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Sam M. Doesburg

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12 papers

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JBHI Journal 2023 Journal Article

Byte-Pair Encoding for classifying routine clinical electroencephalograms in adults over the lifespan

Mykola Klymenko
Sam M. Doesburg
George Medvedev
Pengcheng Xi
Urs Ribary
Vasily A. Vakorin

Routine clinical EEG is a standard test used for the neurological evaluation of patients. A trained specialist interprets EEG recordings and classifies them into clinical categories. Given time demands and high inter-reader variability, there is an opportunity to facilitate the evaluation process by providing decision support tools that can classify EEG recordings automatically. Classifying clinical EEG is associated with several challenges: classification models are expected to be interpretable; EEGs vary in duration and EEGs are recorded by multiple technicians operating various devices. Our study aimed to test and validate a framework for EEG classification which satisfies these requirements by transforming EEG into unstructured text. We considered a highly heterogeneous and extensive sample of routine clinical EEGs (n = 5785), with a wide range of participants aged between 15 and 99 years. EEG scans were recorded at a public hospital, according to 10/20 electrode positioning with 20 electrodes. The proposed framework was based on symbolizing EEG signals and adapting a previously proposed method from natural language processing (NLP) to break symbols into words. Specifically, we symbolized the multichannel EEG time series and applied a byte-pair encoding (BPE) algorithm to extract a dictionary of the most frequent patterns (tokens) reflecting the variability of EEG waveforms. To demonstrate the performance of our framework, we used newly-reconstructed EEG features to predict patients' biological age with a Random Forest regression model. This age prediction model achieved a mean absolute error of 15. 7 years. We also correlated tokens' occurrence frequencies with age. The highest correlations between the frequencies of tokens and age were observed at frontal and occipital EEG channels. Our findings demonstrated the feasibility of applying an NLP-based approach to classifying routine clinical EEG. Notably, the proposed algorithm could be instrumental in classifying clinical EEG with minimal preprocessing and identifying clinically-relevant short events, such as epileptic spikes.

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YNICL Journal 2021 Journal Article

Classification of evoked responses to inverted faces reveals both spatial and temporal cortical response abnormalities in Autism spectrum disorder

Adonay S. Nunes
Fahimeh Mamashli
Nataliia Kozhemiako
Sheraz Khan
Nicole M. McGuiggan
Ainsley Losh
Robert M. Joseph
Jyrki Ahveninen

The neurophysiology of face processing has been studied extensively in the context of social impairments associated with autism spectrum disorder (ASD), but the existing studies have concentrated mainly on univariate analyses of responses to upright faces, and, less frequently, inverted faces. The small number of existing studies on neurophysiological responses to inverted face in ASD have used univariate approaches, with divergent results. Here, we used a data-driven, classification-based, multivariate machine learning decoding approach to investigate the temporal and spatial properties of the neurophysiological evoked response for upright and inverted faces, relative to the neurophysiological evoked response for houses, a neutral stimulus. 21 (2 females) ASD and 29 (4 females) TD participants ages 7 to 19 took part in this study. Group level classification accuracies were obtained for each condition, using first the temporal domain of the evoked responses, and then the spatial distribution of the evoked responses on the cortical surface, each separately. We found that classification of responses to inverted neutral faces vs. houses was less accurate in ASD compared to TD, in both the temporal and spatial domains. In contrast, there were no group differences in the classification of evoked responses to upright neutral faces relative to houses. Using the classification in the temporal domain, lower decoding accuracies in ASD were found around 120 ms and 170 ms, corresponding the known components of the evoked responses to faces. Using the classification in the spatial domain, lower decoding accuracies in ASD were found in the right superior marginal gyrus (SMG), intra-parietal sulcus (IPS) and posterior superior temporal sulcus (pSTS), but not in core face processing areas. Importantly, individual classification accuracies from both the temporal and spatial classifiers correlated with ASD severity, confirming the relevance of the results to the ASD phenotype.

