D Mantini

Publications of D Mantini

• Comparison of hypothesis- and a novel hybrid data/hypothesis-driven method of functional MR imaging analysis in patients with brain gliomas.

  Authors: M Caulo, R Esposito, D Mantini, C Briganti, C Sestieri, P A Mattei, C Colosimo, G L Romani, A Tartaro

  AJNR. American journal of neuroradiology. 03/2011; 32(6):1056-64.

  An alternative technique, which is less influenced by tumor- and patient-related factors, is required to overcome the limits of GLM analysis of fMRI data in patients. The aim of this study was toAn alternative technique, which is less influenced by tumor- and patient-related factors, is required to overcome the limits of GLM analysis of fMRI data in patients. The aim of this study was to statistically assess differences in the identification of language regions and hemispheric lateralization of language function between controls and patients as estimated by both the GLM and a novel combined ICA-GLM procedure. We retrospectively evaluated 42 patients with pathologically confirmed brain gliomas of the left frontal and/or temporoparietal lobes and a control group of 14 age-matched healthy volunteers who underwent BOLD fMRI to lateralize language functions in the cerebral hemispheres. Data were processed by using a classic GLM and ICA-GLM. ICA-GLM demonstrated a higher sensitivity in detecting language activation, specifically in the left TPJ of patients. There were no significant differences between the GLM and ICA-GLM in controls; however, statistically significant differences were observed by using ICA-GLM for the LI in patients. For the computation of the LI, ICA-GLM was less influenced by the chosen statistical threshold compared with the GLM. We suggest the use of the ICA-GLM as a valid alternative to the classic GLM method for presurgical mapping in patients with brain tumors and to replicate the present results in a broader sample of patients.

• The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks.

  Authors: D Hartman, J Hlinka, M Palus, D Mantini, M Corbetta

  Chaos (Woodbury, N.Y.). 03/2011; 21(1):013119.

  In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magneticIn recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures-clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable.

• Functional connectivity MR imaging of the language network in patients with drug-resistant epilepsy.

  Authors: E Pravatà, C Sestieri, D Mantini, C Briganti, G Colicchio, C Marra, C Colosimo, A Tartaro, G L Romani, M Caulo

  AJNR. American journal of neuroradiology. 12/2010; 32(3):532-40.

  Subtle linguistic dysfunction and reorganization of the language network were described in patients with epilepsy, suggesting the occurrence of plasticity changes. We used resting state FC-MRI toSubtle linguistic dysfunction and reorganization of the language network were described in patients with epilepsy, suggesting the occurrence of plasticity changes. We used resting state FC-MRI to investigate the effects induced by chronic epilepsy on the connectivity of the language-related brain regions and correlated it with language performance. FC-MRI was evaluated in 22 right-handed patients with drug-resistant epilepsy (11 with LE and 11 with RE) and in 12 healthy volunteers. Neuropsychological assessment of verbal IQ was performed. Patients and controls underwent BOLD fMRI with a verb-generation task, and language function was lateralized by an LI. Intrinsic activity fluctuations for FC analysis were extracted from data collected during the task. Six seeding cortical regions for speech in both hemispheres were selected to obtain a measure of the connectivity pattern among the language networks. Patients with LE presented atypical language lateralization and an overall reduced connectivity of the language network with respect to controls. In patients with both LE and RE, the mean FC was significantly reduced within the left (dominant) hemisphere and between the 2 hemispheres. In patients with LE, there was a positive correlation between verbal IQ scores and the left intrahemispheric FC. In patients with intractable epilepsy, FC-MRI revealed an overall reduction and reorganization of the connectivity pattern within the language network. FC was reduced in the left hemisphere regardless of the epileptogenic focus side and was positively correlated with linguistic performance only in patients with LE.

• Multimodal integration of fMRI and EEG data for high spatial and temporal resolution analysis of brain networks.

  Authors: D Mantini, L Marzetti, M Corbetta, G L Romani, C Del Gratta

  Brain topography. 06/2010; 23(2):150-8.

  Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporalTwo major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli, respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes.

• Improving MEG source localizations: an automated method for complete artifact removal based on independent component analysis.

  Authors: D Mantini, R Franciotti, G L Romani, V Pizzella

  NeuroImage. 04/2008; 40(1):160-73.

  The major limitation for the acquisition of high-quality magnetoencephalography (MEG) recordings is the presence of disturbances of physiological and technical origins: eye movements, cardiacThe major limitation for the acquisition of high-quality magnetoencephalography (MEG) recordings is the presence of disturbances of physiological and technical origins: eye movements, cardiac signals, muscular contractions, and environmental noise are serious problems for MEG signal analysis. In the last years, multi-channel MEG systems have undergone rapid technological developments in terms of noise reduction, and many processing methods have been proposed for artifact rejection. Independent component analysis (ICA) has already shown to be an effective and generally applicable technique for concurrently removing artifacts and noise from the MEG recordings. However, no standardized automated system based on ICA has become available so far, because of the intrinsic difficulty in the reliable categorization of the source signals obtained with this technique. In this work, approximate entropy (ApEn), a measure of data regularity, is successfully used for the classification of the signals produced by ICA, allowing for an automated artifact rejection. The proposed method has been tested using MEG data sets collected during somatosensory, auditory and visual stimulation. It was demonstrated to be effective in attenuating both biological artifacts and environmental noise, in order to reconstruct clear signals that can be used for improving brain source localizations.

• MEG-EEG-fMRI: What can be gained in the Study of the Brain with a Multimodal Approach

  Authors: C. Del Gratta, M. Brunetti, D. Mantini, G.L. Romani

  Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 2007. NFSI-ICFBI 2007. Joint Meeting of the 6th International Symposium on; 11/2007

  In this short review we describe the potentialities, the difficulties, and the most common methods of combining EEG/MEG and fMRI data into a single neuroimaging technique with high spatial andIn this short review we describe the potentialities, the difficulties, and the most common methods of combining EEG/MEG and fMRI data into a single neuroimaging technique with high spatial and temporal resolution. Two examples of application to brain studies, from our own experience, are also described.

• High-resolution spatio-temporal neuronal activation in the visual oddball task: a simultaneous EEG/fMRI study

  Authors: L. Marzetti, D. Mantini, S. Cugini, G.L. Romani, C. Del Gratta

  Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 2007. NFSI-ICFBI 2007. Joint Meeting of the 6th International Symposium on; 11/2007

  The combined use of EEG and fMRI allows for the fusion of electrophysiological and hemodynamic information in the study of human cognitive functions. In order to investigate cerebral activity duringThe combined use of EEG and fMRI allows for the fusion of electrophysiological and hemodynamic information in the study of human cognitive functions. In order to investigate cerebral activity during a visual oddball task, simultaneous EEG/fMRI recording from 10 healthy subjects was performed. A devoted data-analysis method based on trial-by-trial coupling of concurrent EEG and fMRI for the high-resolution spatio-temporal analysis of P300 neuronal activation was developed. Our results obtained from fMRI data showed the involvement of inferior and medial frontal gyrus, cingulated motor area, middle temporal gyrus, and inferior parietal lobule in the oddball task; furthermore, activations were generally right lateralized, in accordance with previous findings. Using the high temporal resolution of EEG, we could separate neuronal activations specifically related to P300 activity, and therefore study the activation timing. We found that the detection of rare targets, that is able to elicit the P300 component, stimulates a limbic-parietofrontal circuit, with latencies ranging between 300 and 400 ms. Our findings suggest that the proposed approach might be extended to other event-related experimental paradigms, and might represent an valuable tool for a clearer understanding of the cerebral mechanisms underlying a wide range of cognitive functions.

