Sulcal pits of the superior temporal sulcus in schizophrenia patients with auditory verbal hallucinations.

Auditory verbal hallucinations (AVHs) are among the most common and disabling symptoms of schizophrenia. They involve the superior temporal sulcus (STS), which is associated with language processing; specific STS patterns may reflect vulnerability to auditory hallucinations in schizophrenia. STS sulcal pits are the deepest points of the folds in this region and were investigated here as an anatomical landmark of AVHs. This study included 53 patients diagnosed with schizophrenia and past or present AVHs, as well as 100 healthy control volunteers. All participants underwent a 3-T magnetic resonance imaging T1 brain scan, and sulcal pit differences were compared between the two groups. Compared with controls, patients with AVHs had a significantly different distributions for the number of sulcal pits in the left STS, indicating a less complex morphological pattern. The association of STS sulcal morphology with AVH suggests an early neurodevelopmental process in the pathophysiology of schizophrenia with AVHs.

Population-wise labeling of sulcal graphs using multi-graph matching.

Population-wise matching of the cortical folds is necessary to compute statistics, a required step for e.g. identifying biomarkers of neurological or psychiatric disorders. The difficulty arises from the massive inter-individual variations in the morphology and spatial organization of the folds. The task is challenging both methodologically and conceptually. In the widely used registration-based techniques, these variations are considered as noise and the matching of folds is only implicit. Alternative approaches are based on the extraction and explicit identification of the cortical folds. In particular, representing cortical folding patterns as graphs of sulcal basins-termed sulcal graphs-enables to formalize the task as a graph-matching problem. In this paper, we propose to address the problem of sulcal graph matching directly at the population level using multi-graph matching techniques. First, we motivate the relevance of the multi-graph matching framework in this context. We then present a procedure for generating populations of artificial sulcal graphs, which allows us to benchmark several state-of-the-art multi-graph matching methods. Our results on both artificial and real data demonstrate the effectiveness of multi-graph matching techniques in obtaining a population-wise consistent labeling of cortical folds at the sulcal basin level.

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data.

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.

Long-term follow-up of neurodegenerative phenomenon in severe traumatic brain injury using MRI.

BACKGROUND: Traumatic brain injury (TBI) lesions are known to evolve over time, but the duration and consequences of cerebral remodelling are unclear. Degenerative mechanisms occurring in the chronic phase after TBI could constitute "tertiary" lesions related to the neurological outcome. OBJECTIVE: The objective of this prospective study of severe TBI was to longitudinally evaluate the volume of white and grey matter structures and white matter integrity with 2 time-point multimodal MRI. METHODS: Longitudinal MRI follow-up was obtained for 11 healthy controls (HCs) and 22 individuals with TBI (mean [SD] 60 [15] months after injury) along with neuropsychological assessments. TBI individuals were classified in the "favourable" recovery group (Glasgow Outcome Scale Extended [GOSE] 6-8) and "unfavourable" recovery group (GOSE 3-5) at 5 years. Variation in brain volumes (3D T1-weighted image) and white matter integrity (diffusion tensor imaging [DTI]) were quantitatively assessed over time and used to predict neurological outcome. RESULTS: TBI individuals showed a marked decrease in volumes of whole white matter (median -11.4% [interquartile range -5.8; -14.6]; p < 0.001) and deep grey nuclear structures (-17.1% [-10.6; -20.5]; p < 0.001). HCs did not show any significant change over the same time period. Median volumetric loss in several brain regions was higher with GOSE 3-5 than 6-8. These lesions were associated with lower fractional anisotropy and higher mean diffusivity at baseline. Volumetric variations were positively correlated with normalized fractional anisotropy and negatively with normalized mean diffusivity at baseline and follow-up. A computed predictive model with baseline DTI showed good accuracy to predict neurological outcome (area under the receiver operating characteristic curve 0.82 [95% confidence interval 0.81-0.83]) CONCLUSIONS: We characterised the striking atrophy of deep brain structures after severe TBI. DTI imaging in the subacute phase can predict the occurrence and localization of these tertiary lesions as well as long-term neurological outcome. TRIAL REGISTRATION: ClinicalTrials.gov: NCT00577954. Registered on October 2006.

FMRI-based identity classification accuracy in left temporal and frontal regions predicts speaker recognition performance.

