Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences

Fetterhoff, Dustin; Kraft, Robert A.; Sandler, Roman; Opriş, Ioan; Sexton, Cheryl Ann; Marmarelis, Vasilis Z.; Hampson, Robert E.; Deadwyler, Sam A.

doi:10.3389/fnsys.2015.00130

“…All data was previously used in a study on the effects of cannabinoids on hippocampal multifractality [21, 22]. Briefly, in the sample phase, the rats were presented one of two levers.…”

Section: Resultsmentioning

confidence: 99%

Cannabinoids disrupt memory encoding by functionally isolating hippocampal CA1 from CA3

Sandler

¹

,

Fetterhoff

²

,

Hampson

³

et al. 2017

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Much of the research on cannabinoids (CBs) has focused on their effects at the molecular and synaptic level. However, the effects of CBs on the dynamics of neural circuits remains poorly understood. This study aims to disentangle the effects of CBs on the functional dynamics of the hippocampal Schaffer collateral synapse by using data-driven nonparametric modeling. Multi-unit activity was recorded from rats doing an working memory task in control sessions and under the influence of exogenously administered tetrahydrocannabinol (THC), the primary CB found in marijuana. It was found that THC left firing rate unaltered and only slightly reduced theta oscillations. Multivariate autoregressive models, estimated from spontaneous spiking activity, were then used to describe the dynamical transformation from CA3 to CA1. They revealed that THC served to functionally isolate CA1 from CA3 by reducing feedforward excitation and theta information flow. The functional isolation was compensated by increased feedback excitation within CA1, thus leading to unaltered firing rates. Finally, both of these effects were shown to be correlated with memory impairments in the working memory task. By elucidating the circuit mechanisms of CBs, these results help close the gap in knowledge between the cellular and behavioral effects of CBs.

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“…To the authors' best knowledge, this is the first time that MFDFA has been used for discrimination of ADHD. Several recent studies suggest that changes in the multifractal structure of EEG signal reflect changes in the adaptability of underlying physiological process [19,21]. According to the results shown in Fig.…”

Section: Discussionmentioning

confidence: 91%

“…Fractal analysis of EEG time series has proved to be useful for describing brain activity during sleep [20]. Fetterhoff et al [21] show that multifractal firing patterns of hippocampal spike trains are more complex during a working memory task and significantly declined with the administration of memory impairment in rats. Zorick et al [19] showed the capability of MFDFA to interfere with the recognition of changes in states of consciousness.…”

Section: Introductionmentioning

confidence: 99%

Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals

2018

J. Integr. Neurosci.

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Attention deficit hyperactivity disorder is a neurodevelopmental condition associated with varying levels of hyperactivity, inattention, and impulsivity. This study investigated brain function in children with attention deficit hyperactivity disorder using measures of nonlinear dynamics in electroencephalogram signals during rest. During eyes-closed resting, 19 channel electroencephalogram signals were recorded from 12 ADHD and 12 normal age-matched children. The multifractal singularity spectrum, the largest Lyapunov exponent, and approximate entropy were employed to quantify the chaotic nonlinear dynamics of these electroencephalogram signals. As confirmed by Wilcoxon rank sum test, the largest Lyapunov exponent over left frontal-central cortex exhibited a significant difference between attention deficit hyperactivity disorder subjects and the age-matched control groups. Further, mean approximate entropy was significantly lower in attention deficit hyperactivity disorder subjects in prefrontal cortex. The singularity spectrum was also considerably altered in attention deficit hyperactivity disorder subjects when compared to control children. Evaluation of these features was performed with two classifiers: a support vector machine and a radial basis function neural network. For better comparison, subject classification based on frequency band power was assessed using the same types of classifiers. Nonlinear features provided better discrimination between attention deficit hyperactivity disorder and control than band power features. Under four-fold cross-validation testing, the support vector machine gave 83.33% accurate classification results.

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“…EEG signals were filtered with a 16th order Butterworth band-pass filter to extract four typical frequency bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). For each band, power was calculated by invoking Perceval's theorem and summing the signal in the time domain [22].…”

Section: Eeg Frequency Band Powermentioning

confidence: 99%

Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals

2018

J. Integr. Neurosci.

0

View full text Add to dashboard Cite

Attention deficit hyperactivity disorder is a neurodevelopmental condition associated with varying levels of hyperactivity, inattention, and impulsivity. This study investigated brain function in children with attention deficit hyperactivity disorder using measures of nonlinear dynamics in electroencephalogram signals during rest. During eyes-closed resting, 19 channel electroencephalogram signals were recorded from 12 ADHD and 12 normal age-matched children. The multifractal singularity spectrum, the largest Lyapunov exponent, and approximate entropy were employed to quantify the chaotic nonlinear dynamics of these electroencephalogram signals. As confirmed by Wilcoxon rank sum test, the largest Lyapunov exponent over left frontal-central cortex exhibited a significant difference between attention deficit hyperactivity disorder subjects and the age-matched control groups. Further, mean approximate entropy was significantly lower in attention deficit hyperactivity disorder subjects in prefrontal cortex. The singularity spectrum was also considerably altered in attention deficit hyperactivity disorder subjects when compared to control children. Evaluation of these features was performed with two classifiers: a support vector machine and a radial basis function neural network. For better comparison, subject classification based on frequency band power was assessed using the same types of classifiers. Nonlinear features provided better discrimination between attention deficit hyperactivity disorder and control than band power features. Under four-fold cross-validation testing, the support vector machine gave 83.33% accurate classification results.

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Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences

Cited by 14 publications

References 62 publications

Cannabinoids disrupt memory encoding by functionally isolating hippocampal CA1 from CA3

Cannabinoids disrupt memory encoding by functionally isolating hippocampal CA1 from CA3

Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals

Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals

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