The Privileged Brain Representation of First Olfactory Associations

SummaryAuthors [1], poets [2], and scientists [3–6] have been fascinated by the strength of childhood olfactory memories. Indeed, in long-term memory, the first odor-to-object association was stronger than subsequent associations of the same odor with other objects [7]. Here we tested the hypothesis that first odor associations enjoy a privileged brain representation. Because emotion impacts memory [8–10], we further asked whether the pleasantness of an odor would influence such a representation. On day 1, we associated the same visual objects initially with one, and subsequently with a second, set of pleasant and unpleasant olfactory and auditory stimuli. One week later, we presented the same visual objects and tested odor-associative memory concurrent with functional magnetic resonance brain imaging. We found that the power (% remembered) of early associations was enhanced when they were unpleasant, regardless of whether they were olfactory or auditory. Brain imaging, however, revealed a unique hippocampal activation for early olfactory but not auditory associations, regardless of whether they were pleasant or unpleasant. Activity within the hippocampus on day 1 predicted the olfactory but not auditory associations that would be remembered one week later. These findings confirmed the hypothesis of a privileged brain representation for first olfactory associations

The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension

The “Narratives” collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging

Neural synchronization as a function of engagement with the narrative

We can all agree that a good story engages us, however, agreeing which story is good is far more debatable. In this study, we explored whether engagement with a narrative synchronizes listeners’ brain responses, by examining individual differences in engagement to the same story. To do so, we pre-registered and re-analyzed a previously collected dataset by Chang et al. (2021) of functional Magnetic Resonance Imaging (fMRI) scans of 25 participants who listened to a one-hour story and answered questionnaires. We assessed the degree of their overall engagement with the story and their engagement with the main characters. The questionnaires revealed individual differences in engagement with the story, as well as different valence towards specific characters. Neuroimaging data showed that the auditory cortex, the default mode network (DMN) and language regions were involved in processing the story. Increased engagement with the story was correlated with increased neural synchronization within regions in the DMN (especially the medial prefrontal cortex), as well as regions outside the DMN such as the dorso-lateral prefrontal cortex and the reward system. Interestingly, positively and negatively engaging characters elicited different patterns of neural synchronization. Finally, engagement increased functional connectivity within and between the DMN, the ventral attention network and the control network. Taken together, these findings suggest that engagement with a narrative synchronizes listeners’ responses in regions involved in mentalizing, reward, working memory and attention. By examining individual differences in engagement, we revealed that these synchronization patterns are due to engagement, and not due to differences in the narrative's content

Amplification of local changes along the timescale processing hierarchy

This dataset contains of 18 subjects passively listening to an audio Story1 (“MilkyWay”) and 18 subjects passively listening to an audio Story2 (“Vodka”). Each subject has one functional run of 297 TRs, during which s/he heard an audio story (“Milky Way” or “Vodka”), while viewing a gray screen. Subjects were instructed to attend to the details of the narrative. Download the README.txt file for a detailed description of this dataset's contentSmall changes in word choice can lead to dramatically different interpretations of narratives. How does the brain accumulate and integrate such local changes to construct unique neural representations for different stories? In this study we created two distinct narratives by changing only a few words in each sentence (e.g. “he” to “she” or “sobbing” to “laughing”) while preserving the grammatical structure across stories. We then measured changes in neural responses between the two stories. We found that the differences in neural responses between the two stories gradually increased along the hierarchy of processing timescales. For areas with short integration windows, such as early auditory cortex, the differences in neural responses between the two stories were relatively small. In contrast, in areas with the longest integration windows at the top of the hierarchy, such as the precuneus, temporal parietal junction, and medial frontal cortices, there were large differences in neural responses between stories. Furthermore, this gradual increase in neural difference between the stories was highly correlated with an area’s ability to integrate information over time. Amplification of neural differences did not occur when changes in words did not alter the interpretation of the story (e.g. “sobbing” to “crying”). Our results demonstrate how subtle differences in words are gradually accumulated and amplified along the cortical hierarchy as the brain constructs a narrative over time

