Network-specific differences in transient brain activity at rest are associated with age-related reductions in motor performance.

Multiple functional changes occur in the brain with increasing age. Among those, older adults typically display more restricted fluctuations of brain activity, both during resting-state and task execution. These altered dynamic patterns have been linked to reduced task performance across multiple behavioral domains. Windowed functional connectivity, which is typically employed in the study of connectivity dynamics, however, might not be able to properly characterize moment-to-moment variations of individual networks. In the present study, we used innovation-driven co-activation patterns (ICAP) to overcome this limitation and investigate the length (duration) and frequency (innovation) in which various brain networks emerged across the adult lifespan (N= 92) during a resting-state period. We identified a link between increasing age and a tendency to engage brain areas with distinct functional associations simultaneously as a single network. The emergence of isolated and spatially well-defined visual, motor, frontoparietal, and posterior networks decreased with increased age. This reduction in dynamics of specialized networks mediated age-related performance decreases (i.e., increases in interlimb interference) in a bimanual motor task. Altogether, our findings demonstrated that older compared to younger adults tend to activate fewer network configurations, which include multiple functionally distinct brain areas. The reduction in independent emergence of functionally well-defined and task-relevant networks may reflect an expression of brain dedifferentiation and is likely associated with functional modulatory deficits, negatively impacting motor behavior.

Task-related measures of short-interval intracortical inhibition and GABA levels in healthy young and older adults: A multimodal TMS-MRS study.

Establishing the associations between magnetic resonance spectroscopy (MRS)-assessed gamma-aminobutyric acid (GABA) levels and transcranial magnetic stimulation (TMS)-derived 'task-related' modulations in GABAA receptor-mediated inhibition and how these associations change with advancing age is a topic of interest in the field of human neuroscience. In this study, we identified the relationship between GABA levels and task-related modulations in GABAA receptor-mediated inhibition in the dominant (left) and non-dominant (right) sensorimotor (SM) cortices. GABA levels were measured using edited MRS and task-related GABAA receptor-mediated inhibition was measured using a short-interval intracortical inhibition (SICI) TMS protocol during the preparation and premotor period of a choice reaction time (CRT) task in 25 young (aged 18-33 years) and 25 older (aged 60-74 years) adults. Our results demonstrated that GABA levels in both SM voxels were lower in older adults as compared to younger adults; and higher SM GABA levels in the dominant as compared to the non-dominant SM voxel pointed to a lateralization effect, irrespective of age group. Furthermore, older adults showed decreased GABAA receptor-mediated inhibition in the preparation phase of the CRT task within the dominant primary motor cortex (M1), as compared to young adults. Finally, results from an exploratory correlation analysis pointed towards positive relationships between MRS-assessed GABA levels and TMS-derived task-related SICI measures. However, after correction for multiple comparisons none of the correlations remained significant.

Functional specialization of macaque premotor F5 subfields with respect to hand and mouth movements: A comparison of task and resting-state fMRI.

Based on architectonic, tract-tracing or functional criteria, the rostral portion of ventral premotor cortex in the macaque monkey, also termed area F5, has been divided into several subfields. Cytoarchitectonical investigations suggest the existence of three subfields, F5c (convexity), F5p (posterior) and F5a (anterior). Electrophysiological investigations have suggested a gradual dorso-ventral transition from hand- to mouth-dominated motor fields, with F5p and ventral F5c strictly related to hand movements and mouth movements, respectively. The involvement of F5a in this respect, however, has received much less attention. Recently, data-driven resting-state fMRI approaches have also been used to examine the presence of distinct functional fields in macaque ventral premotor cortex. Although these studies have suggested several functional clusters in/near macaque F5, so far the parcellation schemes derived from these clustering methods do not completely retrieve the same level of F5 specialization as suggested by aforementioned invasive techniques. Here, using seed-based resting-state fMRI analyses, we examined the functional connectivity of different F5 seeds with key regions of the hand and face/mouth parieto-frontal-insular motor networks. In addition, we trained monkeys to perform either hand grasping or ingestive mouth movements in the scanner in order to compare resting-state with task-derived functional hand and mouth motor networks. In line with previous single-cell investigations, task-fMRI suggests involvement of F5p, dorsal F5c and F5a in the execution of hand grasping movements, while non-communicative mouth movements yielded particularly pronounced responses in ventral F5c. Corroborating with anatomical tracing data of macaque F5 subfields, seed-based resting-state fMRI suggests a transition from predominant functional correlations with the hand-motor network in F5p to mostly mouth-motor network functional correlations in ventral F5c. Dorsal F5c yielded robust functional correlations with both hand- and mouth-motor networks. In addition, the deepest part of the fundus of the inferior arcuate, corresponding to area 44, displayed a strikingly different functional connectivity profile compared to neighboring F5a, suggesting a different functional specialization for these two neighboring regions.

