Browsing by Subject "INDEPENDENT COMPONENT ANALYSIS"

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  • Trusbak Haumann, Niels; Hansen, Brian; Huotilainen, Minna; Vuust, Peter; Brattico, Elvira (2020)
    Background The accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) in measuring neural evoked responses (ERs) is challenged by overlapping neural sources. This lack of accuracy is a severe limitation to the application of ERs to clinical diagnostics. New method We here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural assemblies, and a spike density component analysis (SCA) method for isolating specific neural sources. The method is tested in three empirical studies with 564 cases of ERs to auditory stimuli from 94 humans, each measured with 60 EEG electrodes and 306 MEG sensors, and a simulation study with 12,300 ERs. Results The first study showed that neural sources (but not non-encephalic artifacts) in individual averaged MEG/EEG waveforms are modelled accurately with temporal Gaussian probability density functions (median 99.7 %–99.9 % variance explained). The following studies confirmed that SCA can isolate an ER, namely the mismatch negativity (MMN), and that SCA reveals inter-individual variation in MMN amplitude. Finally, SCA reduced errors by suppressing interfering sources in simulated cases. Comparison with existing methods We found that gamma and sine functions fail to adequately describe individual MEG/EEG waveforms. Also, we observed that principal component analysis (PCA) and independent component analysis (ICA) does not consistently suppress interference from overlapping brain activity in neither empirical nor simulated cases. Conclusions These findings suggest that the overlapping neural sources in single-subject or patient data can be more accurately separated by applying SCA in comparison to PCA and ICA.
  • Lindstrom, R.; Lepistö-Paisley, T.; Makkonen, T.; Reinvall, O.; Nieminen-von Wendt, T.; Alen, R.; Kujala, T. (2018)
    Objective: The present study explored the processing of emotional speech prosody in school-aged children with autism spectrum disorders (ASD) but without marked language impairments (children with ASD [no LI]). Methods: The mismatch negativity (MMN)/the late discriminative negativity (LDN), reflecting pre-attentive auditory discrimination processes, and the P3a, indexing involuntary orienting to attention-catching changes, were recorded to natural word stimuli uttered with different emotional connotations (neutral, sad, scornful and commanding). Perceptual prosody discrimination was addressed with a behavioral sound-discrimination test. Results: Overall, children with ASD (no LI) were slower in behaviorally discriminating prosodic features of speech stimuli than typically developed control children. Further, smaller standard-stimulus event related potentials (ERPs) and MMN/LDNs were found in children with ASD (no LI) than in controls. In addition, the amplitude of the P3a was diminished and differentially distributed on the scalp in children with ASD (no LI) than in control children. Conclusions: Processing of words and changes in emotional speech prosody is impaired at various levels of information processing in school-aged children with ASD (no LI). Significance: The results suggest that low-level speech sound discrimination and orienting deficits might contribute to emotional speech prosody processing impairments observed in ASD. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
  • Mutanen, Tuomas P.; Metsomaa, Johanna; Liljander, Sara; Ilmoniemi, Risto J. (2018)
    Electroencephalography (EEG) and magnetoencephalography (MEG) often suffer from noise-and artifact-contaminated channels and trials. Conventionally, EEG and MEG data are inspected visually and cleaned accordingly, e.g., by identifying and rejecting the so-called "bad" channels. This approach has several shortcomings: data inspection is laborious, the rejection criteria are subjective, and the process does not fully utilize all the information in the collected data. Here, we present noise-cleaning methods based on modeling the multi-sensor and multi-trial data. These approaches offer objective, automatic, and robust removal of noise and disturbances by taking into account the sensor-or trial-specific signal-to-noise ratios. We introduce a method called the source-estimate-utilizing noise-discarding algorithm (the SOUND algorithm). SOUND employs anatomical information of the head to cross-validate the data between the sensors. As a result, we are able to identify and suppress noise and artifacts in EEG and MEG. Furthermore, we discuss the theoretical background of SOUND and show that it is a special case of the well-known Wiener estimators. We explain how a completely data-driven Wiener estimator (DDWiener) can be used when no anatomical information is available. DDWiener is easily applicable to any linear multivariate problem; as a demonstrative example, we show how DDWiener can be utilized when estimating event-related EEG/MEG responses. We validated the performance of SOUND with simulations and by applying SOUND to multiple EEG and MEG datasets. SOUND considerably improved the data quality, exceeding the performance of the widely used channel-rejection and interpolation scheme. SOUND also helped in localizing the underlying neural activity by preventing noise from contaminating the source estimates. SOUND can be used to detect and reject noise in functional brain data, enabling improved identification of active brain areas.