Sleep-anticipating effects of melatonin in the human brain

Melatonin, the hormone produced nocturnally by the pineal gland, is an endogenous regulator of the sleep–wake cycle. The effects of melatonin on brain activities and their relation to induction of sleepiness were studied in a randomized, double-blind, placebo controlled functional magnetic resonance imaging (fMRI) study. Melatonin, but not placebo, reduced task-related activity in the rostro-medial aspect of the occipital cortex during a visual-search task and in the auditory cortex during a music task. These effects correlated with subjective measurements of fatigue. In addition, melatonin enhanced the activation in the left parahippocampus in an autobiographic memory task. Results demonstrate that melatonin modulates brain activity in a manner resembling actual sleep although subjects are fully awake. Furthermore, the fatigue inducing effect of melatonin on brain activity is essentially different from that of sleep deprivation thus revealing differences between fatigues related to the circadian sleep regulation as opposed to increased homeostatic sleep need. Our findings highlight the role of melatonin in priming sleep-associated brain activation patterns in anticipation of sleep

The 21st year: transcription, motif list, and relation score

This study collected fMRI data when 25 participants listened to a story ("The 21st year"). We implanted 28 recurrent narrative motifs in the story, which were expected to create connections between separate events that shared the same motifs. The participants’ understanding of the relations created by these motifs was assessed by 5 independent raters based on post-scan questionnaires. This dataset includes the transcription of the story, the list of motifs, the fMRI participants' responses to the relation test, and the relation scores. (averaged across raters). Download the README.txt file for a detailed description of this dataset's content.The fMRI data and the auditory story used in this study are also publicly available on OpenNeuro: https://openneuro.org/datasets/ds002245.This study examined how the brain dynamically updates event representations by integrating new information over multiple minutes while segregating irrelevant input. A professional writer custom-designed a narrative with two independent storylines, interleaving across minute-long segments (ABAB). In the last (C) part, characters from the two storylines meet and their shared history is revealed. Part C is designed to induce the spontaneous recall of past events, upon the recurrence of narrative motifs from A/B, and to shed new light on them. Our fMRI results showed storyline-specific neural patterns, which were reinstated (i.e. became more active) during storyline transitions. This effect increased along the processing timescale hierarchy, peaking in the default mode network. Similarly, the neural reinstatement of motifs was found during part C. Furthermore, participants showing stronger motif reinstatement performed better in integrating A/B and C events, demonstrating the role of memory reactivation in information integration over intervening irrelevant events

Same story, different story: the neural representation of interpretive frameworks

This data set contains functional runs of 475 TRs for 40 subjects passively listening to an audio story while viewing a gray screen. Just before listening subjects were exposed to one of two different brief contexts, “cheating” or “paranoia”, which had an effect on the whole interpretation of the story.Download the README.txt file for a detailed description of this dataset's content

Dynamic reconfiguration of the default mode network during narrative comprehension

Subjects were scanned in a 3T full-body MRI scanner while listening to a three version of a story, an intact version, a version in paragraph scramble condition, and word scramble condition.Download the README.txt file for a detailed description of this dataset's contentDoes the default mode network (DMN) reconfigure to encode information about the changing environment? This question has proven difficult, because patterns of functional connectivity reflect a mixture of stimulus-induced neural processes, intrinsic neural processes and non-neuronal noise. Here we introduce inter-subject functional correlation (ISFC), which isolates stimulus-dependent inter-regional correlations between brains exposed to the same stimulus. During fMRI, we had subjects listen to a real-life auditory narrative and to temporally scrambled versions of the narrative. We used ISFC to isolate correlation patterns within the DMN that were locked to the processing of each narrative segment and specific to its meaning within the narrative context. The momentary configurations of DMN ISFC were highly replicable across groups. Moreover, DMN coupling strength predicted memory of narrative segments. Thus, ISFC opens new avenues for linking brain network dynamics to stimulus features and behaviour

Neuroimaging Analysis Methods For Naturalistic Data

Version 1.0 of the Naturalistic-Data.org educational course. Naturalistic-Data.org is an open access online educational resource that provides an introduction to analyzing naturalistic functional neuroimaging datasets using Python. Naturalistic-Data.org is built using Jupyter-Book and provides interactive tutorials for introducing advanced analytic techniques . This includes functional alignment, inter-subject correlations, inter-subject representational similarity analysis, inter-subject functional connectivity, event segmentation, natural language processing, hidden semi-markov models, automated annotation extraction, and visualizing high dimensional data. The tutorials focus on practical applications using open access data, short open access video lectures, and interactive Jupyter notebooks. All of the tutorials use open source packages from the python scientific computing community (e.g., numpy, pandas, scipy, matplotlib, scikit-learn, networkx, nibabel, nilearn, brainiak, hypertoos, timecorr, pliers, statesegmentation, and nltools). The course is designed to be useful for varying levels of experience, including individuals with minimal experience with programming, Python, and statistics