Biomedical subjects

Elia Formisano

Publications and source records attributed to Elia Formisano.

At least 19 recordsLinked to original sources

PubMed2006

Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers.

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.

Algorithms↗

PubMed2006

Phase coupling in a cerebro-cerebellar network at 8-13 Hz during reading.

Words forming a continuous story were presented to 9 subjects at frequencies ranging from 5 to 30 Hz, determined individually to render comprehension easy, effortful, or practically impossible. We identified a left-hemisphere neural network sensitive to reading performance directly from the time courses of activation in the brain, derived from magnetoencephalography data. Regardless of the stimulus rate, communication within the long-range neural network occurred at a frequency of 8-13 Hz. Our coherence-based detection of interconnected nodes reproduced several brain regions that have been previously reported as active in reading tasks, based on traditional contrast estimates. Intriguingly, the face motor cortex and the cerebellum, typically associated with speech production, and the orbitofrontal cortex, linked to visual recognition and working memory, additionally emerged as densely connected components of the network. The left inferior occipitotemporal cortex, involved in early letter-string or word-specific processing, and the cerebellum turned out to be the main forward driving nodes of the network. Synchronization within a subset of nodes formed by the left occipitotemporal, the left superior temporal, and orbitofrontal cortex was increased with the subjects' effort to comprehend the text. Our results link long-range neural synchronization and directionality with cognitive performance.

Adult↗

PubMed2006

The effect of temporal asynchrony on the multisensory integration of letters and speech sounds.

Temporal proximity is a critical determinant for cross-modal integration by multisensory neurons. Information content may serve as an additional binding factor for more complex or less natural multisensory information. Letters and speech sounds, which form the basis of literacy acquisition, are not naturally related but associated through explicit learning. We investigated the relative importance of temporal proximity and information content on the integration of letters and speech sounds by manipulating both factors within the same functional magnetic resonance imaging (fMRI) design. The results reveal significant interactions between temporal proximity and content congruency in anterior and posterior auditory association cortex, indicating that temporal synchrony is critical for the integration of letters and speech sounds. The temporal profiles for multisensory integration in the auditory association cortex resemble those demonstrated for single multisensory neurons in different brain structures and animal species. This similarity suggests that basic neural integration rules apply to the binding of multisensory information that is not naturally related but overlearned during literacy acquisition. Furthermore, the present study shows the suitability of fMRI to study temporal aspects of multisensory neural processing.

Acoustic Stimulation↗

PubMed2006

Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis.

The Functional Image Analysis Contest (FIAC) 2005 dataset was analyzed using BrainVoyager QX. First, we performed a standard analysis of the functional and anatomical data that includes preprocessing, spatial normalization into Talairach space, hypothesis-driven statistics (one- and two-factorial, single-subject and group-level random effects, General Linear Model [GLM]) of the block- and event-related paradigms. Strong sentence and weak speaker group-level effects were detected in temporal and frontal regions. Following this standard analysis, we performed single-subject and group-level (Talairach-based) Independent Component Analysis (ICA) that highlights the presence of functionally connected clusters in temporal and frontal regions for sentence processing, besides revealing other networks related to auditory stimulation or to the default state of the brain. Finally, we applied a high-resolution cortical alignment method to improve the spatial correspondence across brains and re-run the random effects group GLM as well as the group-level ICA in this space. Using spatially and temporally unsmoothed data, this cortex-based analysis revealed comparable results but with a set of spatially more confined group clusters and more differential group region of interest time courses.

Algorithms↗

PubMed2005

Mapping directed influence over the brain using Granger causality and fMRI.

We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity.

Attention↗

PubMed2005

Independent component analysis of fMRI group studies by self-organizing clustering.

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.

Adult↗

PubMed2005

The spatiotemporal pattern of auditory cortical responses during verbal hallucinations.

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.

Acoustic Stimulation↗

PubMed2004

Integration of letters and speech sounds in the human brain.

Most people acquire literacy skills with remarkable ease, even though the human brain is not evolutionarily adapted to this relatively new cultural phenomenon. Associations between letters and speech sounds form the basis of reading in alphabetic scripts. We investigated the functional neuroanatomy of the integration of letters and speech sounds using functional magnetic resonance imaging (fMRI). Letters and speech sounds were presented unimodally and bimodally in congruent or incongruent combinations. Analysis of single-subject data and group data aligned on the basis of individual cortical anatomy revealed that letters and speech sounds are integrated in heteromodal superior temporal cortex. Interestingly, responses to speech sounds in a modality-specific region of the early auditory cortex were modified by simultaneously presented letters. These results suggest that efficient processing of culturally defined associations between letters and speech sounds relies on neural mechanisms similar to those naturally evolved for integrating audiovisual speech.

Adult↗

PubMed2004

The functional neuroanatomy of metrical stress evaluation of perceived and imagined spoken words.