Age-related differences in network flexibility and segregation at rest and during motor performance.

Brain networks undergo widespread changes in older age. A large body of knowledge gathered about those changes evidenced an increase of functional connectivity between brain networks. Previous work focused mainly on cortical networks during the resting state. Subcortical structures, however, are of critical importance during the performance of motor tasks. In this study, we investigated age-related changes in cortical, striatal and cerebellar functional connectivity at rest and its modulation by motor task execution. To that end, functional MRI from twenty-five young (mean age 21.5 years) and eighteen older adults (mean age 68.6 years) were analysed during rest and while performing a bimanual tracking task practiced over a two-week period. We found that inter-network connectivity among cortical structures was more positive in older adults both during rest and task performance. Functional connectivity within striatal structures decreased with age during rest and task execution. Network flexibility, the changes in network composition from rest to task, was also reduced in older adults, but only in networks with an age-related increase in connectivity. Finally, flexibility of areas in the prefrontal cortex were associated with lower error scores during task execution, especially in older adults. In conclusion, our findings indicate an age-related reduction in the ability to suppress irrelevant network communication, leading to less segregated and less flexible cortical networks. At the same time, striatal connectivity is impaired in older adults, while cerebellar connectivity shows heterogeneous age-related effects during rest and task execution. Future research is needed to clarify how cortical and subcortical connectivity changes relate to one another.

Exploring quantitative group-wise differentiation of Alzheimer's disease and behavioural variant frontotemporal dementia using tract-specific microstructural white matter and functional connectivity measures at multiple time points.

OBJECTIVES: This study explored group-wise quantitative measures of tract-specific white matter (WM) microstructure and functional default mode network (DMN) connectivity to establish an initial indication of their clinical applicability for early-stage and follow-up differential diagnosis of Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD). METHODS: Eleven AD and 12 bvFTD early-stage patients and 18 controls underwent diffusion tensor imaging and resting state functional magnetic resonance imaging at 3 T. All AD and 6 bvFTD patients underwent the same protocol at 1-year follow-up. Functional connectivity measures of DMN and WM tract-specific diffusivity measures were determined for all groups. Exploratory analyses were performed to compare all measures between the three groups at baseline and between patients at follow-up. Additionally, the difference between baseline and follow-up diffusivity measures in AD and bvFTD patients was compared. RESULTS: Functional connectivity of the DMN was not different between groups at baseline and at follow-up. Diffusion abnormalities were observed widely in bvFTD and regionally in the hippocampal cingulum in AD. The extent of the differences between bvFTD and AD was diminished at follow-up, yet abnormalities were still more pronounced in bvFTD. The rate of change was similar in bvFTD and AD. CONCLUSIONS: This study provides a tentative indication that quantitative tract-specific microstructural WM abnormalities, but not quantitative functional connectivity of the DMN, may aid early-stage and follow-up differential diagnosis of bvFTD and AD. Specifically, pronounced microstructural changes in anterior WM tracts may characterise bvFTD, whereas microstructural abnormalities of the hippocampal cingulum may characterise AD. KEY POINTS: • The clinical applicability of quantitative brain imaging measures for early-stage and follow-up differential diagnosis of dementia subtypes was explored using a group-wise approach. • Quantitative tract-specific microstructural white matter abnormalities, but not quantitative functional connectivity of the default mode network, may aid early-stage and follow-up differential diagnosis of behavioural variant frontotemporal dementia and Alzheimer's disease. • Pronounced microstructural white matter (WM) changes in anterior WM tracts characterise behavioural variant frontotemporal dementia, whereas microstructural WM abnormalities of the hippocampal cingulum in the absence of other WM changes characterise Alzheimer's disease.

Age-Related Declines in Motor Performance are Associated With Decreased Segregation of Large-Scale Resting State Brain Networks.

Aging is typically associated with substantial declines in motor functioning as well as robust changes in the functional organization of brain networks. Previous research has investigated the link between these 2 age-varying factors but examinations were predominantly limited to the functional organization within motor-related brain networks. Little is known about the relationship between age-related behavioral impairments and changes in functional organization at the whole brain (i.e., multiple network) level. This knowledge gap is surprising given that the decreased segregation of brain networks (i.e., increased internetwork connectivity) can be considered a hallmark of the aging process. Accordingly, we investigated the association between declines in motor performance across the adult lifespan (20-75 years) and age-related modulations of functional connectivity within and between resting state networks. Results indicated that stronger internetwork resting state connectivity observed as a function of age was significantly related to worse motor performance. Moreover, performance had a significantly stronger association with the strength of internetwork as compared with intranetwork connectivity, including connectivity within motor networks. These findings suggest that age-related declines in motor performance may be attributed to a breakdown in the functional organization of large-scale brain networks rather than simply age-related connectivity changes within motor-related networks.