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YNICL Journal 2020 Journal Article

Atypical neuromagnetic resting activity associated with thalamic volume and cognitive outcome in very preterm children

Adonay S. Nunes
Nataliia Kozhemiako
Evan Hutcheon
Cecil Chau
Urs Ribary
Ruth E. Grunau
Sam M. Doesburg

Children born very preterm, even in the absence of overt brain injury or major impairment, are at increased risk of cognitive difficulties. This risk is associated with developmental disruptions of the thalamocortical system during critical periods while in the neonatal intensive care unit. The thalamus is an important structure that not only relays sensory information but acts as a hub for integration of cortical activity which regulates cortical power across a range of frequencies. In this study, we investigate the association between atypical power at rest in children born very preterm at school age using magnetoencephalography (MEG), neurocognitive function and structural alterations related to the thalamus using MRI. Our results indicate that children born extremely preterm have higher power at slow frequencies (delta and theta) and lower power at faster frequencies (alpha and beta), compared to controls born full-term. A similar pattern of spectral power was found to be associated with poorer neurocognitive outcomes, as well as with normalized T1 intensity and the volume of the thalamus. Overall, this study provides evidence regarding relations between structural alterations related to very preterm birth, atypical oscillatory power at rest and neurocognitive difficulties at school-age children born very preterm.

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YNIMG Journal 2020 Journal Article

Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses

Adonay S. Nunes
Alexander Moiseev
Nataliia Kozhemiako
Teresa Cheung
Urs Ribary
Sam M. Doesburg

Functional brain connectivity is increasingly being seen as critical for cognition, perception and motor control. Magnetoencephalography and electroencephalography are modalities that offer noninvasive mapping of electrophysiological interactions among brain regions, yet suffer from signal leakage and signal cancellation when estimating brain activity. This leads to biased connectivity values which complicate interpretation. In this study, we test the hypothesis that a Multiple Constrained Minimum Variance beamformer (MCMV) outperforms the more traditional Linearly Constrained Minimum Variance beamformer (LCMV) for estimation of electrophysiological connectivity. To this end, MCMV and LCMV performance is compared in task related analyses with both simulated data and human MEG recordings of visual steady state signals, and in resting state analyses with simulated data and human MEG data of 89 subjects. In task related scenarios connectivity was estimated using coherence and phase locking values, whereas envelope correlations were used for the resting state data. We also introduce a novel Augmented Pairwise MCMV (APW-MCMV) approach for signal leakage suppression in resting state analyses and assess its performance against LCMV and more conventional MCMV approaches. We demonstrate that with MCMV effects of signal mixing and coherent source cancellation are greatly reduced in both task related and resting state conditions, while in contrast to other approaches 0- and short time lag interactions are preserved. In addition, we demonstrate that in resting state analyses, APW-MCMV strongly reduces spurious connections while better controlling for false negatives compared to more conservative measures such as symmetrical orthogonalization.

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YNIMG Journal 2020 Journal Article

Neuromagnetic activation and oscillatory dynamics of stimulus-locked processing during naturalistic viewing

Adonay S. Nunes
Nataliia Kozhemiako
Alexander Moiseev
Robert A. Seymour
Teresa P.L. Cheung
Urs Ribary
Sam M. Doesburg