  • Petersen, Bjorn; Weed, Ethan; Sandmann, Pascale; Brattico, Elvira; Hansen, Mads; Sorensen, Stine Derdau; Vuust, Peter (2015)
    Cochlear implants (CIs) are primarily designed to assist deaf individuals in perception of speech, although possibilities for music fruition have also been documented. Previous studies have indicated the existence of neural correlates of residual music skills in postlingually deaf adults and children. However, little is known about the behavioral and neural correlates of music perception in the new generation of prelingually deaf adolescents who grew up with CIs. With electroencephalography (EEG), we recorded the mismatch negativity (MMN) of the auditory event-related potential to changes in musical features in adolescent CI users and in normal-hearing (NH) age mates. EEG recordings and behavioral testing were carried out before (T1) and after (T2) a 2-week music training program for the CI users and in two sessions equally separated in time for NH controls. We found significant MMNs in adolescent CI users for deviations in timbre, intensity, and rhythm, indicating residual neural prerequisites for musical feature processing. By contrast, only one of the two pitch deviants elicited an MMN in CI users. This pitch discrimination deficit was supported by behavioral measures, in which CI users scored significantly below the NH level. Overall, MMN amplitudes were significantly smaller in CI users than in NH controls, suggesting poorer music discrimination ability. Despite compliance from the CI participants, we found no effect of the music training, likely resulting from the brevity of the program. This is the first study showing significant brain responses to musical feature changes in prelingually deaf adolescent CI users and their associations with behavioral measures, implying neural predispositions for at least some aspects of music processing. Future studies should test any beneficial effects of a longer lasting music intervention in adolescent CI users.
  • Haumann, Niels Trusbak; Parkkonen, Lauri; Kliuchko, Marina; Vuust, Peter; Brattico, Elvira (2016)
    We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal-slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the wave form when the signal-to-noise ratio (SNR) in the original data is relatively low-in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.
  • Klovatch-Podlipsky, Ilana; Gazit, Tomer; Fahoum, Firas; Tsirelson, Boris; Kipervasser, Svetlana; Kremer, Uri; Ben-Zeev, Bruria; Goldberg-Stern, Hadassah; Eisenstein, Orna; Harpaz, Yuval; Levy, Ory; Kirschner, Adi; Neufeld, Miriam Y.; Fried, Itzhak; Hendler, Talma; Medvedovsky, Mordekhay (2016)
    Objective: Although simultaneous recording of EEG and MRI has gained increasing popularity in recent years, the extent of its clinical use remains limited by various technical challenges. Motion interference is one of the major challenges in EEG-fMRI. Here we present an approach which reduces its impact with the aid of an MR compatible dual-array EEG (daEEG) in which the EEG itself is used both as a brain signal recorder and a motion sensor. Methods: We implemented two arrays of EEG electrodes organized into two sets of nearly orthogonally intersecting wire bundles. The EEG was recorded using referential amplifiers inside a 3 T MR-scanner. Virtual bipolar measurements were taken both along bundles (creating a small wire loop and therefore minimizing artifact) and across bundles (creating a large wire loop and therefore maximizing artifact). Independent component analysis (ICA) was applied. The resulting ICA components were classified into brain signal and noise using three criteria: 1) degree of two-dimensional spatial correlation between ICA coefficients along bundles and across bundles; 2) amplitude along bundles vs. across bundles; 3) correlation with ECG. The components which passed the criteria set were transformed back to the channel space. Motion artifact suppression and the ability to detect interictal epileptic spikes following daEEG and Optimal Basis Set (OBS) procedures were compared in 10 patients with epilepsy. Results: The SNR achieved by daEEG was 11.05 +/- 3.10 and by OBS was 8.25 +/- 1.01 (p <0.00001). In 9 of 10 patients, more spikes were detected after daEEG than after OBS (p <0.05). Significance: daEEG improves signal quality in EEG-fMRI recordings, expanding its clinical and research potential. (C) 2016 Elsevier Inc. All rights reserved.