Speaker recognition is characterized by considerable inter-individual variability with poorly understood neural bases. This study was aimed at (1) clarifying the cerebral correlates of speaker recognition in humans, in particular the involvement of prefrontal areas, using multi voxel pattern analysis (MVPA) applied to fMRI data from a relatively large group of participants, and (2) at investigating the relationship across participants between fMRI-based classification and the group's variable behavioural performance at the speaker recognition task. A cohort of subjects (N = 40, 28 females) selected to present a wide distribution of voice recognition abilities underwent an fMRI speaker identification task during which they were asked to recognize three previously learned speakers with finger button presses. The results showed that speaker identity could be significantly decoded based on fMRI patterns in voice-sensitive regions including bilateral temporal voice areas (TVAs) along the superior temporal sulcus/gyrus but also in bilateral parietal and left inferior frontal regions. Furthermore, fMRI-based classification accuracy showed a significant correlation with individual behavioural performance in left anterior STG/STS and left inferior frontal gyrus. These results highlight the role of both temporal and extra-temporal regions in performing a speaker identity recognition task with motor responses.

Inter-subject pattern analysis for multivariate group analysis of functional neuroimaging. A unifying formalization.

BACKGROUND AND OBJECTIVE: In medical imaging, population studies have to overcome the differences that exist between individuals to identify invariant image features that can be used for diagnosis purposes. In functional neuroimaging, an appealing solution to identify neural coding principles that hold at the population level is inter-subject pattern analysis, i.e. to learn a predictive model on data from multiple subjects and evaluate its generalization performance on new subjects. Although it has gained popularity in recent years, its widespread adoption is still hampered by the blatant lack of a formal definition in the literature. In this paper, we precisely introduce the first principled formalization of inter-subject pattern analysis targeted at multivariate group analysis of functional neuroimaging. METHODS: We propose to frame inter-subject pattern analysis as a multi-source transductive transfer question, thus grounding it within several well defined machine learning settings and broadening the spectrum of usable algorithms. We describe two sets of inter-subject brain decoding experiments that use several open datasets: a magneto-encephalography study with 16 subjects and a functional magnetic resonance imaging paradigm with 100 subjects. We assess the relevance of our framework by performing model comparisons, where one brain decoding model exploits our formalization while others do not. RESULTS: The first set of experiments demonstrates the superiority of a brain decoder that uses subject-by-subject standardization compared to state of the art models that use other standardization schemes, making the case for the interest of the transductive and the multi-source components of our formalization The second set of experiments quantitatively shows that, even after such transformation, it is more difficult for a brain decoder to generalize to new participants rather than to new data from participants available in the training phase, thus highlighting the transfer gap that needs to be overcome. CONCLUSION: This paper describes the first formalization of inter-subject pattern analysis as a multi-source transductive transfer learning problem. We demonstrate the added value of this formalization using proof-of-concept experiments on several complementary functional neuroimaging datasets. This work should contribute to popularize inter-subject pattern analysis for functional neuroimaging population studies and pave the road for future methodological innovations.

Single-trial fMRI activation maps measured during the InterTVA event-related voice localizer. A data set ready for inter-subject pattern analysis.

Multivariate pattern analysis (MVPA) of functional neuroimaging data has emerged as a key tool for studying the cognitive architecture of the human brain. At the group level, we have recently demonstrated the advantages of an under-exploited scheme that consists in training a machine learning model on data from a set of subjects and evaluating its generalization ability on data from unseen subjects (see Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA [1]). We here provide a data set that is fully ready to perform inter-subject pattern analysis, which includes 5616 single-trial brain activation maps recorded in 39 participants who were scanned using functional magnetic resonance imaging (fMRI) with a voice localizer paradigm. This data set should therefore reveal valuable for data scientists developing brain decoding algorithms as well as cognitive neuroscientists interested in voice perception.

Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA.

Multivariate pattern analysis (MVPA) has become vastly popular for analyzing functional neuroimaging data. At the group level, two main strategies are used in the literature. The standard one is hierarchical, combining the outcomes of within-subject decoding results in a second-level analysis. The alternative one, inter-subject pattern analysis, directly works at the group-level by using, e.g. a leave-one-subject-out cross-validation. This study provides a thorough comparison of these two group-level decoding schemes, using both a large number of artificial datasets where the size of the multivariate effect and the amount of inter-individual variability are parametrically controlled, as well as two real fMRI datasets comprising 15 and 39 subjects, respectively. We show that these two strategies uncover distinct significant regions with partial overlap, and that inter-subject pattern analysis is able to detect smaller effects and to facilitate the interpretation. The core source code and data are openly available, allowing to fully reproduce most of these results.