• Fusion of EEG and fMRI for the investigation of functional connectivity during a visual oddball task

  Authors: D. Mantini, S. Cugini, G.L. Romani, C. Del Gratta

  Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 2007. NFSI-ICFBI 2007. Joint Meeting of the 6th International Symposium on; 11/2007

  The combined use of EEG and fMRI allows the fusion of electrophysiological and hemodynamic information for the study of the human brain function. In order to investigate functional connectivityThe combined use of EEG and fMRI allows the fusion of electrophysiological and hemodynamic information for the study of the human brain function. In order to investigate functional connectivity during a visual oddball task, we performed simultaneous EEG/fMRI recording from 14 healthy subjects. A completely data-driven approach, based on trial-by-trial coupling of concurrent EEG and fMRI, was used for the retrieval of cerebral networks linked to P300. An ICA-based analysis of fMRI BOLD data allowed to characterize neuro-anatomically defined patterns, each of them with a definite spatial map and a specific time-course of activation. In addition, we performed single-trial analysis on EEG data, reconstructing the temporal evolution of the P300 amplitude. This time-course, convolved with a canonical HRF, was considered as an estimate of the P300-related activity in the BOLD signals, and allowed to identify two cerebral networks linked to target detection. They showed spatial maps already observed in previous studies, which could be respectively associated with the dorsal attention and the change-detection networks. Time-domain analysis of BOLD data suggested that the attention network might account for sustained activity reflecting amplitude modulations of P300, whereas the change-detection network for transient activity associated with the event-related P300 response.

• Electrophysiological signatures of resting state networks in the human brain.

  Authors: D Mantini, M G Perrucci, C Del Gratta, G L Romani, M Corbetta

  Proceedings of the National Academy of Sciences of the United States of America. 08/2007; 104(32):13170-5.

  Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline isFunctional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.

• Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis.

  Authors: D Mantini, M G Perrucci, S. Cugini, A Ferretti, G L Romani, C Del Gratta

  NeuroImage. 02/2007; 34(2):598-607.

  The simultaneous recording of EEG and fMRI is a promising method for combining the electrophysiological and hemodynamic information on cerebral dynamics. However, EEG recordings performed in the MRIThe simultaneous recording of EEG and fMRI is a promising method for combining the electrophysiological and hemodynamic information on cerebral dynamics. However, EEG recordings performed in the MRI scanner are contaminated by imaging, ballistocardiographic (BCG) and ocular artifacts. A number of processing techniques for the cancellation of fMRI environment disturbances exist: the most popular is averaged artifact subtraction (AAS), which performs well for the imaging artifact, but has some limitations in removing the BCG artifact, due to the variability in cardiac wave duration and shape; furthermore, no processing method to attenuate ocular artifact is currently used in EEG/fMRI, and contaminated epochs are simply rejected before signal analysis. In this work, we present a comprehensive method based on independent component analysis (ICA) for simultaneously removing BCG and ocular artifacts from the EEG recordings, as well as residual MRI contamination left by AAS. The ICA method has been tested on event-related potentials (ERPs) obtained from a visual oddball paradigm: it is very effective in attenuating artifacts in order to reconstruct clear brain signals from EEG acquired in the MRI scanner. It performs significantly better than the AAS method in removing the BCG artifact. Furthermore, since ocular artifacts can be completely suppressed, a larger number of trials is available for analysis. A comparison of ERPs inside the magnetic environment with those obtained out of the MRI scanner confirms that no systematic bias in the ERP waveform is produced by the ICA method.

• Performance comparison of independent component analysis algorithms for fetal cardiac signal reconstruction: a study on synthetic fMCG data.

  Authors: D Mantini, K E Hild, G Alleva, S Comani

  Physics in medicine and biology. 03/2006; 51(4):1033-46.