Naturalistic stimuli such as watching a movie while in the scanner provide an ecologically valid paradigm that has the potential of extracting valuable information on how the brain processes complex stimuli in realistic visual and auditory contexts. Naturalistic viewing is also easier to conduct with challenging participant groups including patients and children. Given the high temporal resolution of MEG, in the present study, we demonstrate how a short movie clip can be used to map distinguishable activation and connectivity dynamics underlying the processing of specific classes of visual stimuli such as face and hand manipulations, as well as contrasting activation dynamics for auditory words and non-words. MEG data were collected from 22 healthy volunteers (6 females, 3 left handed, mean age – 27. 7 ± 5. 28 years) during the presentation of naturalistic audiovisual stimuli. The MEG data were split into trials with the onset of the stimuli belonging to classes of interest (words, non-words, faces, hand manipulations). Based on the components of the averaged sensor ERFs time-locked to the visual and auditory stimulus onset, four and three time-windows, respectively, were defined to explore brain activation dynamics. Pseudo-Z, defined as the ratio of the source-projected time-locked power to the projected noise power for each vertex, was computed and used as a proxy of time-locked brain activation. Statistical testing using the mean-centered Partial Least Squares analysis indicated periods where a given visual or auditory stimuli had higher activation. Based on peak pseudo-Z differences between the visual conditions, time-frequency resolved analyses were performed to assess beta band desynchronization in motor-related areas, and inter-trial phase synchronization between face processing areas. Our results provide the first evidence that activation and connectivity dynamics in canonical brain regions associated with the processing of particular classes of visual and auditory stimuli can be reliably mapped using MEG during presentation of naturalistic stimuli. Given the strength of MEG for brain mapping in temporal and frequency domains, the use of naturalistic stimuli may open new techniques in analyzing brain dynamics during ecologically valid sensation and perception.

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YNIMG Journal 2019 Journal Article

Idiosyncratic organization of cortical networks in autism spectrum disorder

Adonay S. Nunes
Nicholas Peatfield
Vasily Vakorin
Sam M. Doesburg

Neuroimaging studies of Autism Spectrum Disorder (ASD) have yielded inconsistent results indicating either increases or decreases in functional connectivity, or both. Recent findings suggest that these seemingly divergent results might be underpinned by greater inter-individual variability in brain network connectivity in ASD. We tested the hypothesis that the spatial patterns of intrinsic connectivity networks (ICNs) are more idiosyncratic in ASD, and demonstrated that this increased variability is associated with symptomatology. We estimated whole brain functional connectivity based on resting state functional magnetic resonance imaging (fMRI) data obtained from the Autism Brain Imaging Data Exchange I & II (ABIDE I & II) repository: 422 (69 females) participants with ASD and 424 (59 females) typically developing (TD) participants between 6 and 30 years of age. We clustered individuals’ patterns of resting state functional connectivity into seven networks, each representing an ICN, and assessed the heterogeneity of each vertex on the cortical surface across individuals in terms of its incorporation into a particular ICN. We found that the incorporation of individual anatomical locations (vertices) to a common network was less consistent across individuals in ASD, indicating a more idiosyncratic organization of ICNs in the ASD brain. This spatial shifting effect was particularly pronounced in the Sensory-Motor Network (SMN) and the Default Mode Network (DMN). We also found that this idiosyncrasy in large-scale brain network organization was correlated with ASD symptomatology (ADOS). These results support the view that idiosyncratic functional connectivity is a hallmark of the ASD brain. We provide the first evidence that the anatomical organization of ICNs is idiosyncratic in ASD, as well as providing evidence that such abnormalities in brain network organization may contribute to the symptoms of ASD.

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YNICL Journal 2016 Journal Article

Bedside functional brain imaging in critically-ill children using high-density EEG source modeling and multi-modal sensory stimulation

Danny Eytan
Elizabeth W. Pang
Sam M. Doesburg
Vera Nenadovic
Bojan Gavrilovic
Peter Laussen
Anne-Marie Guerguerian

