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fMRI data from participants performing a visual sequence recall task. The data is in BIDS 1.0.1 format but each participant's data is compressed into a single archive following a 'sub-*id*.tar.gz' pattern. For each participant there are three sub-folders: (1) 'anat' -- T1-weighted anatomical image. (2) 'func' -- (a) EPI BOLD images (multiband factor 2), (b) event timings data for contrast regressors (.tsv files). (3) 'fmap' -- EPI images in opposite phase-encoding direction to 'func' images to derive inhomogeneity field maps. For full details on the file formats included see the BIDS specification at https://bids-specification.readthedocs.io/en/stable/ The MRI data has been fully anonymised: all information linking the participants to the MRI scans has been removed. This included 'de-facing' where all facial features are removed from the images to ensure a greater degree of anonymity for data sharing purposes. De-facing was performed with 'pydeface' package (https://github.com/poldracklab/pydeface). The data was acquired at the Medical Research Council Cognition and Brain Sciences Unit (Cambridge, UK) on a 3T Siemens Prisma MRI scanner using a 32-channel head coil and simultaneous multi-slice data acquisition. Functional images were collected using 32 slices covering the whole brain (slice thickness 2 mm, in-plane resolution 2��2 mm) with acquisition time of 1.206 seconds, echo time of 30ms, and flip angle of 74 degrees. Each participant performed two scanning runs and 510 scans were acquired per run. The initial ten volumes from the run were discarded to allow for T1 equilibration effects. Stimulus presentation was controlled by PsychToolbox software: the trials were rear projected onto a translucent screen outside the bore of the magnet and viewed via a mirror system attached to the head coil. For full details of acquisition see 'Sequence learning recodes cortical representations instead of strengthening initial ones' by Kalm K, Norris D. PLOS Computational Biology, 2021. Analysis scripts for the data are available at: https://gitlab.com/kristjankalm/fmri_seq_ltm

This collection contains raw data in MEG-BIDS format. The purpose is to allow re-use of the data in different projects. Original publication abstract: The possibility to combine smaller units of meaning (e.g., words) to create new and more complex meanings (e.g., phrases and sentences) is a fundamental feature of human language. In the present project, we investigated how the brain supports the semantic and syntactic composition of two-word adjective-noun phrases in Dutch, using magnetoencephalography (MEG). The present investigation followed up on previous studies reporting a composition effect in the left anterior temporal lobe (LATL) when comparing neural activity at nouns combined with adjectives, as opposed to nouns in a non-compositional context. The first aim of the present study was to investigate whether this effect, as well as its modulation by noun specificity and adjective class, can also be observed in Dutch. A second aim was to investigate to what extent these effects may be driven by syntactic composition rather than primarily by semantic composition as was previously proposed. To this end, a novel condition was administered in which participants saw nouns combined with pseudowords lacking meaning but agreeing with the nouns in terms of grammatical gender, as real adjectives would. We failed to observe a composition effect or its modulation in both a confirmatory analysis (focused on the cortical region and time-window where it has previously been reported) and in exploratory analyses (where we tested multiple regions and an extended potential time-window of the effect). A syntactically driven composition effect was also not observed in our data. We do, however, successfully observe an independent, previously reported effect on single word processing in our data, confirming that our MEG data processing pipeline does meaningfully capture language processing activity by the brain. The failure to observe the composition effect in LATL is surprising given that it has been previously reported in multiple studies. Reviewing all previous studies investigating this effect, we propose that materials and a task involving imagery might be necessary for this effect to be observed. In addition, we identified substantial variability in the regions of interest analysed in previous studies, which warrants additional checks of robustness of the effect. Further research should identify limits and conditions under which this effect can be observed. [..]

