scholarly journals Assessing the sensitivity of EEG-based frequency-tagging as a metric for statistical learning

2021 ◽  
pp. 1-46
Author(s):  
Danna Pinto ◽  
Anat Prior ◽  
Elana Zion Golumbic
Keyword(s):  
Statistical Learning ◽  
Neural Activity ◽  
Alternative Measure ◽  
Artificial Language ◽  
Behavioral Tests ◽  
Group Level ◽  
Behavioral Measures ◽  
Non Invasive ◽  
Eeg Recordings ◽  
Low Sensitivity

Abstract Statistical Learning (SL) is hypothesized to play an important role in language development. However, the measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative measure for studying SL. We tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive EEG recordings of neural activity in humans. Importantly, we use carefully constructed controls to address potential acoustic confounds of the frequency-tagging approach, and compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral tests of SL. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Comparison of the neural metric to previously established behavioral measures for assessing SL showed a significant yet weak correspondence with performance on an implicit task, which was above-chance in 70% of participants, but no correspondence with the more common explicit 2AFC task, where performance did not exceed chance-level. Given the proposed ubiquitous nature of SL, our results highlight some of the operational and methodological challenges of obtaining robust metrics for assessing SL, as well as the potential confounds that should be taken into account when using the frequency-tagging approach in EEG studies.

scholarly journals Assessing the sensitivity of EEG-based frequency-tagging as a metric for statistical learning

2021 ◽  
Author(s):  
Danna Pinto ◽  
Anat Prior ◽  
Elana Zion Golumbic
Keyword(s):  
Statistical Learning ◽  
Artificial Language ◽  
Group Level ◽  
Behavioral Measures ◽  
Individual Level ◽  
Non Invasive ◽  
Eeg Data ◽  
Eeg Recordings ◽  
The Individual ◽  
Low Sensitivity

Statistical Learning (SL) is hypothesized to play an important role in language development. However, the behavioral measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and often have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative and more direct measure for studying SL. Here we tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive EEG recordings of neural activity in humans. Importantly, we use carefully constructed controls, in order to address potential acoustic confounds of the frequency-tagging approach. We compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral tests of SL, and the correspondence between these presumed converging operations. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Conversely, the implicit behavior measures indicated that SL has occurred in 70% of participants, which is more consistent with the proposed ubiquitous nature of SL. Moreover, there was low correspondence between the different measures used to assess SL. Taken together, while some researchers may find the frequency-tagging approach suitable for their needs, our results highlight the methodological challenges of assessing SL at the individual level, and the potential confounds that should be taken into account when interpreting frequency-tagged EEG data.

scholarly journals Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

2021 ◽  
Author(s):  
Leonhard Waschke ◽  
Thomas Donoghue ◽  
Lorenz Fiedler ◽  
Sydney Smith ◽  
Douglas D. Garrett ◽  
...  
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Spectral Exponent ◽  
Non Invasive ◽  
Neural Processes ◽  
Highly Sensitive ◽  
Neural Populations ◽  
Sensory Cortices ◽  
Behavioural Performance ◽  
Eeg Recordings

AbstractA hallmark of electrophysiological brain activity is its 1/f-like spectrum – power decreases with increasing frequency. The steepness of this “roll-off” is approximated by the spectral exponent, which in invasively recorded neural populations reflects the balance of excitatory to inhibitory neural activity (E:I balance). Here, we first demonstrate that the spectral exponent of non-invasive electroencephalography (EEG) recordings is highly sensitive to general, anaesthesia-driven as well as specific, attention-driven changes in E:I balance. We then present results from an EEG experiment during which participants detected faint target stimuli in streams of simultaneously presented auditory and visual noise. EEG spectral exponents over auditory and visual sensory cortices tracked stimulus spectral exponents of the corresponding domain, while evoked responses remained unchanged. Crucially, the degree of this stimulus–brain spectral-exponent coupling was positively linked to behavioural performance. Our results highlight the relevance of neural 1/f-like activity and enable the study of neural processes previously thought to be inaccessible in non-invasive human recordings.

