Abstract

Rhythms of the brain are generated by neural oscillations across multiple frequencies. These oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. In practice, the number and ranges of decodable frequency intervals are determined by sampling parameters, often ignored by researchers. To improve the situation, we report on an open toolbox with a graphical user interface for decoding rhythms of the brain system (DREAM). We provide worked examples of DREAM to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM decoded the head motion oscillations and uncovered that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age of 7 to 9 years. It is interesting that the higher frequency bands contain more head movements, and showed stronger age-motion associations but weaker sex-motion interactions. Using data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. The resting-state brain ranks its spontaneous oscillation’s amplitudes spatially from high in ventral-temporal areas to low in ventral-occipital areas when the frequency band increased from low to high, while those in part of parietal and ventral frontal regions are reversed. The higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, DREAM adds a reliable and valid tool to mapping human brain function from a multiple-frequency window into brain waves.

Highlights

  • Rhythms of the brain are generated by neural oscillations occurring across multiple frequencies (Buzsaki 2006)

  • The natural logarithm linear law (N3L) offers a theoretical framework for parcellating these brain oscillations into multiple frequency intervals linking to distinct physiological roles (Penttonen and Buzsaki 2003)

  • FMRI has the potential to contribute to the study of certain neural oscillations in the human brain in vivo

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Summary

Introduction

Rhythms of the brain are generated by neural oscillations occurring across multiple frequencies (Buzsaki 2006). The natural logarithm linear law (N3L) offers a theoretical framework for parcellating these brain oscillations into multiple frequency intervals linking to distinct physiological roles (Penttonen and Buzsaki 2003). Natural logarithm scale, resulting in a full parcellation of the whole frequency domain where each parcel of the frequencies is fixed in theory, namely frequency intervals These frequency intervals have been repeatedly observed experimentally (Buzsaki and Draguhn 2004). This characteristic suggests that distinct physiological mechanisms may contribute to distinct intervals. These brain oscillations can be measured by different technologies such as EEG and MEG. FMRI has the potential to contribute to the study of certain neural oscillations in the human brain in vivo

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