Acute brain injury is a common cause of death and critical illness in children and young adults. Fundamental management focuses on early characterization of the extent of injury and optimizing recovery by preventing secondary damage during the days following the primary injury. Currently, bedside technology for measuring neurological function is mainly limited to using electroencephalography (EEG) for detection of seizures and encephalopathic features, and evoked potentials. We present a proof of concept study in patients with acute brain injury in the intensive care setting, featuring a bedside functional imaging set-up designed to map cortical brain activation patterns by combining high density EEG recordings, multi-modal sensory stimulation (auditory, visual, and somatosensory), and EEG source modeling. Use of source-modeling allows for examination of spatiotemporal activation patterns at the cortical region level as opposed to the traditional scalp potential maps. The application of this system in both healthy and brain-injured participants is demonstrated with modality-specific source-reconstructed cortical activation patterns. By combining stimulation obtained with different modalities, most of the cortical surface can be monitored for changes in functional activation without having to physically transport the subject to an imaging suite. The results in patients in an intensive care setting with anatomically well-defined brain lesions suggest a topographic association between their injuries and activation patterns. Moreover, we report the reproducible application of a protocol examining a higher-level cortical processing with an auditory oddball paradigm involving presentation of the patient's own name. This study reports the first successful application of a bedside functional brain mapping tool in the intensive care setting. This application has the potential to provide clinicians with an additional dimension of information to manage critically-ill children and adults, and potentially patients not suited for magnetic resonance imaging technologies.

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YNIMG Journal 2016 Journal Article

Top-down alpha oscillatory network interactions during visuospatial attention orienting

Sam M. Doesburg
Nicolas Bedo
Lawrence M. Ward

Neuroimaging and lesion studies indicate that visual attention is controlled by a distributed network of brain areas. The covert control of visuospatial attention has also been associated with retinotopic modulation of alpha-band oscillations within early visual cortex, which are thought to underlie inhibition of ignored areas of visual space. The relation between distributed networks mediating attention control and more focal oscillatory mechanisms, however, remains unclear. The present study evaluated the hypothesis that alpha-band, directed, network interactions within the attention control network are systematically modulated by the locus of visuospatial attention. We localized brain areas involved in visuospatial attention orienting using magnetoencephalographic (MEG) imaging and investigated alpha-band Granger-causal interactions among activated regions using narrow-band transfer entropy. The deployment of attention to one side of visual space was indexed by lateralization of alpha power changes between about 400ms and 700ms post-cue onset. The changes in alpha power were associated, in the same time period, with lateralization of anterior-to-posterior information flow in the alpha-band from various brain areas involved in attention control, including the anterior cingulate cortex, left middle and inferior frontal gyri, left superior temporal gyrus, and right insula, and inferior parietal lobule, to early visual areas. We interpreted these results to indicate that distributed network interactions mediated by alpha oscillations exert top-down influences on early visual cortex to modulate inhibition of processing for ignored areas of visual space.

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YNICL Journal 2015 Journal Article

Disconnected neuromagnetic networks in children born very preterm

Annette X. Ye
Michelle AuCoin-Power
Margot J. Taylor
Sam M. Doesburg

Many children born very preterm (≤32 weeks) experience significant cognitive difficulties, but the biological basis of such problems has not yet been determined. Functional MRI studies have implicated altered functional connectivity; however, little is known regarding the spatiotemporal organization of brain networks in this population. We provide the first examination of resting-state neuromagnetic connectivity mapped in brain space in school age children born very preterm. Thirty-four subjects (age range 7-12 years old), consisting of 17 very preterm-born children and 17 full-term born children were included. Very preterm-born children exhibited global decreases in inter-regional synchrony in all analysed frequency ranges, from theta (4-7 Hz) to high gamma (80-150 Hz; p < 0.01, corrected). These reductions were expressed in spatially and frequency specific brain networks (p < 0.0005, corrected). Our results demonstrate that mapping connectivity with high spatiotemporal resolution offers new insights into altered organization of neurophysiological networks which may contribute to the cognitive difficulties in this vulnerable population.