We hypothesized that areas in the temporal lobe that have been implicated in the phonological processing of spoken words would also be activated during the generation and phonological processing of imagined speech. We tested this hypothesis using functional magnetic resonance imaging during a behaviorally controlled task of metrical stress evaluation. Subjects were presented with bisyllabic words and had to determine the alternation of strong and weak syllables. Thus, they were required to discriminate between weak-initial words and strong-initial words. In one condition, the stimuli were presented auditorily to the subjects (by headphones). In the other condition the stimuli were presented visually on a screen and subjects were asked to imagine hearing the word. Results showed activation of the supplementary motor area, inferior frontal gyrus (Broca's area) and insula in both conditions. In the superior temporal gyrus (STG) and in the superior temporal sulcus (STS) strong activation was observed during the auditory (perceptual) condition. However, a region located in the posterior part of the STS/STG also responded during the imagery condition. No activation of this same region of the STS was observed during a control condition which also involved processing of visually presented words, but which required a semantic decision from the subject. We suggest that processing of metrical stress, with or without auditory input, relies in part on cortical interface systems located in the posterior part of STS/STG. These results corroborate behavioral evidence regarding phonological loop involvement in auditory-verbal imagery.

Acoustic Stimulation↗

PubMed2004

Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest.

Cortical functional connectivity, as indicated by the concurrent spontaneous activity of spatially segregated regions, is being studied increasingly because it may determine the reaction of the brain to external stimuli and task requirements and it is reportedly altered in many neurological and psychiatric disorders. In functional magnetic resonance imaging (fMRI), such functional connectivity is investigated commonly by correlating the time course of a chosen "seed voxel" with the remaining voxel time courses in a voxel-by-voxel manner. This approach is biased by the actual choice of the seed voxel, however, because it only shows functional connectivity for the chosen brain region while ignoring other potentially interesting patterns of coactivation. We used spatial independent component analysis (sICA) to assess cortical functional connectivity maps from resting state data. SICA does not depend on any chosen temporal profile of local brain activity. We hypothesized that sICA would be able to find functionally connected brain regions within sensory and motor regions in the absence of task-related brain activity. We also investigated functional connectivity patterns of several parietal regions including the superior parietal cortex and the posterior cingulate gyrus. The components of interest were selected in an automated fashion using predefined anatomical volumes of interest. SICA yielded connectivity maps of bilateral auditory, motor and visual cortices. Moreover, it showed that prefrontal and parietal areas are also functionally connected within and between hemispheres during the resting state. These connectivity maps showed an extremely high degree of consistency in spatial, temporal, and frequency parameters within and between subjects. These results are discussed in the context of the recent debate on the functional relevance of fluctuations of neural activity in the resting state.

Adult↗

PubMed2004

Cortex-based independent component analysis of fMRI time series.

The cerebral cortex is the main target of analysis in many functional magnetic resonance imaging (fMRI) studies. Since only about 20% of the voxels of a typical fMRI data set lie within the cortex, statistical analysis can be restricted to the subset of the voxels obtained after cortex segmentation. While such restriction does not influence conventional univariate statistical tests, it may have a substantial effect on the performance of multivariate methods. Here, we describe a novel approach for data-driven analysis of single-subject fMRI time series that combines techniques for the segmentation and reconstruction of the cortical surface of the brain and the spatial independent component analysis (sICA) of the functional time courses (TCs). We use the mesh of the white matter/gray matter boundary, automatically reconstructed from high-spatial-resolution anatomical MR images, to limit the sICA decomposition of a coregistered functional time series to those voxels which are within a specified region with respect to the cortical sheet (cortex-based ICA, or cbICA). We illustrate our analysis method in the context of fMRI blocked and event-related experimental designs and in an fMRI experiment with perceptually ambiguous stimulation, in which an a priori specification of the stimulation protocol is not possible. A comparison between cbICA and conventional hypothesis-driven statistical methods shows that cortical surface maps and component TCs blindly obtained with cbICA reliably reflect task-related spatiotemporal activation patterns. Furthermore, the advantages of using cbICA when the specification of a temporal model of the expected hemodynamic response is not straightforward are illustrated and discussed. A comparison between cbICA and anatomically unconstrained ICA reveals that--beside reducing computational demand--the cortex-based approach improves the fitting of the ICA model in the gray matter voxels, the separation of cortical components and the estimation of their TCs, particularly in the case of fMRI data sets with a complex spatiotemporal statistical structure.

Algorithms↗

PubMed2003

Mirror-symmetric tonotopic maps in human primary auditory cortex.

Understanding the functional organization of the human primary auditory cortex (PAC) is an essential step in elucidating the neural mechanisms underlying the perception of sound, including speech and music. Based on invasive research in animals, it is believed that neurons in human PAC that respond selectively with respect to the spectral content of a sound form one or more maps in which neighboring patches on the cortical surface respond to similar frequencies (tonotopic maps). The number and the cortical layout of such tonotopic maps in the human brain, however, remain unknown. Here we use silent, event-related functional magnetic resonance imaging at 7 Tesla and a cortex-based analysis of functional data to delineate with high spatial resolution the detailed topography of two tonotopic maps in two adjacent subdivisions of PAC. These maps share a low-frequency border, are mirror symmetric, and clearly resemble those of presumably homologous fields in the macaque monkey.

