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Probabilistic language models are increasingly used to provide neural representations of linguistic features under naturalistic settings. Word surprisal models can be applied to continuous fMRI recordings during task-free listening of narratives, to detect regions linked to language prediction and comprehension. Here, to this purpose, a novel semantics-weighted lexical surprisal is applied to naturalistic fMRI data. FMRI was performed at 3 Tesla in 31 subjects during task-free listening to a 12-minute audiobook played in both original and word-reversed (control) version. Lexical-only and semantics-weighted lexical surprisal models were estimated for the original and control word series. The two series were alternatively chosen to build the predictor of interest in the first-level general linear model and were compared in the second-level (group) analysis. The addition of the surprisal predictor to the stimulus-related predictors significantly improved the fitting of the neural signal. In average, the semantics-weighted model yielded lower surprisal values and, in some areas, better fitting of the fMRI data compared to the lexical-only model. The two models produced both overlapping and distinct activations: while lexical-only surprisal activated secondary auditory areas in the superior temporal gyri and the cerebellum, semantics-weighted surprisal additionally activated the left inferior frontal gyrus. These results confirm the usefulness of surprisal models in the naturalistic fMRI analysis of linguistic processes and suggest that the use of semantic information may increase the sensitivity of a probabilistic language model in higher-order language-related areas, with possible implications for future naturalistic fMRI studies of language under normal and (clinically or pharmacologically) modified conditions., Competing Interests: Declaration of Competing Interest The authors report no disclosures., (Copyright © 2020. Published by Elsevier Inc.)

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., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Among male patients affected by Kallmann syndrome, a genetically determined disease due to defective neural migration leading to hypogonadropic hypogonadism and hypo/anosmia, about 40% present the peculiar phenomenon of mirror movements, i.e. involuntary movements mirroring contralateral voluntary hand movements. Several pathogenic hypotheses have been proposed, but the ultimate neurological mechanisms are still elusive. The aim of the present study was to investigate brain anatomical substrates of mirror movements in Kallmann syndrome by means of a panel of quantitative MRI analyses. Forty-nine male Kallmann syndrome patients underwent brain MRI. The study protocol included 3D-T1-weighted gradient echo, fluid attenuated inversion recovery and diffusion tensor imaging. Voxel-based morphometry, sulcation, curvature and cortical thickness analyses and tract based spatial statistics were performed using SPM8, Freesurfer and FSL. All patients underwent a complete physical and neurological examination including the evaluation of mirror movements (according to the Woods and Teuber criteria). Kallmann syndrome patients presenting with mirror movements (16/49, 32%) displayed the following brain changes: 1) increased gray matter density in the depth of the left precentral sulcus behind the middle frontal gyrus; 2) decreased cortical thickness in the precentral gyrus bilaterally, in the depth of right precentral sulcus and in the posterior portion of the right superior frontal gyrus; and 3) decreased fractional anisotropy in the left hemisphere involving the temporal lobe and peritrigonal white matter. No differences were shown by cortical curvature and sulcation analyses. The composite array of brain changes observed in Kallmann syndrome patients with mirror movements likely represents the anatomical-structural underpinnings leading to the peculiar derangement of the complex circuitry committed to unilateral hand voluntary movements., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Imagination is a key function for many human activities, such as reminiscing, learning, or planning. Unravelling its neuro-biological basis is paramount to grasp the essence of our thoughts. Previous neuroimaging studies have identified brain regions subserving the visualisation of "what?" (e.g. faces or objects) and "where?" (e.g. spatial layout) content of mental images. However, the functional role of a common set of involved regions - the frontal regions - and their interplay with the "what" and "where" regions, has remained largely unspecified. This study combines functional MRI and electroencephalography to examine the full-brain network that underlies the visual imagery of complex scenes and to investigate the spectro-temporal properties of its nodes, especially of the frontal cortex. Our results indicate that frontal regions integrate the "what" and "where" content of our thoughts into one visually imagined scene. We link early synchronisation of anterior theta and beta oscillations to regional activation of right and central frontal cortices, reflecting retrieval and integration of information. These frontal regions orchestrate remote occipital-temporal regions (including calcarine sulcus and parahippocampal gyrus) that encode the detailed representations of the objects, and parietal "where" regions that encode the spatial layout into forming one coherent mental picture. Specifically the mesial superior frontal gyrus appears to have a principal integrative role, as its activity during the visualisation of the scene predicts subsequent performance on the imagery task., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Edges are important cues defining coherent auditory objects. As a model of auditory edges, sound on- and offset are particularly suitable to study their neural underpinnings because they contrast a specific physical input against no physical input. Change from silence to sound, that is onset, has extensively been studied and elicits transient neural responses bilaterally in auditory cortex. However, neural activity associated with sound onset is not only related to edge detection but also to novel afferent inputs. Edges at the change from sound to silence, that is offset, are not confounded by novel physical input and thus allow to examine neural activity associated with sound edges per se. In the first experiment, we used silent acquisition functional magnetic resonance imaging and found that the offset of pulsed sound activates planum temporale, superior temporal sulcus and planum polare of the right hemisphere. In the planum temporale and the superior temporal sulcus, offset response amplitudes were related to the pulse repetition rate of the preceding stimulation. In the second experiment, we found that these offset-responsive regions were also activated by single sound pulses, onset of sound pulse sequences and single sound pulse omissions within sound pulse sequences. However, they were not active during sustained sound presentation. Thus, our data show that circumscribed areas in right temporal cortex are specifically involved in identifying auditory edges. This operation is crucial for translating acoustic signal time series into coherent auditory objects.