Functional connectivity within the voice perception network and its behavioural relevance.

Recognizing who is speaking is a cognitive ability characterized by considerable individual differences, which could relate to the inter-individual variability observed in voice-elicited BOLD activity. Since voice perception is sustained by a complex brain network involving temporal voice areas (TVAs) and, even if less consistently, extra-temporal regions such as frontal cortices, functional connectivity (FC) during an fMRI voice localizer (passive listening of voices vs non-voices) has been computed within twelve temporal and frontal voice-sensitive regions ("voice patches") individually defined for each subject (N = 90) to account for inter-individual variability. Results revealed that voice patches were positively co-activated during voice listening and that they were characterized by different FC pattern depending on the location (anterior/posterior) and the hemisphere. Importantly, FC between right frontal and temporal voice patches was behaviorally relevant: FC significantly increased with voice recognition abilities as measured in a voice recognition test performed outside the scanner. Hence, this study highlights the importance of frontal regions in voice perception and it supports the idea that looking at FC between stimulus-specific and higher-order frontal regions can help understanding individual differences in processing social stimuli such as voices.

Cross-classification of musical and vocal emotions in the auditory cortex.

Whether emotions carried by voice and music are processed by the brain using similar mechanisms has long been investigated. Yet neuroimaging studies do not provide a clear picture, mainly due to lack of control over stimuli. Here, we report a functional magnetic resonance imaging (fMRI) study using comparable stimulus material in the voice and music domains-the Montreal Affective Voices and the Musical Emotional Bursts-which include nonverbal short bursts of happiness, fear, sadness, and neutral expressions. We use a multivariate emotion-classification fMRI analysis involving cross-timbre classification as a means of comparing the neural mechanisms involved in processing emotional information in the two domains. We find, for affective stimuli in the violin, clarinet, or voice timbres, that local fMRI patterns in the bilateral auditory cortex and upper premotor regions support above-chance emotion classification when training and testing sets are performed within the same timbre category. More importantly, classifier performance generalized well across timbre in cross-classifying schemes, albeit with a slight accuracy drop when crossing the voice-music boundary, providing evidence for a shared neural code for processing musical and vocal emotions, with possibly a cost for the voice due to its evolutionary significance.

Enhanced Neonatal Brain Responses To Sung Streams Predict Vocabulary Outcomes By Age 18 Months.

Words and melodies are some of the basic elements infants are able to extract early in life from the auditory input. Whether melodic cues contained in songs can facilitate word-form extraction immediately after birth remained unexplored. Here, we provided converging neural and computational evidence of the early benefit of melodies for language acquisition. Twenty-eight neonates were tested on their ability to extract word-forms from continuous flows of sung and spoken syllabic sequences. We found different brain dynamics for sung and spoken streams and observed successful detection of word-form violations in the sung condition only. Furthermore, neonatal brain responses for sung streams predicted expressive vocabulary at 18 months as demonstrated by multiple regression and cross-validation analyses. These findings suggest that early neural individual differences in prosodic speech processing might be a good indicator of later language outcomes and could be considered as a relevant factor in the development of infants' language skills.

Improving voltage-sensitive dye imaging: with a little help from computational approaches.

Voltage-sensitive dye imaging (VSDI) is a key neurophysiological recording tool because it reaches brain scales that remain inaccessible to other techniques. The development of this technique from in vitro to the behaving nonhuman primate has only been made possible thanks to the long-lasting, visionary work of Amiram Grinvald. This work has opened new scientific perspectives to the great benefit to the neuroscience community. However, this unprecedented technique remains largely under-utilized, and many future possibilities await for VSDI to reveal new functional operations. One reason why this tool has not been used extensively is the inherent complexity of the signal. For instance, the signal reflects mainly the subthreshold neuronal population response and is not linked to spiking activity in a straightforward manner. Second, VSDI gives access to intracortical recurrent dynamics that are intrinsically complex and therefore nontrivial to process. Computational approaches are thus necessary to promote our understanding and optimal use of this powerful technique. Here, we review such approaches, from computational models to dissect the mechanisms and origin of the recorded signal, to advanced signal processing methods to unravel new neuronal interactions at mesoscopic scale. Only a stronger development of interdisciplinary approaches can bridge micro- to macroscales.