  Independent component analysis (ICA) algorithms have been successfully used for signal extraction tasks in the field of biomedical signal processing. We studied the performances of six algorithmsIndependent component analysis (ICA) algorithms have been successfully used for signal extraction tasks in the field of biomedical signal processing. We studied the performances of six algorithms (FastICA, CubICA, JADE, Infomax, TDSEP and MRMI-SIG) for fetal magnetocardiography (fMCG). Synthetic datasets were used to check the quality of the separated components against the original traces. Real fMCG recordings were simulated with linear combinations of typical fMCG source signals: maternal and fetal cardiac activity, ambient noise, maternal respiration, sensor spikes and thermal noise. Clusters of different dimensions (19, 36 and 55 sensors) were prepared to represent different MCG systems. Two types of signal-to-interference ratios (SIR) were measured. The first involves averaging over all estimated components and the second is based solely on the fetal trace. The computation time to reach a minimum of 20 dB SIR was measured for all six algorithms. No significant dependency on gestational age or cluster dimension was observed. Infomax performed poorly when a sub-Gaussian source was included; TDSEP and MRMI-SIG were sensitive to additive noise, whereas FastICA, CubICA and JADE showed the best performances. Of all six methods considered, FastICA had the best overall performance in terms of both separation quality and computation times.

• Optimal filter design for shielded and unshielded ambient noise reduction in fetal magnetocardiography.

  Authors: S Comani, D Mantini, G Alleva, S Di Luzio, G L Romani

  Physics in medicine and biology. 01/2006; 50(23):5509-21.

  The greatest impediment to extracting high-quality fetal signals from fetal magnetocardiography (fMCG) is environmental magnetic noise, which may have peak-to-peak intensity comparable to fetal QRSThe greatest impediment to extracting high-quality fetal signals from fetal magnetocardiography (fMCG) is environmental magnetic noise, which may have peak-to-peak intensity comparable to fetal QRS amplitude. Being an unstructured Gaussian signal with large disturbances at specific frequencies, ambient field noise can be reduced with hardware-based approaches and/or with software algorithms that digitally filter magnetocardiographic recordings. At present, no systematic evaluation of filters' performances on shielded and unshielded fMCG is available. We designed high-pass and low-pass Chebychev II-type filters with zero-phase and stable impulse response; the most commonly used band-pass filters were implemented combining high-pass and low-pass filters. The achieved ambient noise reduction in shielded and unshielded recordings was quantified, and the corresponding signal-to-noise ratio (SNR) and signal-to-distortion ratio (SDR) of the retrieved fetal signals was evaluated. The study regarded 66 fMCG datasets at different gestational ages (22-37 weeks). Since the spectral structures of shielded and unshielded magnetic noise were very similar, we concluded that the same filter setting might be applied to both conditions. Band-pass filters (1.0-100 Hz) and (2.0-100 Hz) provided the best combinations of fetal signal detection rates, SNR and SDR; however, the former should be preferred in the case of arrhythmic fetuses, which might present spectral components below 2 Hz.

• A method for the automatic reconstruction of fetal cardiac signals from magnetocardiographic recordings.

  Authors: D Mantini, G Alleva, S Comani

  Physics in medicine and biology. 11/2005; 50(20):4763-81.

  Fetal magnetocardiography (fMCG) allows monitoring the fetal heart function through algorithms able to retrieve the fetal cardiac signal, but no standardized automatic model has become available soFetal magnetocardiography (fMCG) allows monitoring the fetal heart function through algorithms able to retrieve the fetal cardiac signal, but no standardized automatic model has become available so far. In this paper, we describe an automatic method that restores the fetal cardiac trace from fMCG recordings by means of a weighted summation of fetal components separated with independent component analysis (ICA) and identified through dedicated algorithms that analyse the frequency content and temporal structure of each source signal. Multichannel fMCG datasets of 66 healthy and 4 arrhythmic fetuses were used to validate the automatic method with respect to a classical procedure requiring the manual classification of fetal components by an expert investigator. ICA was run with input clusters of different dimensions to simulate various MCG systems. Detection rates, true negative and false positive component categorization, QRS amplitude, standard deviation and signal-to-noise ratio of reconstructed fetal signals, and real and per cent QRS differences between paired fetal traces retrieved automatically and manually were calculated to quantify the performances of the automatic method. Its robustness and reliability, particularly evident with the use of large input clusters, might increase the diagnostic role of fMCG during the prenatal period.