Acoustic Stimulation↗

PubMed2003

In vivo mapping of functional domains and axonal connectivity in cat visual cortex using magnetic resonance imaging.

Noninvasive cognitive neuroimaging studies based on functional magnetic resonance imaging (fMRI) are of ever-increasing importance for basic and clinical neurosciences. The explanatory power of fMRI could be greatly expanded, however, if the pattern of the neuronal circuitry underlying functional activation could be made visible in an equally noninvasive manner. In this study, blood oxygenation level-dependent (BOLD)-based fMRI and diffusion tensor imaging (DTI) were performed in the same cat visual cortex, and the foci of fMRI activation utilized as seeding points for 3D DTI fiber reconstruction algorithms, thus providing the map of the axonal circuitry underlying visual information processing. The methods developed in this study will lay the foundation for in vivo neuroanatomy and the ability for noninvasive longitudinal studies of brain development.

Algorithms↗

PubMed2003

fMRI of the auditory system: understanding the neural basis of auditory gestalt.

Functional magnetic resonance imaging (fMRI) has rapidly become the most widely used imaging method for studying brain functions in humans. This is a result of its extreme flexibility of use and of the astonishingly detailed spatial and temporal information it provides. Nevertheless, until very recently, the study of the auditory system has progressed at a considerably slower pace compared to other functional systems. Several factors have limited fMRI research in the auditory field, including some intrinsic features of auditory functional anatomy and some peculiar interactions between fMRI technique and audition. A well known difficulty arises from the high intensity acoustic noise produced by gradient switching in echo-planar imaging (EPI), as well as in other fMRI sequences more similar to conventional MR sequences. The acoustic noise interacts in an unpredictable way with the experimental stimuli both from a perceptual point of view and in the evoked hemodynamics. To overcome this problem, different approaches have been proposed recently that generally require careful tailoring of the experimental design and the fMRI methodology to the specific requirements posed by the auditory research. The novel methodological approaches can make the fMRI exploration of auditory processing much easier and more reliable, and thus may permit filling the gap with other fields of neuroscience research. As a result, some fundamental neural underpinnings of audition are being clarified, and the way sound stimuli are integrated in the auditory gestalt are beginning to be understood.

Auditory Cortex↗

PubMed2003

Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping.

We present a framework aimed to reveal directed interactions of activated brain areas using time-resolved fMRI and vector autoregressive (VAR) modeling in the context of Granger causality. After describing the underlying mathematical concepts, we present simulations helping to characterize the conditions under which VAR modeling and Granger causality can reveal directed interactions from fluctuations in BOLD-like signal time courses. We apply the proposed approach to a dynamic sensorimotor mapping paradigm. In an event-related fMRI experiment, subjects performed a visuomotor mapping task for which the mapping of two stimuli ("faces" vs "houses") to two responses ("left" or "right") alternated periodically between the two possible mappings. Besides expected activity in sensory and motor areas, a fronto-parietal network was found to be active during presentation of a cue indicating a change in the stimulus-response (S-R) mapping. The observed network includes the superior parietal lobule and premotor areas. These areas might be involved in setting up and maintaining stimulus-response associations. The Granger causality analysis revealed a directed influence exerted by the left lateral prefrontal cortex and premotor areas on the left posterior parietal cortex.

Brain Mapping↗

PubMed2003

Real-time independent component analysis of fMRI time-series.

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.

Algorithms↗

PubMed2003

Tracking cognitive processes with functional MRI mental chronometry.

Functional magnetic resonance imaging (fMRI) is used widely to determine the spatial layout of brain activation associated with specific cognitive tasks at a spatial scale of millimeters. Recent methodological improvements have made it possible to determine the latency and temporal structure of the activation at a temporal scale of few hundreds of milliseconds. Despite the sluggishness of the hemodynamic response, fMRI can detect a cascade of neural activations - the signature of a sequence of cognitive processes. Decomposing the processing into stages is greatly aided by measuring intermediate responses. By combining event-related fMRI and behavioral measurement in experiment and analysis, trial-by-trial temporal links can be established between cognition and its neural substrate.

Brain↗

PubMed2002

Activity patterns in human motion-sensitive areas depend on the interpretation of global motion.

Numerous imaging studies have contributed to the localization of motion-sensitive areas in the human brain. It is, however, still unclear how these areas contribute to global motion perception. Here, we investigate with functional MRI whether the motion-sensitive area hMT+/V5 is involved in perceptual segmentation and integration of motion signals. Stimuli were overlapping moving gratings that can be perceived either as two independently moving, transparent surfaces or as a single surface moving in an intermediate direction. We examined whether motion-sensitive area hMT+/V5 is involved in mediating the switches between the two percepts. The data show differential activation of hMT+/V5 with perceptual switches, suggesting that these are associated with a reconfiguration of cell assemblies in this area.

Brain↗