Social and asocial prefrontal cortex neurons: a new look at social facilitation and the social brain.

A fundamental aspect of behavior in many animal species is 'social facilitation', the positive effect of the mere presence of conspecifics on performance. To date, the neuronal counterpart of this ubiquitous phenomenon is unknown. We recorded the activity of single neurons from two prefrontal cortex regions, the dorsolateral part and the anterior cingulate cortex in monkeys as they performed a visuomotor task, either in the presence of a conspecific (Presence condition) or alone. Monkeys performed better in the presence condition than alone (social facilitation), and analyses of outcome-related activity of 342 prefrontal neurons revealed that most of them (86%) were sensitive to the performance context. Two populations of neurons were discovered: 'social neurons', preferentially active under social presence and 'asocial neurons', preferentially active under social isolation. The activity of these neurons correlated positively with performance only in their preferred context (social neurons under social presence; asocial neurons under social isolation), thereby providing a potential neuronal mechanism of social facilitation. More generally, the fact that identical tasks recruited either social or asocial neurons depending on the presence or absence of a conspecific also brings a new look at the social brain hypothesis.

Structural graph-based morphometry: A multiscale searchlight framework based on sulcal pits.

Studying the topography of the cortex has proved valuable in order to characterize populations of subjects. In particular, the recent interest towards the deepest parts of the cortical sulci - the so-called sulcal pits - has opened new avenues in that regard. In this paper, we introduce the first fully automatic brain morphometry method based on the study of the spatial organization of sulcal pits - Structural Graph-Based Morphometry (SGBM). Our framework uses attributed graphs to model local patterns of sulcal pits, and further relies on three original contributions. First, a graph kernel is defined to provide a new similarity measure between pit-graphs, with few parameters that can be efficiently estimated from the data. Secondly, we present the first searchlight scheme dedicated to brain morphometry, yielding dense information maps covering the full cortical surface. Finally, a multi-scale inference strategy is designed to jointly analyze the searchlight information maps obtained at different spatial scales. We demonstrate the effectiveness of our framework by studying gender differences and cortical asymmetries: we show that SGBM can both localize informative regions and estimate their spatial scales, while providing results which are consistent with the literature. Thanks to the modular design of our kernel and the vast array of available kernel methods, SGBM can easily be extended to include a more detailed description of the sulcal patterns and solve different statistical problems. Therefore, we suggest that our SGBM framework should be useful for both reaching a better understanding of the normal brain and defining imaging biomarkers in clinical settings.

Probing the functional impact of sub-retinal prosthesis.

Retinal prostheses are promising tools for recovering visual functions in blind patients but, unfortunately, with still poor gains in visual acuity. Improving their resolution is thus a key challenge that warrants understanding its origin through appropriate animal models. Here, we provide a systematic comparison between visual and prosthetic activations of the rat primary visual cortex (V1). We established a precise V1 mapping as a functional benchmark to demonstrate that sub-retinal implants activate V1 at the appropriate position, scalable to a wide range of visual luminance, but with an aspect-ratio and an extent much larger than expected. Such distorted activation profile can be accounted for by the existence of two sources of diffusion, passive diffusion and activation of ganglion cells' axons en passant. Reverse-engineered electrical pulses based on impedance spectroscopy is the only solution we tested that decreases the extent and aspect-ratio, providing a promising solution for clinical applications.

Graph-based inter-subject pattern analysis of FMRI data.

In brain imaging, solving learning problems in multi-subjects settings is difficult because of the differences that exist across individuals. Here we introduce a novel classification framework based on group-invariant graphical representations, allowing to overcome the inter-subject variability present in functional magnetic resonance imaging (fMRI) data and to perform multivariate pattern analysis across subjects. Our contribution is twofold: first, we propose an unsupervised representation learning scheme that encodes all relevant characteristics of distributed fMRI patterns into attributed graphs; second, we introduce a custom-designed graph kernel that exploits all these characteristics and makes it possible to perform supervised learning (here, classification) directly in graph space. The well-foundedness of our technique and the robustness of the performance to the parameter setting are demonstrated through inter-subject classification experiments conducted on both artificial data and a real fMRI experiment aimed at characterizing local cortical representations. Our results show that our framework produces accurate inter-subject predictions and that it outperforms a wide range of state-of-the-art vector- and parcel-based classification methods. Moreover, the genericity of our method makes it is easily adaptable to a wide range of potential applications. The dataset used in this study and an implementation of our framework are available at http://dx.doi.org/10.6084/m9.figshare.1086317.