• Automatic detection of cardiac waves on fetal magnetocardiographic signals.

  Authors: S Comani, D Mantini, G Alleva, S Di Luzio, G L Romani

  Physiological measurement. 09/2005; 26(4):459-75.

  Fetal magnetocardiography (fMCG) provides fetal cardiac traces useful for the prenatal monitoring of fetal heart function. In this paper, we describe an analytical model (ACWD) for the automaticFetal magnetocardiography (fMCG) provides fetal cardiac traces useful for the prenatal monitoring of fetal heart function. In this paper, we describe an analytical model (ACWD) for the automatic detection of cardiac waves boundaries that works on fetal signals reconstructed from fMCG by means of independent component analysis. ACWD was validated for 45 healthy and 4 arrhythmic fetuses ranging from 22 to 37 weeks; ACWD outcomes were compared with the estimates of three independent investigators. Descriptive statistics were used to assess correspondence between the outcomes of the automatic and manual approaches. The parametric two-tailed Pearson correlation test (alpha=0.01) was employed to quantify, by means of the coefficients of determination, the amount of common variation between the sequences of intervals quantified automatically and manually. ACWD performances on short and long rhythm strips were investigated. ACWD demonstrated to be a robust tool providing dependable estimates of cardiac intervals and their variability during the third gestational trimester also in case of fetal arrhythmias. SNR and stability of fetal traces were the factors limiting ACWD performances. ACWD computation time, which was approximately 1:600 with respect to the manual procedure, was comparable with the time required for fCTI estimation on averaged beats.

• Simultaneous monitoring of separate fetal magnetocardiographic signals in twin pregnancy.

  Authors: S Comani, D Mantini, G Alleva, E Gabriele, M Liberati, G L Romani

  Physiological measurement. 07/2005; 26(3):193-201.

  Fetal magnetocardiography (fMCG) allows the non-invasive recording of fetal cardiac electrical activity with increasing efficacy as gestation progresses. Many reports on the successful extraction ofFetal magnetocardiography (fMCG) allows the non-invasive recording of fetal cardiac electrical activity with increasing efficacy as gestation progresses. Many reports on the successful extraction of reliable fetal magnetocardiographic traces in singleton pregnancies exist in the literature, whereas there is only one report on the reconstruction of averaged fetal cardiac signals obtained in a twin pregnancy with the use of a double sensor array system. In this paper, we aimed at assessing the effectiveness of an ICA-based procedure to reconstruct the time course of fetal cardiac signals recorded with a single-shot multi-channel fMCG device in an uncomplicated twin pregnancy at 27 weeks. The evaluation of heart rate and beats synchronicity permitted the differentiation of fetal components; the quality of reconstructed fetal signals allowed visual inspection on single cycles and the simultaneous monitoring of separate fetal heart rate patterns. The proposed technique might be applied in twin pregnancies not only to characterize fetal arrhythmias, but also in all cases of discordant fetal growth, either in the case of intra-uterine growth retardation affecting one fetus, or in the case of twin-twin transfusion syndrome, a life-threatening condition where both fetuses are at risk of heart failure.

• Integrated software suite for magnetocardiographic data analysis--a proposal based on an interactive programming environment.

  Authors: S Comani, D Mantini, B Merlino, M Reale, S Di Luzio, G L Romani

  Methods of information in medicine. 02/2005; 44(1):114-23.