In this study, two event-related potential experiments were conducted to investigate whether readers adapt their expectations to morphosyntactically (Experiment 1) or semantically (Experiment 2) anomalous sentences when they are repeatedly exposed to them. To address this issue, we experimentally manipulated the probability of occurrence of grammatical sentences and syntactically and semantically anomalous sentences through experiments. For the low probability block, anomalous sentences were presented less frequently than grammatical sentences (with a ratio of 1 to 4), while they were presented as frequently as grammatical sentences in the equal probability block. Experiment 1 revealed a smaller P600 effect for morphosyntactic violations in the equal probability block than in the low probability block. Linear mixed-effects models were used to examine how the size of the P600 effect changed as the experiment went along. The results showed that the smaller P600 effect of the equal probability block resulted from an amplitude’s decline in morphosyntactically violated sentences over the course of the experiment, suggesting an adaptation to morphosyntactic violations. In Experiment 2, semantically anomalous sentences elicited a larger N400 effect than their semantically natural counterparts regardless of probability manipulation. Little evidence was found in favour of adaptation to semantic violations in that the processing cost associated with the N400 did not decrease over the course of the experiment. Therefore, a dynamic aspect of language-processing system was demonstrated in this study. We will discuss why the language-processing system shows a selective adaptation to morphosyntactic violations.

Abstract Background There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, but it is unclear if these reports contain reliable information on these findings. Methods Four radiology residents reviewed 1000 neuroimaging reports (RI) of patients age > 50 years without clinical histories of stroke, TIA, or dementia for the presence, acuity, and location of SBIs, and the presence and severity of WMD. Four neuroradiologists directly reviewed a subsample of 182 images (DR). An NLP algorithm was developed to identify findings in reports. We assessed interrater reliability for DR and RI, and agreement between these two and with NLP. Results For DR, interrater reliability was moderate for the presence of SBIs (k = 0.58, 95 % CI 0.46–0.69) and WMD (k = 0.49, 95 % CI 0.35–0.63), and moderate to substantial for characteristics of SBI and WMD. Agreement between DR and RI was substantial for the presence of SBIs and WMD, and fair to substantial for characteristics of SBIs and WMD. Agreement between NLP and DR was substantial for the presence of SBIs (k = 0.64, 95 % CI 0.53–0.76) and moderate (k = 0.52, 95 % CI 0.39–0.65) for the presence of WMD. Conclusions Neuroimaging reports in routine care capture the presence of SBIs and WMD. An NLP can identify these findings (comparable to direct imaging review) and can likely be used for cohort identification.

Pliny the Elder was a prolific Roman author, naturalist, and military leader. Yet, his impact on modern-day neuroscience, psychiatry, and neurology has been little explored. Here, we aimed to trace the origins of our current understanding of the brain in ancient Rome through Pliny and his work, Natural History. As his magnum opus, this 37-book tome catalogs the facts and observations of natural life collected by Pliny, reflecting the knowledge of his time. Following the cephalocentric school of thought, Pliny places the brain as an agent for consciousness and details its diseases. Further, we explore Pliny’s methods, which allow him to build a thorough collection of clinical descriptions and remedies. This body of work serves as an important lesson for future neuroscientists on the power of observation, the role of the humanities, and the necessity of understanding the origin of modern scientific thinking.

This dataset contains key characteristics about the data described in the Data Descriptor The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format

How interpreting training may enhance semantic integration in general language processing (instead of language processing during interpreting) was not clear, and its neural correlates remained unexplored. Two groups of university students with different amounts of interpreting training experience (more-IE and less-IE) performed a second language (L2) reading task, in which the target sentence ended with three types of lateralized words: expected words, words of related semantic violation (related violation), and words of unrelated semantic violation (unrelated violation). N400 responses elicited by the final words were used to index semantic integration. Two major findings suggested potential contributions of interpreting training to L2 semantic integration. First, the more-IE group exhibited smaller N400 amplitudes than the less-IE group for expected words, and no group differences were obtained for the two violations. Second, N400 amplitudes for related violations were generally smaller than those for unrelated violations in the right visual field, and this difference was more fully captured (more broadly distributed over the scalp) in the more-IE group than in the less-IE group. Therefore, smaller N400 amplitudes for expected words but not for violations, and broader scalp distribution of the differences between related and unrelated violations constitute the neural correlates for the potential contributions of interpreting training to general L2 semantic integration. The results also suggest possible connections between different types of language experience.

This dataset contains key characteristics about the data described in the Data Descriptor The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format