Among other auditory operations, the analysis of different sound levels received at both ears is fundamental for the localization of a sound source. These so-called interaural level differences, in animals, are coded by excitatory-inhibitory neurons yielding asymmetric hemispheric activity patterns with acoustic stimuli having maximal interaural level differences. In human auditory cortex, the temporal blood oxygen level-dependent (BOLD) response to auditory inputs, as measured by functional magnetic resonance imaging (fMRI), consists of at least two independent components: an initial transient and a subsequent sustained signal, which, on a different time scale, are consistent with electrophysiological human and animal response patterns. However, their specific functional role remains unclear. Animal studies suggest these temporal components being based on different neural networks and having specific roles in representing the external acoustic environment. Here we hypothesized that the transient and sustained response constituents are differentially involved in coding interaural level differences and therefore play different roles in spatial information processing. Healthy subjects underwent monaural and binaural acoustic stimulation and BOLD responses were measured using high signal-to-noise-ratio fMRI. In the anatomically segmented Heschl's gyrus the transient response was bilaterally balanced, independent of the side of stimulation, while in opposite the sustained response was contralateralized. This dissociation suggests a differential role at these two independent temporal response components, with an initial bilateral transient signal subserving rapid sound detection and a subsequent lateralized sustained signal subserving detailed sound characterization.

We present a general method for the classification of independent components (ICs) extracted from functional MRI (fMRI) data sets. The method consists of two steps. In the first step, each fMRI-IC is associated with an IC-fingerprint, i.e., a representation of the component in a multidimensional space of parameters. These parameters are post hoc estimates of global properties of the ICs and are largely independent of a specific experimental design and stimulus timing. In the second step a machine learning algorithm automatically separates the IC-fingerprints into six general classes after preliminary training performed on a small subset of expert-labeled components. We illustrate this approach in a multisubject fMRI study employing visual structure-from-motion stimuli encoding faces and control random shapes. We show that: (1) IC-fingerprints are a valuable tool for the inspection, characterization and selection of fMRI-ICs and (2) automatic classifications of fMRI-ICs in new subjects present a high correspondence with those obtained by expert visual inspection of the components. Importantly, our classification procedure highlights several neurophysiologically interesting processes. The most intriguing of which is reflected, with high intra- and inter-subject reproducibility, in one IC exhibiting a transiently task-related activation in the 'face' region of the primary sensorimotor cortex. This suggests that in addition to or as part of the mirror system, somatotopic regions of the sensorimotor cortex are involved in disambiguating the perception of a moving body part. Finally, we show that the same classification algorithm can be successfully applied, without re-training, to fMRI collected using acquisition parameters, stimulation modality and timing considerably different from those used for training.

The auditory cortex is anatomically segregated into a central core and a peripheral belt region, which exhibit differences in preference to bandpassed noise and in temporal patterns of response to acoustic stimuli. While it has been shown that visual stimuli can modify response magnitude in auditory cortex, little is known about differential patterns of multisensory interactions in core and belt. Here, we used functional magnetic resonance imaging and examined the influence of a short visual stimulus presented prior to acoustic stimulation on the spatial pattern of blood oxygen level-dependent signal response in auditory cortex. Consistent with crossmodal inhibition, the light produced a suppression of signal response in a cortical region corresponding to the core. In the surrounding areas corresponding to the belt regions, however, we found an inverse modulation with an increasing signal in centrifugal direction. Our data suggest that crossmodal effects are differentially modulated according to the hierarchical core-belt organization of auditory cortex.