  • Jiang, Ping; Vuontela, Virve; Tokariev, Maksym; Lin, Hai; Aronen, Eeva T.; Ma, YuanYe; Carlson, Synnöve (2018)
    Earlier studies on adults have shown that functional connectivity (FC) of brain networks can vary depending on the brain state and cognitive challenge. Network connectivity has been investigated quite extensively in children in resting state, much less during tasks and is largely unexplored between these brain states. Here we used functional magnetic resonance imaging and independent component analysis to investigate the functional architecture of large-scale brain networks in 16 children (aged 7-11 years, 11 males) and 16 young adults (aged 22-29 years, 10 males) during resting state and visual working memory tasks. We identified the major neurocognitive intrinsic connectivity networks (ICNs) in both groups. Children had stronger FC than adults within the cingulo-opercular network in resting state, during task performance, and after controlling for performance differences. During tasks, children had stronger FC than adults also within the default mode (DMN) and right frontoparietal (rFPN) networks, and between the anterior DMN and the frontopolar network, whereas adults had stronger coupling between the anterior DMN and rFPN. Furthermore, children compared to adults modulated the FC strength regarding the rFPN differently between the brain states. The FC within the anterior DMN correlated with age and performance in children so that the younger they were, the stronger was the FC, and the stronger the FC within this network, the slower they performed the tasks. The group differences in the network connectivity reported here, and the observed correlations with task performance, provide insight into the normative development of the preadolescent brain and link maturation of functional connectivity with improving cognitive performance.
  • Monti, Ricardo Pio; Gibberd, Alex; Roy, Sandipan; Nunes, Matthew; Lorenz, Romy; Leech, Robert; Ogawa, Takeshi; Kawanabe, Motoaki; Hyvärinen, Aapo (2020)
    Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we seek to build models of brain age based on functional connectivity while prioritizing model interpretability and understanding. This way, the models serve to both provide accurate estimates of brain age as well as allow us to investigate changes in functional connectivity which occur during the ageing process. The methods proposed in this work consist of a two-step procedure: first, linear latent variable models, such as PCA and its extensions, are employed to learn reproducible functional connectivity networks present across a cohort of subjects. The activity within each network is subsequently employed as a feature in a linear regression model to predict brain age. The proposed framework is employed on the data from the CamCAN repository and the inferred brain age models are further demonstrated to generalize using data from two open-access repositories: the Human Connectome Project and the ATR Wide-Age-Range.
  • Boldt, Robert; Malinen, Sanna; Seppa, Mika; Tikka, Pia; Savolainen, Petri Ilmari; Hari, Riitta; Carlson, Synnöve (2013)
  • Rossini, P. M.; Di Iorio, R.; Bentivoglio, M.; Bertini, G.; Ferreri, F.; Gerloff, C.; Ilmoniemi, R. J.; Miraglia, F.; Nitsche, M. A.; Pestilli, F.; Rosanova, M.; Shirota, Y.; Tesoriero, C.; Ugawa, Y.; Vecchio, F.; Ziemann, U.; Hallett, M. (2019)
    The goal of this paper is to examine existing methods to study the "Human Brain Connectome" with a specific focus on the neurophysiological ones. In recent years, a new approach has been developed to evaluate the anatomical and functional organization of the human brain: the aim of this promising multimodality effort is to identify and classify neuronal networks with a number of neurobiologically meaningful and easily computable measures to create its connectome. By defining anatomical and functional connections of brain regions on the same map through an integrated approach, comprising both modern neurophysiological and neuroimaging (i.e. flow/metabolic) brain-mapping techniques, network analysis becomes a powerful tool for exploring structural-functional connectivity mechanisms and for revealing etiological relationships that link connectivity abnormalities to neuropsychiatric disorders. Following a recent IFCN-endorsed meeting, a panel of international experts was selected to produce this current state-of-art document, which covers the available knowledge on anatomical and functional connectivity, including the most commonly used structural and functional MRI, EEG, MEG and non-invasive brain stimulation techniques and measures of local and global brain connectivity. (C) 2019 Published by Elsevier B.V. on behalf of International Federation of Clinical Neurophysiology.
