Dataset Information

Human intracranial high-frequency activity maps episodic memory formation in space and time.

ABSTRACT: Noninvasive neuroimaging studies have revealed a network of brain regions that activate during human memory encoding; however, the relative timing of such activations remains unknown. Here we used intracranially recorded high-frequency activity (HFA) to first identify regions that activate during successful encoding. Then, we leveraged the high-temporal precision of HFA to investigate the timing of such activations. We found that memory encoding invokes two spatiotemporally distinct activations: early increases in HFA that involve the ventral visual pathway as well as the medial temporal lobe and late increases in HFA that involve the left inferior frontal gyrus, left posterior parietal cortex, and left ventrolateral temporal cortex. We speculate that these activations reflect higher-order visual processing and top-down modulation of attention/semantic information, respectively.

SUBMITTER: Burke JF

PROVIDER: S-EPMC4289670 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Human intracranial high-frequency activity maps episodic memory formation in space and time.

Burke John F JF Long Nicole M NM Zaghloul Kareem A KA Sharan Ashwini D AD Sperling Michael R MR Kahana Michael J MJ

NeuroImage 20130701

Noninvasive neuroimaging studies have revealed a network of brain regions that activate during human memory encoding; however, the relative timing of such activations remains unknown. Here we used intracranially recorded high-frequency activity (HFA) to first identify regions that activate during successful encoding. Then, we leveraged the high-temporal precision of HFA to investigate the timing of such activations. We found that memory encoding invokes two spatiotemporally distinct activations: ...[more]

PMID: 23827329

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Project description:High-frequency oscillations >80 Hz (HFOs) have unique features distinguishing them from spikes and artifactual components that can be well-evidenced in the time-frequency representations. We introduce an unsupervised HFO detector that uses computer-vision algorithms to detect HFO landmarks on two-dimensional (2D) time-frequency maps. To validate the detector, we introduce an analytical model of the HFO based on a sinewave having a Gaussian envelope, for which analytical equations in time-frequency space can be derived, allowing us to establish a direct correspondence between common HFO detection criteria in the time domain with the ones in the frequency domain, used by the computer-vision detection algorithm. The detector identifies potential HFO events on the time-frequency representation, which are classified as true HFOs if criteria regarding the HFO's frequency, amplitude, and duration are met. The detector is validated on simulated HFOs according to the analytical model, in the presence of noise, with different signal-to-noise ratios (SNRs) ranging from -9 to 0 dB. The detector's sensitivity was 0.64 at an SNR of -9 dB, 0.98 at -6 dB, and >0.99 at -3 dB and 0 dB, while its positive prediction value was >0.95, regardless of the SNR. Using the same simulation dataset, our detector is benchmarked against four previously published HFO detectors. The F-measure, a combined metric that takes into account both sensitivity and positive prediction value, was used to compare detection algorithms. Our detector surpassed the other detectors at -6, -3, and 0 dB and had the second best F-score at -9 dB SNR after the MNI detector (0.77 vs. 0.83). The ability to detect HFOs in clinical recordings has been tested on a set of 36 intracranial electroencephalogram (EEG) channels in six patients, with 89% of the detections being validated by two independent reviewers. The results demonstrate that the unsupervised detection of HFOs based on their 2D features in time-frequency maps is feasible and has a performance comparable or better than the most used HFO detectors.

Dataset Information

Human intracranial high-frequency activity maps episodic memory formation in space and time.

Publications

Human intracranial high-frequency activity maps episodic memory formation in space and time.

Similar Datasets

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