  OBJECTIVES: This paper describes an integrated software suite (ISS) for the processing of magnetocardiographic (MCG) recordings obtained with super-conducting multi-channel systems having differentOBJECTIVES: This paper describes an integrated software suite (ISS) for the processing of magnetocardiographic (MCG) recordings obtained with super-conducting multi-channel systems having different characteristics. We aimed to develop a highly flexible suite including toolboxes for current MCG applications, organized consistently with an open architecture that allows function integrations and upgrades with minimal modifications; the suite was designed for the compliance not only of physicists and engineers but also of physicians, who have a different professional profile and are accustomed to retrieve information in different ways. METHODS: The MCG-ISS was designed to work with all common graphical user interface operative systems. MATLAB was chosen as the interactive programming environment (IPE), and the software was developed to achieve usability, interactivity, reliability, modularity, expansibility, interoperability, adaptability and graphics style tailoring. Three users, already experienced in MCG data analysis, have intensively tested MCG-ISS for six months. A great amount of MCG data on normal subjects and patients was used to assess software performances in terms of user compliance and confidence and total analysis time. RESULTS: The proposed suite is an all-in-one analysis tool that succeeded in speeding MCG data analysis up to about 55% with respect to standard reference routines; it consequently enhanced analysis performance and user compliance. CONCLUSIONS: Those results, together with the MCG-ISS advantage of being independent on the acquisition system, suggest that software suites like the proposed one could uphold a wider diffusion of MCG as a diagnostic tool in the clinical setting.

• Fetal magnetocardiographic mapping using independent component analysis.

  Authors: S Comani, D Mantini, G Alleva, S Di Luzio, G L Romani

  Physiological measurement. 01/2005; 25(6):1459-72.

  Fetal magnetocardiography (fMCG) is the only noninvasive technique allowing effective assessment of fetal cardiac electrical activity during the prenatal period. The reconstruction of reliableFetal magnetocardiography (fMCG) is the only noninvasive technique allowing effective assessment of fetal cardiac electrical activity during the prenatal period. The reconstruction of reliable magnetic field mapping associated with fetal heart activity would allow three-dimensional source localization. The efficiency of independent component analysis (ICA) in restoring reliable fetal traces from multichannel fMCG has already been demonstrated. In this paper, we describe a method of reconstructing a complete set of fetal signals hidden in multichannel fMCG preserving their correct spatial distribution, waveform, polarity and amplitude. Fetal independent components, retrieved with an ICA algorithm (FastICA), were interpolated (fICI method) using information gathered during FastICA iterations. The restored fetal signals were used to reconstruct accurate magnetic mapping for every millisecond during the average beat. The procedure was validated on fMCG recorded from the 22nd gestational week onward with a multichannel MCG system working in a shielded room. The interpolated traces were compared with those obtained with a standard technique, and the consistency of fetal mapping was checked evaluating source localizations relative to fetal echocardiographic information. Good magnetic field distributions during the P-QRS-T waves were attained with fICI for all gestational periods; their reliability was confirmed by three-dimensional source localizations.

• Time course reconstruction of fetal cardiac signals from fMCG: independent component analysis versus adaptive maternal beat subtraction.

  Authors: S Comani, D Mantini, A Lagatta, F Esposito, S Di Luzio, G L Romani

  Physiological measurement. 11/2004; 25(5):1305-21.

  M-mode and pulsed Doppler echocardiography, cardiotocography and transabdominal fetal ECG are available in clinical practice to monitor fetal cardiac activity during advancing gestation, but none ofM-mode and pulsed Doppler echocardiography, cardiotocography and transabdominal fetal ECG are available in clinical practice to monitor fetal cardiac activity during advancing gestation, but none of these methods allows the direct measurement of morphological and temporal parameters for fetal rhythm assessment. Fetal magnetocardiograms (fMCGs) are noninvasive recordings of magnetic field variations associated with electrical activity of the fetal heart obtained with superconducting sensors positioned over the maternal abdomen inside a shielded room. Because of maternal cardiac activity, fMCGs are contaminated by maternal components that need to be eliminated to reconstruct fetal cardiac traces. The aim of the present work was to use two methods working in the time domain, an independent component analysis algorithm (FastICA) and an adaptive maternal beat subtraction technique (AMBS), for the retrieval of fetal cardiac signals from fMCGs. Detection rates of both methods were calculated, and FastICA and AMBS performances were compared in the context of clinical applications by estimating several temporal and morphological characteristics of the retrieved fetal traces, such as the shape and duration P-QRS-T waves, arrhythmic beat detection and classification, and noise reduction. Quantitative and qualitative comparison produced figures that always suggested that FastICA was superior to AMBS from the perspective of clinical use of the recovered fetal signals.