Source-reconstruction of the sensorimotor network from resting-state macaque electrocorticography.

The discovery of hemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electric cortical rhythms are organized into the same RSNs as hemodynamic signals. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or scalp electroencephalography (EEG), which limits the spatial resolution with which electrophysiological RSNs can be observed. Due to their close proximity to the cortical surface, electrocorticographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms, albeit at the expense of spatial coverage. In this study we propose using source-space spatial independent component analysis (spatial ICA) for identifying generators of resting-state cortical rhythms as recorded with ECoG and for reconstructing their functional connectivity. Network structure is assessed by two kinds of connectivity measures: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. By simulating rhythmic cortical generators, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated, which troubles the interpretation of lag-based connectivity measures. We illustrate the methodology on somatosensory beta rhythms recorded from a macaque monkey using ECoG. The methodology decomposes the resting-state sensorimotor network into three cortical generators, distributed across primary somatosensory and primary and higher-order motor areas. The generators display significant and reproducible amplitude correlations and phase-locking values with non-zero lags. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human.

Coordinative task difficulty and behavioural errors are associated with increased long-range beta band synchronization.

The neural network and the task-dependence of (local) activity changes involved in bimanual coordination are well documented. However, much less is known about the functional connectivity within this neural network and its modulation according to manipulations of task complexity. Here, we assessed neural activity via high-density electroencephalography, focussing on changes of activity in the beta frequency band (~15-30Hz) across the motor network in 26 young adult participants (19-29 years old). We investigated how network connectivity was modulated with task difficulty and errors of performance during a bimanual visuomotor movement consisting of dial rotation according to three different ratios of speed: an isofrequency movement (1:1), a non-isofrequency movement with the right hand keeping the fast pace (1:3), and the converse ratio with the left hand keeping the fast pace (3:1). To quantify functional coupling, we determined neural synchronization which might be key for the timing of the activity within brain regions during task execution. Individual source activity with realistic head models was reconstructed at seven regions of interest including frontal and parietal areas, among which we estimated phase-based connectivity. Partial least squares analysis revealed a significant modulation of connectivity with task difficulty, and significant correlations between connectivity and errors in performance, in particular between sensorimotor cortices. Our findings suggest that modulation of long-range synchronization is instrumental for coping with increasing task demands in bimanual coordination.

Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?

During the last several years, the focus of research on resting-state functional magnetic resonance imaging (fMRI) has shifted from the analysis of functional connectivity averaged over the duration of scanning sessions to the analysis of changes of functional connectivity within sessions. Although several studies have reported the presence of dynamic functional connectivity (dFC), statistical assessment of the results is not always carried out in a sound way and, in some studies, is even omitted. In this study, we explain why appropriate statistical tests are needed to detect dFC, we describe how they can be carried out and how to assess the performance of dFC measures, and we illustrate the methodology using spontaneous blood-oxygen level-dependent (BOLD) fMRI recordings of macaque monkeys under general anesthesia and in human subjects under resting-state conditions. We mainly focus on sliding-window correlations since these are most widely used in assessing dFC, but also consider a recently proposed non-linear measure. The simulations and methodology, however, are general and can be applied to any measure. The results are twofold. First, through simulations, we show that in typical resting-state sessions of 10 min, it is almost impossible to detect dFC using sliding-window correlations. This prediction is validated by both the macaque and the human data: in none of the individual recording sessions was evidence for dFC found. Second, detection power can be considerably increased by session- or subject-averaging of the measures. In doing so, we found that most of the functional connections are in fact dynamic. With this study, we hope to raise awareness of the statistical pitfalls in the assessment of dFC and how they can be avoided by using appropriate statistical methods.

Modulation of alpha power at encoding and retrieval tracks the precision of visual short-term memory.

Our ability to hold information in mind is strictly limited. We sought to understand the relationship between oscillatory brain activity and the allocation of resources within visual short-term memory (VSTM). Participants attempted to remember target arrows embedded among distracters and used a continuous method of responding to report their memory for a cued target item. Trial-to-trial variability in the absolute circular accuracy with which participants could report the target was predicted by event-related alpha synchronization during initial processing of the memoranda and by alpha desynchronization during the retrieval of those items from VSTM. Using a model-based approach, we were also able to explore further which parameters of VSTM-guided behavior were most influenced by alpha band changes. Alpha synchronization during item processing enhanced the precision with which an item could be retained without affecting the likelihood of an item being represented per se (as indexed by the guessing rate). Importantly, our data outline a neural mechanism that mirrors the precision with which items are retained; the greater the alpha power enhancement during encoding, the greater the precision with which that item can be retained.

