Although certain changes in the brain may reflect fear learning, there are no known markers that indicate whether an aversive experience will develop into fear memory. We examined the moment-to-moment dynamics of human fear learning by applying multi-voxel pattern analysis to single-trial blood oxygen level-dependent magnetic resonance imaging data. We found that the long-term behavioral expression of fear memory could be predicted from neural patterns at the time of learning.

We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform.

Single-trial analysis is particularly useful for assessing cognitive processes that are intrinsically dynamic, such as learning. Studying these processes with fMRI is problematic, as the low signal-to-noise ratio of fMRI requires the averaging over multiple trials, obscuring trial-by-trial changes in neural activation. The superior sensitivity of multivoxel pattern analysis over univariate analyses has opened up new possibilities for single-trial analysis, but this may require different fMRI designs. Here, we measured fMRI and pupil dilation responses during discriminant aversive conditioning, to assess associative learning in a trial-by-trial manner. The impact of design choices was examined by varying trial spacing and trial order in a series of five experiments (total n = 66), while keeping stimulus duration constant (4.5 s). Our outcome measure was the change in similarity between neural response patterns related to two consecutive presentations of the same stimulus (within-stimulus) and between patterns related to pairs of different stimuli (between-stimulus) that shared a specific outcome (electric stimulation vs. no consequence). This trial-by-trial similarity analysis revealed clear single-trial learning curves in conditions with intermediate (8.1-12.6 s) and long (16.5-18.4 s) intervals, with effects being strongest in designs with long intervals and counterbalanced stimulus presentation. No learning curves were observed in designs with shorter intervals (1.6-6.1 s), indicating that rapid event-related designs-at present, the most common designs in fMRI research-are not suited for single-trial pattern analysis. These findings emphasize the importance of deciding on the type of analysis prior to data collection.