• Functional Connectivity MR Imaging of the Language Network in Patients with Drug-Resistant Epilepsy

  Authors: E. Pravata, C. Sestieri, D. Mantini, C. Briganti, G. Colicchio, C. Marra, C. Colosimo, A. Tartaro, G. L. Romani, M. Caulo

  AJNR Am J Neuroradiol.

  BACKGROUND AND PURPOSE: Subtle linguistic dysfunction and reorganization of the language network were described in patients with epilepsy, suggesting the occurrence of plasticity changes. We usedBACKGROUND AND PURPOSE: Subtle linguistic dysfunction and reorganization of the language network were described in patients with epilepsy, suggesting the occurrence of plasticity changes. We used resting state FC-MRI to investigate the effects induced by chronic epilepsy on the connectivity of the language-related brain regions and correlated it with language performance. MATERIALS AND METHODS: FC-MRI was evaluated in 22 right-handed patients with drug-resistant epilepsy (11 with LE and 11 with RE) and in 12 healthy volunteers. Neuropsychological assessment of verbal IQ was performed. Patients and controls underwent BOLD fMRI with a verb-generation task, and language function was lateralized by an LI. Intrinsic activity fluctuations for FC analysis were extracted from data collected during the task. Six seeding cortical regions for speech in both hemispheres were selected to obtain a measure of the connectivity pattern among the language networks. RESULTS: Patients with LE presented atypical language lateralization and an overall reduced connectivity of the language network with respect to controls. In patients with both LE and RE, the mean FC was significantly reduced within the left (dominant) hemisphere and between the 2 hemispheres. In patients with LE, there was a positive correlation between verbal IQ scores and the left intrahemispheric FC. CONCLUSIONS: In patients with intractable epilepsy, FC-MRI revealed an overall reduction and reorganization of the connectivity pattern within the language network. FC was reduced in the left hemisphere regardless of the epileptogenic focus side and was positively correlated with linguistic performance only in patients with LE.

• Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis

  Authors: D. Mantini, M.G. Perrucci, S. Cugini, A. Ferretti, G.L. Romani, C. Del Gratta

  NeuroImage.

  The simultaneous recording of EEG and fMRI is a promising method for combining the electrophysiological and hemodynamic information on cerebral dynamics. However, EEG recordings performed in the MRIThe simultaneous recording of EEG and fMRI is a promising method for combining the electrophysiological and hemodynamic information on cerebral dynamics. However, EEG recordings performed in the MRI scanner are contaminated by imaging, ballistocardiographic (BCG) and ocular artifacts. A number of processing techniques for the cancellation of fMRI environment disturbances exist: the most popular is averaged artifact subtraction (AAS), which performs well for the imaging artifact, but has some limitations in removing the BCG artifact, due to the variability in cardiac wave duration and shape; furthermore, no processing method to attenuate ocular artifact is currently used in EEG/fMRI, and contaminated epochs are simply rejected before signal analysis. In this work, we present a comprehensive method based on independent component analysis (ICA) for simultaneously removing BCG and ocular artifacts from the EEG recordings, as well as residual MRI contamination left by AAS. The ICA method has been tested on event-related potentials (ERPs) obtained from a visual oddball paradigm: it is very effective in attenuating artifacts in order to reconstruct clear brain signals from EEG acquired in the MRI scanner. It performs significantly better than the AAS method in removing the BCG artifact. Furthermore, since ocular artifacts can be completely suppressed, a larger number of trials is available for analysis. A comparison of ERPs inside the magnetic environment with those obtained out of the MRI scanner confirms that no systematic bias in the ERP waveform is produced by the ICA method.