Biology subjects

Soria-Frisch, A.

Publications and source records attributed to Soria-Frisch, A..

5 recordsLinked to original sources

bioRxiv2018

Hypoarousal non-stationary ADHD biomarker based on echo-state networks

Attention-Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity and impulsivity. It is one of the most commonly diagnosed neurodevelopmental and psychiatric disorders of childhood and therefore presents a very high prevalence rate. However the high rate of ADHD misdiagnosis makes the discovery of neurophysiological ADHD biomarkers an important clinical challenge. This study proposes a novel non-stationary ADHD biomarker based on Echo State Networks to quantify EEG dynamical changes between low attention/arousal states (resting with eyes closed, or EC) and normal attention/arousal states (resting with eyes open, or EO). Traditionally, EEG biomarkers have revealed an increase in stationary power in the theta band along with a decrease in beta, with these frequencies largely accepted to be altered in the ADHD population. We successfully verify the hypothesis that measured differences between these two conditions are altered in the ADHD population. Statistically significant differences between a group of ADHD subjects and an aged-matched control population were obtained in theta and beta rhythms. Our network discriminates between EO/EC EEG regimes in the ADHDs better than in controls, suggesting that differences in EEG patterns between low and normal arousal/attention states are larger in the ADHD population.

bioengineering↗

bioRxiv2018

Echo State Networks Ensemble for SSVEP Dynamical Online Detection

BackgroundRecent years have witnessed an increased interest in the use of steady state visual evoked potentials (SSVEPs) in brain computer interfaces (BCI), SSVEP is considered a stationary brain process that appears when gazing at a stimulation light source.\n\nNew MethodsThe complex nature of brain processes advocates for non-linear EEG analysis techniques. In this work we explore the use of an Echo State Networks (ESN) based architecture for dynamical SSVEP detection.\n\nResultsWhen simulating a 6-degrees of freedom BCI system, an information transfer rate of 49bits/min was achieved. Detection accuracy proved to be similar for observation windows ranging from 0.5 to 4 seconds.\n\nComparison with existing methodsSSVEP detection performance has been compared to standard canonical correlation analysis (CCA). CCA achieved a maximum information transfer rate of 21 bits/minute. In this case detection accuracy increased along with the observation window length\n\nConclusionsAccording to here presented results ESN outperforms standard canonical correlation and has proved to require shorter observation time windows. However ESN and CCA approaches delivered diverse classification accuracies at subject level for various stimulation frequencies, proving to be complementary methods. A possible explanation of these results may be the occurrence of evoked responses of different nature, which are then detected by different approaches. While reservoir computing methods are able to detect complex dynamical patterns and/or complex synchronization among EEG channels, CCA exclusively captures stationary patterns. Therefore, the ESN-based approach may be used to extend the definition of steady-state response, considered so far a stationary process.\n\nHighlightsO_LIWe present a novel SSVEP dynamical detection approach based on ESN.\nC_LIO_LIThis is the first time ESNs are applied to SSVEP based BCI systems.\nC_LIO_LIWe provide experimental validation of proposed methodology.\nC_LIO_LIExperimental results indicate non-stationarity in SSVEP patterns.\nC_LI

bioengineering↗

bioRxiv2018

Deep learning using EEG spectrograms for prognosis in idiopathic rapid eye movement behavior disorder (RBD)

REM Behavior Disorder (RBD) is now recognized as the prodromal stage of -synucleinopathies such as Parkinsons disease (PD). In this paper, we describe deep learning models for diagnosis/prognosis derived from a few minutes of eyes-closed resting electroencephalography data (EEG) collected from idiopathic RBD patients (n=121) and healthy controls (HC, n=91). A few years after the EEG acquisition (4 {+/-} 2 years), a subset of the RBD patients eventually developed either PD (n=14) or Dementia with Lewy bodies (DLB, n=13), while the rest remained idiopathic. We describe first a simple convolutional neural network (DCNN) with a five-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG spectrograms. We treat the data as in audio or image classification problems where deep networks have proven highly successful by exploiting compositional and translationally invariant features in the data. For comparison, we study an even simpler deep recurrent neural network using three stacked Long Short Term Memory network (LSTM) cells or gated-recurrent unit (GRU) cells--with very similar results. The performance of these networks typically reaches 80% ({+/-}1%) classification accuracy in the balanced HC vs. PD-outcome classification problem. In particular, using data from a single EEG channel we obtain an area under the curve (AUC) of 87% ({+/-}1%) while avoiding spectral feature selection. The trained classifier can also be used to generate synthetic spectrograms to study what spectrogram features are relevant for classification, pointing to the presence of theta band bursts and a decrease of power in the alpha band in future PD or DLB patients compared to HCs. We conclude that deep networks may provide a key tool for the analysis of EEG dynamics even from relatively small datasets and enable the delivery of new biomarkers.