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YNIMG Journal 2015 Journal Article

Minimum variance beamformer weights revisited

Alexander Moiseev
Sam M. Doesburg
Ruth E. Grunau
Urs Ribary

Adaptive minimum variance beamformers are widely used analysis tools in MEG and EEG. When the target brain activity presents in the form of spatially localized responses, the procedure usually involves two steps. First, positions and orientations of the sources of interest are determined. Second, the filter weights are calculated and source time courses reconstructed. This last step is the object of the current study. Despite different approaches utilized at the source localization stage, basic expressions for the weights have the same form, dictated by the minimum variance condition. These classic expressions involve covariance matrix of the measured field, which includes contributions from both the sources of interest and the noise background. We show analytically that the same weights can alternatively be obtained, if the full field covariance is replaced with that of the noise, provided the beamformer points to the true sources precisely. In practice, however, a certain mismatch is always inevitable. We show that such mismatch results in partial suppression of the true sources if the traditional weights are used. To avoid this effect, the “alternative” weights based on properly estimated noise covariance should be applied at the second, source time course reconstruction step. We demonstrate mathematically and using simulated and real data that in many situations the alternative weights provide significantly better time course reconstruction quality than the traditional ones. In particular, they a) improve source-level SNR and yield more accurately reconstructed waveforms; b) provide more accurate estimates of inter-source correlations; and c) reduce the adverse influence of the source correlations on the performance of single-source beamformers, which are used most often. Importantly, the alternative weights come at no additional computational cost, as the structure of the expressions remains the same.

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YNICL Journal 2014 Journal Article

Reduced beta band connectivity during number estimation in autism

Katrin A. Bangel
Magali Batty
Annette X. Ye
Emilie Meaux
Margot J. Taylor
Sam M. Doesburg

Recent evidence suggests that disruption of integrative processes in sensation and perception may play a critical role in cognitive and behavioural atypicalities characteristic of ASD. In line with this, ASD is associated with altered structural and functional brain connectivity and atypical patterns of inter-regional communication which have been proposed to contribute to cognitive difficulties prevalent in this group. The present MEG study used atlas-guided source space analysis of inter-regional phase synchronization in ASD participants, as well as matched typically developing controls, during a dot number estimation task. This task included stimuli with globally integrated forms (animal shapes) as well as randomly-shaped stimuli which lacked a coherent global pattern. Early task-dependent increases in inter-regional phase synchrony in theta, alpha and beta frequency bands were observed. Reduced long-range beta-band phase synchronization was found in participants with ASD at 70-145 ms during presentation of globally coherent dot patterns. This early reduction in task-dependent inter-regional connectivity encompassed numerous areas including occipital, parietal, temporal, and frontal lobe regions. These results provide the first evidence for inter-regional phase synchronization during numerosity estimation, as well as its alteration in ASD, and suggest that problems with communication among brain areas may contribute to difficulties with integrative processes relevant to extraction of meaningful 'Gestalt' features in this population.

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YNIMG Journal 2011 Journal Article

Altered long-range alpha-band synchronization during visual short-term memory retention in children born very preterm

Sam M. Doesburg
Urs Ribary
Anthony T. Herdman
Steven P. Miller
Kenneth J. Poskitt
Alexander Moiseev
Michael F. Whitfield
Anne Synnes

Children born very preterm, even when intelligence is broadly normal, often experience selective difficulties in executive function and visual-spatial processing. Development of structural cortical connectivity is known to be altered in this group, and functional magnetic resonance imaging (fMRI) evidence indicates that very preterm children recruit different patterns of functional connectivity between cortical regions during cognition. Synchronization of neural oscillations across brain areas has been proposed as a mechanism for dynamically assigning functional coupling to support perceptual and cognitive processing, but little is known about what role oscillatory synchronization may play in the altered neurocognitive development of very preterm children. To investigate this, we recorded magnetoencephalographic (MEG) activity while 7–8year old children born very preterm and age-matched full-term controls performed a visual short-term memory task. Very preterm children exhibited reduced long-range synchronization in the alpha-band during visual short-term memory retention, indicating that cortical alpha rhythms may play a critical role in altered patterns functional connectivity expressed by this population during cognitive and perceptual processing. Long-range alpha-band synchronization was also correlated with task performance and visual-perceptual ability within the very preterm group, indicating that altered alpha oscillatory mechanisms mediating transient functional integration between cortical regions may be relevant to selective problems in neurocognitive development in this vulnerable population at school age.

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