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

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 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.

Age-dependent modulation of motor network connectivity for skill acquisition, consolidation and interlimb transfer after motor practice.

OBJECTIVE: Age-related differences in neural strategies for motor learning are not fully understood. We determined the effects of age on the relationship between motor network connectivity and motor skill acquisition, consolidation, and interlimb transfer using dynamic imaging of coherent sources. METHODS: Healthy younger (n = 24, 18-24 y) and older (n = 24, 65-87 y) adults unilaterally practiced a visuomotor task and resting-state electroencephalographic data was acquired before and after practice as well as at retention. RESULTS: The results showed that right-hand skill acquisition and consolidation did not differ between age groups. However, age affected the ability to transfer the newly acquired motor skill to the non-practiced limb. Moreover, strengthened left- and right-primary motor cortex-related beta connectivity was negatively and positively associated with right-hand skill acquisition and left-hand skill consolidation in older adults, respectively. CONCLUSION: Age-dependent modulations of bilateral resting-state motor network connectivity indicate age-specific strategies for the acquisition, consolidation, and interlimb transfer of novel motor tasks. SIGNIFICANCE: The present results provide insights into the mechanisms underlying motor learning that are important for the development of interventions for patients with unilateral injuries.

Pharmacological functional MRI assessment of the effect of ibuprofen-arginine in painful conditions.

Pharmacological functional magnetic resonance imaging (phMRI) is a valuable tool for the investigation of pharmacological effects of a drug on pain processing. We hypothesized that the ibuprofen-arginine combination, in line with its characteristic analgesic properties, may influence the phMRI response at the central level, as compared to placebo. Ten healthy subjects underwent a double-blind, placebo-controlled, randomized, cross-over phFMRI study with somatosensory painful stimulation of the right median nerve. We measured the blood oxygen level dependent (BOLD) signal variations induced in conditions of pain after oral administration of either ibuprofen-arginine or placebo formulations. Independent component analysis (ICA) was used for the analysis of the fMRI data, without assuming a specific hemodynamic response function (HRF), which may be altered by drug administration. Median nerve electrical painful stimulation mainly activated the primary contralateral and the secondary somatosensory cortices, the insula, the supplementary motor area, and the middle frontal gyrus. Placebo and ibuprofen-arginine administration induced activation bilaterally in the premotor cortex, and an overall reduction in the other pain-related areas, which was more prominent in the left hemisphere. A task-related increase of BOLD signal between drug and placebo was observed bilaterally in the primary somatosensory area and the middle frontal gyrus without any changes in subjective pain scores. Overall, our findings show that ibuprofen-arginine, in line with the characteristic analgesic properties of ibuprofen, influences the BOLD response in specific pain-related brain areas with respect to placebo, with a vasoactive effect possibly due to arginine.

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

Two 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 the quality of combined EEG-TMS neural recordings: Introducing the coil spacer.

BACKGROUND: In the last decade, interest in combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) approaches has grown substantially. Aside from the obvious artifacts induced by the magnetic pulses themselves, separate and more sinister signal disturbances arise as a result of contact between the TMS coil and EEG electrodes. NEW METHOD: Here we profile the characteristics of these artifacts and introduce a simple device - the coil spacer - to provide a platform allowing physical separation between the coil and electrodes during stimulation. RESULTS: EEG data revealed high amplitude signal disturbances when the TMS coil was in direct contact with the EEG electrodes, well within the physiological range of viable EEG signals. The largest artifacts were located in the Delta and Theta frequency range, and standard data cleanup using independent components analysis (ICA) was ineffective due to the artifact's similarity to real brain oscillations. COMPARISON WITH EXISTING METHOD: While the current best practice is to use a large coil holding apparatus to fixate the coil 'hovering' over the head with an air gap, the spacer provides a simpler solution that ensures this distance is kept constant throughout testing. CONCLUSIONS: The results strongly suggest that data collected from combined TMS-EEG studies with the coil in direct contact with the EEG cap are polluted with low frequency artifacts that are indiscernible from physiological brain signals. The coil spacer provides a cheap and simple solution to this problem and is recommended for use in future simultaneous TMS-EEG recordings.

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

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 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.

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

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 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.

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

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 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.

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

OBJECTIVES: 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.