bioinformatics↗

bioRxiv2017

Algorithmic complexity of EEG as a prognosis biomarker of neurodegeneration in idiopathic rapid eye movement behavior disorder (RBD)

ObjectiveIdiopathic REM sleep behavior disorder (RBD) is a serious risk factor for neurodegenerative processes such as Parkinsons disease (PD). We investigate the use of EEG algorithmic complexity derived metrics for its prognosis.\n\nMethodsWe analyzed resting state EEG data collected from 114 idiopathic RBD patients and 83 healthy controls in a longitudinal study forming a cohort in which several RBD patients developed PD or dementia with Lewy bodies. Multichannel data from[~] 5 minute recordings was converted to spectrograms and their algorithmic complexity estimated using Lempel-Ziv-Welch compression (LZW).\n\nResultsComplexity measures and entropy rate displayed statistically significant differences between groups. Results are compared to those using the ratio of slow to fast frequency power, which they are seen to complement by displaying increased sensitivity even when using a few EEG channels.\n\nConclusionsPoor prognosis in RBD appears to be associated with decreased complexity of EEG spectrograms stemming in part from frequency power imbalances and cross-frequency amplitude coupling.\n\nSignificanceAlgorithmic complexity metrics provide a robust, powerful and complementary way to quantify the dynamics of EEG signals in RBD with links to emerging theories of brain function stemming from algorithmic information theory.\n\nIndex TermsBiomarkers, EEG, LZW, PD, LBD

neuroscience↗

bioRxiv2017

Occipital tACS bursts during a visual task impact ongoing neural oscillation power, coherence and LZW complexity

Little is known about the precise neural mechanisms by which tACS affects the human cortex. Current hypothesis suggest that transcranial current stimulation (tCS) can directly enhance ongoing brain oscillations and induce long - lasting effects through the activation of synaptic plasticity mechanisms [1]. Entrainment has been demonstrated in in - vitro studies, but its presence in non-invasive human studies is still under debate [2,3]. Here, we aim to investigate the immediate and short-term effects of tACS bursts on the occipital cortex of participants engaged in a change - of - speed detection task, a task that has previously reported to have a clear physiology - behavior relationship, where trials with faster responses also have increased power in {gamma} - oscillations (50 - 80 Hz) [4]. The dominant brain oscillations related to the visual task are modulated using multichannel tACS at 10 and 70 Hz within occipital cortex. We found that tACS stimulation at 10 Hz (tACS 10) enhanced both (8 - 13 Hz) and {gamma} oscillations, in hand with an increase in reaction time (RT) in the change - of - speed detection visual task. On the other hand, tACS at 70Hz desynchronized visual cortices, impairing both phase - locked and endogenous {gamma} - power while increasing RT. While both tACS protocols seem to revert the relationship reported in [4], we argue that tACS produces a shift in attentional resources within visual cortex while leaving unaltered the resources required to conduct the task. This theory is supported by the fact that the correlation between fast RT and high {gamma}- power trials is maintained for tACS sessions too. Finally, we measured cortical excitability by analyzing Event - Related - Potentials (ERP) Lempel - Ziv - Welch Complexity (LZW). In control sessions we observe that lower {gamma} - LZW complexity correlates to faster reaction times. Both metrics are altered by tACS stimulation, as tACS 10 decreased amplitude of the P300 peak, while increasing {gamma}- LZW complexity. To this end, our study highlights the nonlinear cross - frequency interaction between exogenous stimulation and endogenous brain dynamics, and proposes the use of complexity metrics, as LZW, to characterize excitability patterns of cortical areas in a behaviorally relevant timescale. These insights will hopefully contribute to the design of adaptive and personalized tACS protocols where cortical excitability can be characterized through complexity metrics.\n\nAdditional Title Page FootnotesO_LIWe introduce a bursting tACS protocol to study semi-concurrent tACS effects in the visual system and their impact on behavior as measured by reaction time.\nC_LIO_LIBurst 10 Hz tACS (tACS10) applied to the visual cortex entrained {gamma}-oscillations and increased RTs in a change-of-speed detection visual task more than 70 Hz tACS (tACS70) or Control conditions.\nC_LIO_LIBurst tACS10 also decreased amplitude of the P300 peak, while increasing -power and {gamma}-LZW complexity.\nC_LIO_LIPhysiological and behavioral impact of occipital tACS10 and tACS70 was frequency-specific. tACS70 reduced {gamma}-oscillations after 20min of tACS stimulation.\nC_LIO_LICognitive task may determine cortical excitation levels as measured by complexity metrics, as lower {gamma}-LZW complexity correlates to faster reaction times.\nC_LI

neuroscience↗