Faster sound stream segmentation in musicians than in nonmusicians.

The musician's brain is considered as a good model of brain plasticity as musical training is known to modify auditory perception and related cortical organization. Here, we show that music-related modifications can also extend beyond motor and auditory processing and generalize (transfer) to speech processing. Previous studies have shown that adults and newborns can segment a continuous stream of linguistic and non-linguistic stimuli based only on probabilities of occurrence between adjacent syllables, tones or timbres. The paradigm classically used in these studies consists of a passive exposure phase followed by a testing phase. By using both behavioural and electrophysiological measures, we recently showed that adult musicians and musically trained children outperform nonmusicians in the test following brief exposure to an artificial sung language. However, the behavioural test does not allow for studying the learning process per se but rather the result of the learning. In the present study, we analyze the electrophysiological learning curves that are the ongoing brain dynamics recorded as the learning is taking place. While musicians show an inverted U shaped learning curve, nonmusicians show a linear learning curve. Analyses of Event-Related Potentials (ERPs) allow for a greater understanding of how and when musical training can improve speech segmentation. These results bring evidence of enhanced neural sensitivity to statistical regularities in musicians and support the hypothesis of positive transfer of training effect from music to sound stream segmentation in general.

Vobi One: a data processing software package for functional optical imaging.

Optical imaging is the only technique that allows to record the activity of a neuronal population at the mesoscopic scale. A large region of the cortex (10-20 mm diameter) is directly imaged with a CCD camera while the animal performs a behavioral task, producing spatio-temporal data with an unprecedented combination of spatial and temporal resolutions (respectively, tens of micrometers and milliseconds). However, researchers who have developed and used this technique have relied on heterogeneous software and methods to analyze their data. In this paper, we introduce Vobi One, a software package entirely dedicated to the processing of functional optical imaging data. It has been designed to facilitate the processing of data and the comparison of different analysis methods. Moreover, it should help bring good analysis practices to the community because it relies on a database and a standard format for data handling and it provides tools that allow producing reproducible research. Vobi One is an extension of the BrainVISA software platform, entirely written with the Python programming language, open source and freely available for download at https://trac.int.univ-amu.fr/vobi_one.

A processing work-flow for measuring erythrocytes velocity in extended vascular networks from wide field high-resolution optical imaging data.

Comprehensive information on the spatio-temporal dynamics of the vascular response is needed to underpin the signals used in hemodynamics-based functional imaging. It has recently been shown that red blood cells (RBCs) velocity and its changes can be extracted from wide-field optical imaging recordings of intrinsic absorption changes in cortex. Here, we describe a complete processing work-flow for reliable RBC velocity estimation in cortical networks. Several pre-processing steps are implemented: image co-registration, necessary to correct for small movements of the vasculature, semi-automatic image segmentation for fast and reproducible vessel selection, reconstruction of RBC trajectories patterns for each micro-vessel, and spatio-temporal filtering to enhance the desired data characteristics. The main analysis step is composed of two robust algorithms for estimating the RBCs' velocity field. Vessel diameter and its changes are also estimated, as well as local changes in backscattered light intensity. This full processing chain is implemented with a software suite that is freely distributed. The software uses efficient data management for handling the very large data sets obtained with in vivo optical imaging. It offers a complete and user-friendly graphical user interface with visualization tools for displaying and exploring data and results. A full data simulation framework is also provided in order to optimize the performances of the algorithm with respect to several characteristics of the data. We illustrate the performance of our method in three different cases of in vivo data. We first document the massive RBC speed response evoked by a spreading depression in anesthetized rat somato-sensory cortex. Second, we show the velocity response elicited by a visual stimulation in anesthetized cat visual cortex. Finally, we report, for the first time, visually-evoked RBC speed responses in an extended vascular network in awake monkey extrastriate cortex.

Multivoxel pattern analysis for FMRI data: a review.

Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC) curves.