  • Morioka, Hiroshi; Calhoun, Vince; Hyvarinen, Aapo (2020)
    Accumulating evidence from whole brain functional magnetic resonance imaging (fMRI) suggests that the human brain at rest is functionally organized in a spatially and temporally constrained manner. However, because of their complexity, the fundamental mechanisms underlying time-varying functional networks are still not well under-stood. Here, we develop a novel nonlinear feature extraction framework called local space-contrastive learning (LSCL), which extracts distinctive nonlinear temporal structure hidden in time series, by training a deep temporal convolutional neural network in an unsupervised, data-driven manner. We demonstrate that LSCL identifies certain distinctive local temporal structures, referred to as temporal primitives, which repeatedly appear at different time points and spatial locations, reflecting dynamic resting-state networks. We also show that these temporal primitives are also present in task-evoked spatiotemporal responses. We further show that the temporal primitives capture unique aspects of behavioral traits such as fluid intelligence and working memory. These re-sults highlight the importance of capturing transient spatiotemporal dynamics within fMRI data and suggest that such temporal primitives may capture fundamental information underlying both spontaneous and task-induced fMRI dynamics.
  • Mutanen, Tuomas P.; Kukkonen, Matleena; Nieminen, Jaakko O.; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J. (2016)
    Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) often suffers from large muscle artifacts. Muscle artifacts can be removed using signal-space projection (SSP), but this canmake the visual interpretation of the remaining EEG data difficult. We suggest to use an additional step after SSP that we call source-informed reconstruction (SIR). SSP-SIR improves substantially the signal quality of artifactual TMS-EEG data, causing minimal distortion in the neuronal signal components. In the SSP-SIR approach, we first project out the muscle artifact using SSP. Utilizing an anatomical model and the remaining signal, we estimate an equivalent source distribution in the brain. Finally, we map the obtained source estimate onto the original signal space, again using anatomical information. This approach restores the neuronal signals in the sensor space and interpolates EEG traces onto the completely rejected channels. The introduced algorithm efficiently suppresses TMS-related muscle artifacts in EEG while retaining well the neuronal EEG topographies and signals. With the presented method, we can remove muscle artifacts from TMS-EEG data and recover the underlying brain responses without compromising the readability of the signals of interest. (C) 2016 Elsevier Inc. All rights reserved.
  • Ilves, Nigul; Ilves, Pilvi; Laugesaar, Rael; Juurmaa, Julius; Mannamaa, Mairi; Loo, Silva; Loorits, Dagmar; Tomberg, Tiiu; Kolk, Anneli; Talvik, Inga; Talvik, Tiina (2016)
    Perinatal stroke is a leading cause of congenital hemiparesis and neurocognitive deficits in children. Dysfunctions in the largescale resting-state functional networks may underlie cognitive and behavioral disability in these children. We studied resting-state functional connectivity in patients with perinatal stroke collected from the Estonian Pediatric Stroke Database. Neurodevelopment of children was assessed by the Pediatric Stroke Outcome Measurement and the Kaufman Assessment Battery. The study included 36 children (age range 7.6-17.9 years): 10 with periventricular venous infarction (PVI), 7 with arterial ischemic stroke (AIS), and 19 controls. There were no differences in severity of hemiparesis between the PVI and AIS groups. A significant increase in default mode network connectivity (FDR 0.1) and lower cognitive functions (p <0.05) were found in children with AIS compared to the controls and the PVI group. The children with PVI had no significant differences in the resting-state networks compared to the controls and their cognitive functions were normal. Our findings demonstrate impairment in cognitive functions and neural network profile in hemiparetic children with AIS compared to children with PVI and controls. Changes in the resting-state networks found in children with AIS could possibly serve as the underlying derangements of cognitive brain functions in these children.
