to overcome these limitations and develop a more efficient model by drawing inspiration from techniques in the field of natural language processing (NLP). The proposed model, called the Multi-head Attention-based Masked Sequence Model (MAMSM), uses a multi-headed attention mechanism and mask training approach to learn different states corresponding to the same voxel values. Additionally, it combines cosine similarity and task design curves to construct a novel loss function. The MAMSM was applied to seven task state datasets from the Human Connectome Project (HCP) tfMRI dataset. Experimental results showed that the features acquired by the MAMSM model exhibit a Pearson correlation coefficient with the task design curves above 0.95 on average. Moreover, the model can extract more meaningful networks beyond the known task-related brain networks. The experimental results demonstrated that MAMSM has great potential in advancing the understanding of functional brain networks.

•We present a method for prediction of individual traits from resting-state fMRI.•Transforming rs-data to predicted task maps improves trait prediction accuracy.•Combining multiple task contrasts improves trait prediction from both actual and predicted task maps.•The method is flexible and applicable to a variety of traits and tasks. Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.