Triggered event-related functional magnetic resonance imaging requires sparse intervals of temporally resolved functional data acquisitions, whose initiation corresponds to the occurrence of an event, typically an epileptic spike in the electroencephalographic trace. However, conventional fMRI time series are greatly affected by non-steady-state magnetization effects, which obscure initial blood oxygen level-dependent (BOLD) signals. Here, conventional echo-planar imaging and a post-processing solution based on principal component analysis were employed to remove the dominant eigenimages of the time series, to filter out the global signal changes induced by magnetization decay and to recover BOLD signals starting with the first functional volume. This approach was compared with a physical solution using radiofrequency preparation, which nullifies magnetization effects. As an application of the method, the detectability of the initial transient BOLD response in the auditory cortex, which is elicited by the onset of acoustic scanner noise, was used to demonstrate that post-processing-based removal of magnetization effects allows to detect brain activity patterns identical with those obtained using the radiofrequency preparation. Using the auditory responses as an ideal experimental model of triggered brain activity, our results suggest that reducing the initial magnetization effects by removing a few principal components from fMRI data may be potentially useful in the analysis of triggered event-related echo-planar time series. The implications of this study are discussed with special caution to remaining technical limitations and the additional neurophysiological issues of the triggered acquisition.

Auditory neuroscience has not tapped fMRI's full potential because of acoustic scanner noise emitted by the gradient switches of conventional echoplanar fMRI sequences. The scanner noise is pulsed, and auditory cortex is particularly sensitive to pulsed sounds. Current fMRI approaches to avoid stimulus-noise interactions are temporally inefficient. Since the sustained BOLD response to pulsed sounds decreases with repetition rate and becomes minimal with unpulsed sounds, we developed an fMRI sequence emitting continuous rather than pulsed gradient sound by implementing a novel quasi-continuous gradient switch pattern. Compared to conventional fMRI, continuous-sound fMRI reduced auditory cortex BOLD baseline and increased BOLD amplitude with graded sound stimuli, short sound events, and sounds as complex as orchestra music with preserved temporal resolution. Response in subcortical auditory nuclei was enhanced, but not the response to light in visual cortex. Finally, tonotopic mapping using continuous-sound fMRI demonstrates that enhanced functional signal-to-noise in BOLD response translates into improved spatial separability of specific sound representations.

Functional magnetic resonance imaging (fMRI) studies can provide insight into the neural correlates of hallucinations. Commonly, such studies require self-reports about the timing of the hallucination events. While many studies have found activity in higher-order sensory cortical areas, only a few have demonstrated activity of the primary auditory cortex during auditory verbal hallucinations. In this case, using self-reports as a model of brain activity may not be sensitive enough to capture all neurophysiological signals related to hallucinations. We used spatial independent component analysis (sICA) to extract the activity patterns associated with auditory verbal hallucinations in six schizophrenia patients. SICA decomposes the functional data set into a set of spatial maps without the use of any input function. The resulting activity patterns from auditory and sensorimotor components were further analyzed in a single-subject fashion using a visualization tool that allows for easy inspection of the variability of regional brain responses. We found bilateral auditory cortex activity, including Heschl's gyrus, during hallucinations of one patient, and unilateral auditory cortex activity in two more patients. The associated time courses showed a large variability in the shape, amplitude, and time of onset relative to the self-reports. However, the average of the time courses during hallucinations showed a clear association with this clinical phenomenon. We suggest that detection of this activity may be facilitated by examining hallucination epochs of sufficient length, in combination with a data-driven approach.

Independent component analysis (ICA) is a valuable technique for the multivariate data-driven analysis of functional magnetic resonance imaging (fMRI) data sets. Applications of ICA have been developed mainly for single subject studies, although different solutions for group studies have been proposed. These approaches combine data sets from multiple subjects into a single aggregate data set before ICA estimation and, thus, require some additional assumptions about the separability across subjects of group independent components. Here, we exploit the application of similarity measures and a related visual tool to study the natural self-organizing clustering of many independent components from multiple individual data sets in the subject space. Our proposed framework flexibly enables multiple criteria for the generation of group independent components and their random-effects evaluation. We present real visual activation fMRI data from two experiments, with different spatiotemporal structures, and demonstrate the validity of this framework for a blind extraction and selection of meaningful activity and functional connectivity group patterns. Our approach is either alternative or complementary to the group ICA of aggregated data sets in that it exploits commonalities across multiple subject-specific patterns, while addressing as much as possible of the intersubject variability of the measured responses. This property is particularly of interest for a blind group and subgroup pattern extraction and selection.