  • Gutmann, Michael U.; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesus (2014)
  • Salmela, Viljami; Salo, Emma; Salmi, Juha; Alho, Kimmo (2018)
    The fronto-parietal attention networks have been extensively studied with functional magnetic resonance imaging (fMRI), but spatiotemporal dynamics of these networks are not well understood. We measured event-related potentials (ERPs) with electroencephalography (EEG) and collected fMRI data from identical experiments where participants performed visual and auditory discrimination tasks separately or simultaneously and with or without distractors. To overcome the low temporal resolution of fMRI, we used a novel ERP-based application of multivariate representational similarity analysis (RSA) to parse time-averaged fMRI pattern activity into distinct spatial maps that each corresponded, in representational structure, to a short temporal ERP segment. Discriminant analysis of ERP-fMRI correlations revealed 8 cortical networks-2 sensory, 3 attention, and 3 other-segregated by 4 orthogonal, temporally multifaceted and spatially distributed functions. We interpret these functions as 4 spatiotemporal components of attention: modality-dependent and stimulus-driven orienting, top-down control, mode transition, and response preparation, selection and execution.
  • Cowley, Benjamin Ultan (2018)
    Sustained attention plays an important role in everyday life, for work, learning, or when affected by attention disorders. Studies of the neural correlates of attention commonly treat sustained attention as an isolated construct, measured with computerized continuous performance tests. However, in any ecological context, sustained attention interacts with other executive functions and depends on lower level perceptual processing. Such interactions occur, for example, in inhibition of interference, and processing of complex hierarchical stimuli; both of which are important for successful ecological attention. Motivated by the need for more studies on neural correlates of higher cognition, I present an experiment to investigate these interactions of attention in 17 healthy participants measured with high-resolution electroencephalography. Participants perform a novel 2-alternative forced-choice computerised performance test, the Primed Subjective Illusory Contour Attention Task (PSICAT), which presents gestalt-stimuli targets with distractor primes to induce interference inhibition during complex-percept processing. Using behavioural and brain-imaging analyses, I demonstrate the novel result that task-irrelevant incongruency can evoke stronger behavioural and neural responses than the task-relevant stimulus condition; a potentially important finding in attention disorder research. PSICAT is available as an open-source code repository at the following url, allowing researchers to reuse and adapt it to their requirements.
  • Näätänen, Risto; Petersen, Bjorn; Torppa, Ritva; Lonka, Eila; Vuust, Peter (2017)
    In the present article, we review the studies on the use of the mismatch negativity (MMN) as a tool for an objective assessment of cochlear-implant (CI) functioning after its implantation and as a function of time of CI use. The MMN indexes discrimination of different sound stimuli with a precision matching with that of behavioral discrimination and can therefore be used as its objective index. Importantly, these measurements can be reliably carried out even in the absence of attention and behavioral responses and therefore they can be extended to populations that are not capable of behaviorally reporting their perception such as infants and different clinical patient groups. In infants and small children with CI, the MMN provides the only means for assessing the adequacy of the CI functioning, its improvement as a function of time of CI use, and the efficiency of different rehabilitation procedures. Therefore, the MMN can also be used as a tool in developing and testing different novel rehabilitation procedures. Importantly, the recently developed multi-feature MMN paradigms permit the objective assessment of discrimination accuracy for all the different auditory dimensions (such as frequency, intensity, and duration) in a short recording time of about 30 min. Most recently, such stimulus paradigms have been successfully developed for an objective assessment of music perception, too. (C) 2017 Elsevier B.V. All rights reserved.
  • Kauppi, Jukka-Pekka; Hahne, Janne; Müller, Klaus-Robert; Müller, Klaus-Robert (2015)
    Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results.