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

scholarly journals Dynamic relationships between spontaneous and evoked electrophysiological activity

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Soren Wainio-Theberge ◽  
Annemarie Wolff ◽  
Georg Northoff
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
Behavioral Outcomes ◽  
Low Frequencies ◽  
Scale Free ◽  
Non Invasive ◽  
Spontaneous Neural Activity ◽  
Electrophysiological Signals ◽  
Evoked Activity ◽  
Dynamic Relationships

AbstractSpontaneous neural activity fluctuations have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioral outcomes. However, the complex electrophysiological mechanisms by which these fluctuations shape stimulus-evoked neural activity remain largely to be explored. Employing a large-scale magnetoencephalographic dataset and an electroencephalographic replication dataset, we investigate the relationship between spontaneous and evoked neural activity across a range of electrophysiological variables. We observe that for high-frequency activity, high pre-stimulus amplitudes lead to greater evoked desynchronization, while for low frequencies, high pre-stimulus amplitudes induce larger degrees of event-related synchronization. We further decompose electrophysiological power into oscillatory and scale-free components, demonstrating different patterns of spontaneous-evoked correlation for each component. Finally, we find correlations between spontaneous and evoked time-domain electrophysiological signals. Overall, we demonstrate that the dynamics of multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, a result which carries implications for experimental design and analysis in non-invasive electrophysiology.

scholarly journals A Neuromorphic Spiking Neural Network Detects Epileptic High Frequency Oscillations in the Scalp EEG

2021 ◽  
Author(s):  
Karla Burelo ◽  
Georgia Ramantani ◽  
Giacomo Indiveri ◽  
Johannes Sarnthein
Keyword(s):  
Low Power ◽  
Seizure Frequency ◽  
High Frequency ◽  
Therapy Monitoring ◽  
Non Invasive ◽  
Scalp Eeg ◽  
High Frequency Oscillations ◽  
Eeg Recordings ◽  
Active Epilepsy ◽  
Wearable Electronic Devices

Abstract Background: Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long- term EEG recording. Spiking neural networks (SNN) have been shown to be optimal architectures for being embedded in compact low-power signal processing hardware. Methods: We analyzed 20 scalp EEG recordings from 11 patients with pediatric focal lesional epilepsy. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. Results: We identified the optimal SNN parameters to automatically detect EoI and reject artifacts. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.83, p < 0.0001, Spearman’s correlation).Conclusions: The fully automated SNN detected clinically relevant HFO in the scalp EEG. This is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.

scholarly journals The pattern of urine cytology among patients with clinical diagnosis of bladder tumor in a tertiary hospital northwest Nigeria

2019 ◽  
Vol 6 (10) ◽  
pp. 3521
Author(s):  
Ahmed M. Umar ◽  
Uzodimma E. Onwuasoanya ◽  
Emmanuel U. Oyibo ◽  
Adamu Dahiru ◽  
Ismaila A. Mungadi
Keyword(s):  
Bladder Cancer ◽  
Clinical Diagnosis ◽  
Bladder Tumour ◽  
Tertiary Hospital ◽  
Urine Cytology ◽  
Female Ratio ◽  
Non Invasive ◽  
And Gender ◽  
Low Sensitivity ◽  
Age Range

Background: Urine cytology is a simple, safe, non-invasive and cheap investigation that is used as adjunct to cystoscopy in the diagnosis of bladder cancer. Its low sensitivity is a major limitation against its use as a sole diagnostic test for bladder cancer. The objective of this study was to determine the pattern of urine cytology seen in patients with clinical diagnosis of bladder tumour in our practice.Methods: This is a retrospective study of patients with clinical diagnosis of bladder tumour that had urine cytology in our centre. The age and gender of the patients, number of urine cytology per patient per year and cytologic diagnosis were analysed using the SPSS 20.Results: During the period under review, a total of 512 urine cytology was done for patients with clinical diagnosis of bladder tumour. The age range of the patients was 6 to 90 years with modal age of 60 years. 457 (89.3%) were males while 54 (10.5%) were females and 1 (0.2%) was unspecified. Male to female ratio was 8.5:1. The highest number of urine cytology was done in 2013 with 64 (12.5%) while the least number was 1 (0.2%) recorded in 2001 and 2003. Only 68 (13.3%) specimens were reported to be malignant while 245 (47.9%) were reported as negative representing the most common cytological diagnosis in the study.Conclusions: Although urine cytology is useful in the diagnostic workup of patients with bladder mass, it is unlikely it would supplant cystoscopy and biopsy in the diagnosis of bladder cancer. 