Real-time functional magnetic resonance imaging (fMRI) enables one to monitor a subject's brain activity during an ongoing session. The availability of online information about brain activity is essential for developing and refining interactive fMRI paradigms in research and clinical trials and for neurofeedback applications. Data analysis for real-time fMRI has traditionally been based on hypothesis-driven processing methods. Off-line data analysis, conversely, may be usefully complemented by data-driven approaches, such as independent component analysis (ICA), which can identify brain activity without a priori temporal assumptions on brain activity. However, ICA is commonly considered a time-consuming procedure and thus unsuitable to process the high flux of fMRI data while they are acquired. Here, by specific choices regarding the implementation, we exported the ICA framework and implemented it into real-time fMRI data analysis. We show that, reducing the ICA input to a few points within a time-series in a sliding-window approach, computational times become compatible with real-time settings. Our technique produced accurate dynamic readouts of brain activity as well as a precise spatiotemporal history of quasistationary patterns in the form of cumulative activation maps and time courses. Results from real and simulated motor activation data show comparable performances for the proposed ICA implementation and standard linear regression analysis applied either in a sliding-window or in a cumulative mode. Furthermore, we demonstrate the possibility of monitoring transient or unexpected neural activities and suggest that real-time ICA may provide the fMRI researcher with a better understanding and control of subjects' behaviors and performances.

The blood oxygen level-dependent (BOLD) signal time course in the auditory cortex is characterized by two components, an initial transient peak and a subsequent sustained plateau with smaller amplitude. Because the T(2)(*) signal detected by functional magnetic resonance imaging (fMRI) depends on at least two counteracting factors, blood oxygenation and volume, we examined whether the reduction in the sustained BOLD signal results from decreased levels of oxygenation or from increased levels of blood volume. We used conventional fMRI to quantify the BOLD signal and fMRI in combination with superparamagnetic contrast agent to quantify blood volume and employed repetition rate-modulated sounds in a silent background to manipulate the response amplitude in the auditory cortex. In the BOLD signal, the initial peak reached 3.3% with pulsed sound and 1.9% with continuous sound, whereas the sustained BOLD signal fell to 2.2% with pulsed sound and to 0.5% with continuous sound, respectively. The repetition rate-dependent reduction in the sustained BOLD amplitude was accompanied by concordant changes in sustained blood volume levels, which, compared to silence, increased by approximately 30% with pulsed and by approximately 10% with continuous sound. Thus, our data suggest that the reduced amplitude of the sustained BOLD signal reflects stimulus-dependent modulation of blood oxygenation rather than blood volume-related effects.

Temporal integration is a fundamental process that the brain carries out to construct coherent percepts from serial sensory events. This process critically depends on the formation of memory traces reconciling past with present events and is particularly important in the auditory domain where sensory information is received both serially and in parallel. It has been suggested that buffers for transient auditory memory traces reside in the auditory cortex. However, previous studies investigating "echoic memory" did not distinguish between brain response to novel auditory stimulus characteristics on the level of basic sound processing and a higher level involving matching of present with stored information. Here we used functional magnetic resonance imaging in combination with a regular pattern of sounds repeated every 100 ms and deviant interspersed stimuli of 100-ms duration, which were either brief presentations of louder sounds or brief periods of silence, to probe the formation of auditory memory traces. To avoid interaction with scanner noise, the auditory stimulation sequence was implemented into the image acquisition scheme. Compared to increased loudness events, silent periods produced specific neural activation in the right planum temporale and temporoparietal junction. Our findings suggest that this area posterior to the auditory cortex plays a critical role in integrating sequential auditory events and is involved in the formation of short-term auditory memory traces. This function of the planum temporale appears to be fundamental in the segregation of simultaneous sound sources.

The gradient switching during fast echoplanar functional magnetic resonance imaging (EPI-fMRI) produces loud noises that may interact with the functional activation of the central auditory system induced by experimental acoustic stimuli. This interaction is unpredictable and is likely to confound the interpretation of functional maps of the auditory cortex. In the present study we used an experimental design which does not require the presentation of stimuli during EPI acquisitions and allows for mapping of the auditory cortex without the interference of scanner noise. The design relies on the physiological delays between the onset, or the end, of stimulation and the corresponding hemodynamic response. Owing to these delays and through a time-resolved acquisition protocol it is possible to analyze the decay of the stimulus-specific signal changes after the cessation of the stimulus itself and before the onset of the EPI-acoustic noise related activation (decay-sampling technique). This experimental design, which might permit a more detailed insight in the auditory cortex, has been applied to the study of the cortical responses to pulsed 1000 Hz sine tones. Distinct activation clusters were detected in the Heschl's gyri and the planum temporale, with an increased extension compared to a conventional block-design paradigm. Furthermore, the comparison of the hemodynamic response of the most anterior and the posterior clusters of activation highlighted differential response patterns to the sound stimulation and to the EPI-noise. These differences, attributable to reciprocal saturation effects unevenly distributed over the superior temporal cortex, provided evidence for functionally distinct auditory fields., (Copyright 2001 Academic Press.)