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Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodings in functional MRI during narrative listening. Linguistic features of word unpredictability (surprisal) and contextual importance (saliency) were derived from the GPT-2 applied to the text of a 12-min narrative. Segments of variable duration (from 15 to 90 s) defined the context for the next word, resulting in different sets of neural predictors for functional MRI signals recorded in 27 healthy listeners of the narrative. GPT-2 surprisal, estimating word prediction errors from the artificial network, significantly explained the neural data in superior and middle temporal gyri (bilaterally), in anterior and posterior cingulate cortices, and in the left prefrontal cortex. GPT-2 saliency, weighing the importance of context words, significantly explained the neural data for longer segments in left superior and middle temporal gyri. These results add novel support to the use of DL tools in the search for neural encodings in functional MRI. A DL language model like the GPT-2 may feature useful data about neural processes subserving language comprehension in humans, including next-word context-related prediction.

Word-predictability measures, such as surprisal, have been used to show that linguistic prediction is an essential neural mechanism for successful comprehension. However, the neural dynamic of this mechanism may differ across individuals listening to the same narrative, revealing possible idiosyncrasies. Therefore, in this study, we investigated the ability of the surprisal measure to address the linguistic prediction in terms of the intersubject phase synchronization (ISPS) among healthy volunteers listening to a narrative during continuous functional Magnetic Resonance Imaging (fMRI) scanning. The data of twenty-seven participants, acquired in a previous study, were re-analyzed to estimate the ISPS associated with an audiobook listening, played forward and backward. Mean ISPS differences between playing conditions were first analyzed across the whole brain. ISPS time-series during the forward condition was explained with both a lexical-only and a semantics-weighted lexical surprisal model of the narrative word series to detect correlations between ISPS and word surprisal values. Compared to the backward condition, mean ISPS was significantly higher during forward condition in a broad network encompassing frontal, temporal, and parietal areas. The lexical-only model disclosed significant negative ISPS-surprisal correlations in the angular gyrus bilaterally, precuneus, left inferior parietal lobule, left middle frontal gyrus, left cerebellum, and left inferior frontal gyrus. The semantics-weighted surprisal model disclosed significant negative ISPS-surprisal correlations in the right angular gyrus, right precuneus, and left inferior frontal gyrus. Besides engaging language-related areas, narrative processing induces significant ISPS levels beyond the language network. The negative ISPS-surprisal correlations observed would signal divergent neural dynamics among individuals, leading to lower group synchronization, when the words to be integrated are characterized by higher surprisal levels. These results provide further support to the use of language models, such as the surprisal, to explain the neural processes of narrative comprehension and additional information into how the human brain exploits predictions during language comprehension.

Cortical surface-based morphometry is based on a semi-automated analysis of structural MRI images. In FreeSurfer, a widespread tool for surface-based analyses, a visual check of gray-white matter borders is followed by the manual placement of control points to drive the topological correction (editing) of segmented data. A novel algorithm combining radial sampling and machine learning is presented for the automated control point search (ACPS). Four data sets with 3 T MRI structural images were used for ACPS validation, including raw data acquired twice in 36 healthy subjects and both raw and FreeSurfer preprocessed data of 125 healthy subjects from public databases. The unedited data from a subgroup of subjects were submitted to manual control point search and editing. The ACPS algorithm was trained on manual control points and tested on new (unseen) unedited data. Cortical thickness (CT) and fractal dimensionality (FD) were estimated in three data sets by reconstructing surfaces from both unedited and edited data, and the effects of editing were compared between manual and automated editing and versus no editing. The ACPS-based editing improved the surface reconstructions similarly to manual editing. Compared to no editing, ACPS-based and manual editing significantly reduced CT and FD in consistent regions across different data sets. Despite the extra processing of control point driven reconstructions, CT and FD estimates were highly reproducible in almost all cortical regions, albeit some problematic regions (e.g. entorhinal cortex) may benefit from different editing. The use of control points improves the surface reconstruction and the ACPS algorithm can automate their search reducing the burden of manual editing.

Multi-factorial causes jeopardize brain integrity in β-thalassaemia. Intracranial parenchymal and vascular changes have been reported among young β-thalassaemia patients but conventional magnetic resonance imaging (MRI) findings are contradictory making early MRI and magnetic resonance angiography (MRA)/venography monitoring a matter of debate. This study prospectively investigated 75 neurologically asymptomatic β-thalassaemia patients (mean-age 35·2±10·7years; 52/75 transfusion-dependent; 41/75 splenectomised) using a 3T magnetic resonance scanner; clinical, laboratory and treatment data were also collected. White matter ischaemic-like abnormalities, intracranial artery stenoses, aneurysms and sinus venous thrombosis were compared between patients and 56 healthy controls (mean-age 33·9±10·8years). No patient or control showed silent territorial or lacunar strokes, intracranial artery stenoses or signs of sinus thrombosis. White matter lesions were found both in patients (35/75, 46·7%) and controls (28/56, 50·0%), without differences in terms of number (4·0±10·6 vs. 4·6±9·1, P=0·63), size and Fazekas' Score. Intracranial aneurysms did not differ between patients and controls for incidence rate (7/75, 9·3% vs. 5/56, 8·9%), size and site. Vascular and parenchymal abnormality rate did not differ according to treatments or clinical phenotype. According to this study, asymptomatic β-thalassaemia patients treated according to current guidelines do not seem to carry an increased risk of brain and intracranial vascular changes, thus weakening recommendations for regular brain MRI monitoring.