MEG/EEG in the Study of Brain Function

2017 ◽  
pp. 304-310
Author(s):  
Riitta Hari ◽  
Aina Puce
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
Social Neuroscience ◽  
Future Research ◽  
Noninvasive Methods ◽  
Advantages And Disadvantages ◽  
Non Invasive ◽  
Combined Use ◽  
Eeg Recordings ◽  
Two Sides

This chapter summarizes some relative advantages and disadvantages of MEG and EEG, most of which have been previously elaborated. MEG and EEG are the two sides of the same coin and provide complementary information about the human brain’s neurodynamics. The combined use of MEG or EEG together and with other noninvasive methods used to study human brain function is advocated to be important for future research in systems and cognitive/social neuroscience. This chapter also examines combined use and interpretation of MEG/EEG with MRI/fMRI, and performing EEG recordings during non-invasive brain stimulation.

scholarly journals Neural Connectivity Subtypes Predict Discrete Attentional-Bias Profiles Among Heterogeneous Anxiety Patients

2020 ◽  
Vol 8 (3) ◽  
pp. 491-505 ◽  
Author(s):  
Rebecca B. Price ◽  
Adriene M. Beltz ◽  
Mary L. Woody ◽  
Logan Cummings ◽  
Danielle Gilchrist ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Selective Attention ◽  
Attentional Bias ◽  
Sensory Processing ◽  
Brain Regions ◽  
Data Driven ◽  
Neural Connectivity ◽  
Group Level ◽  
Behavioral Measures ◽  
Substantial Heterogeneity

On average, anxious patients show altered attention to threat—including early vigilance toward threat and later avoidance of threat—accompanied by altered functional connectivity across brain regions. However, substantial heterogeneity within clinical, neural, and attentional features of anxiety is overlooked in typical group-level comparisons. We used a well-validated method for data-driven parsing of neural connectivity to reveal connectivity-based subgroups among 60 adults with transdiagnostic anxiety. Subgroups were externally compared on attentional patterns derived from independent behavioral measures. Two subgroups emerged. Subgroup A (68% of patients) showed stronger executive network influences on sensory processing regions and a paradigmatic “vigilance–avoidance” pattern on external behavioral measures. Subgroup B was defined by a larger number of limbic influences on sensory regions and exhibited a more atypical and inconsistent attentional profile. Neural connectivity-based categorization revealed an atypical, limbic-driven pattern of connectivity in a subset of anxious patients that generalized to atypical patterns of selective attention.

The Diagnosis of Established Deep Vein Thrombosis

1979 ◽  
Author(s):  
R. Hull ◽  
J. Hirsh
Keyword(s):  
Deep Vein Thrombosis ◽  
Doppler Ultrasonography ◽  
Clinical Symptoms ◽  
Reference Method ◽  
Impedance Plethysmography ◽  
Vein Thrombosis ◽  
Non Invasive ◽  
Patient Morbidity ◽  
Low Sensitivity ◽  
Deep Vein

It is now generally accepted that the clinical diagnosis of deep venous thrombosis (DVT) is inaccurate both because of low sensitivity and specificity. Because more than 50% of symptomatic patients fail to show thrombi on venography, anticoagulant therapy on the basis of clinical symptoms of DVT is not acceptable. Venography has been the standard reference method for the diagnosis of DVT but is invasive and consequently associated with patient morbidity. Impedance plethysmography (IPG) and Doppler ultrasonography (Doppler) are both non-invasive and, in patients with clinically suspected DVT, are sensitive and specific tests for proximal DVT. Both tests are relatively insensitive to calf DVT. IPG has the advantage of being an objective technique whereas Doppler is subjective and its accuracy may suffer in inexperienced hands. 125I fibrinogen leg scanning (leg scanning) is an inappropriate test when used alone in patients with clinically suspected DVT as it is insensitive in the upper thigh, may be negative in 30% of patients with established DVT and may take up to 72 hours to become positive. The combination, however, of IPG and leg scanning provides an accurate approach for the detection of both proximal and calf DVT in patients with established DVT. This approach is not associated with patient morbidity and offers the clinician an alternative to venography.

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