Dream

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.

Chinese Color Nest Project : An accelerated longitudinal brain-mind cohort

“…This baseline data has been released as part of the Consortium for Reliability and Reproducibility (CoRR; Zuo et al, 2014 ), the IPCAS 7 site ( http://dx.doi.org/10.15387/fcp_indi.corr.ipcas7 ), which has been listed as one of the existing, ongoing large-scale developmental dataset ( Rosenberg et al, 2018 ). Head motion data during mock-scanning from devCCNP-CAS are recently demonstrated with frequency-specific evidence to support motion as a developmental trait across children and adolescents by the development of a neuroinformatic tool, namely DREAM ( Gong et al, 2021 ).…”

Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability

“…Advanced by the fast imaging protocols (TR = 720ms), HCP test-retest data allows to obtain more oscillation classes than traditional rfMRI acquisitions (typical TR = 2s). We incorporate the Buzsaki’s framework with the HCP dataset using the DREAM toolbox (Gong et al, 2021) in the Connectome Computation System to decompose the time series into the six slow bands (Fig. 3a): slow-6 (0.0069-0.0116 Hz), slow-5 (0.0116-0.0301 Hz), slow-4 (0.0301-0.0822 Hz), slow-3 (0.0822-0.2234 Hz), slow-2 (0.2234-0.6065 Hz), slow-1 - (0.6065-0.6944 Hz).…”

“…Still existing studies, however, have advocated adopting a multi-frequency perspective to examine the amplitude of brain activity at rest (Zuo et al, 2010) and its network properties (Achard et al, 2006). This approach has been spurred along by recent advances in multi-banded acquisitions and fast imaging protocols, offering rfMRI studies a way to examine spontaneous brain activity at much higher frequencies that may contain neurobiologically meaningful signals (Gong et al, 2021). Our study provides strong evidence of highly reliable signals across higher slow-frequency bands, which are derived with the hierarchical frequency band theory of neuronal oscillation system (Buzsaki & Draguhn, 2004).…”

“…Advanced by the fast imaging protocols offered by the HCP scanner, the short scan interval (TR = 720ms) allows us to obtain more oscillation classes that the traditional rfMRI method. We incorporate the Buzsaki’s framework (Buzsaki & Draguhn, 2004; Penttonen & Buzsáki, 2003) with the HCP fast-TR datasets by using the DREAM toolbox (Gong et al, 2021) in the Connectome Computation System (Xing, Xu, Jiang, Wang, & Zuo, 2022; Xu, Yang, Jiang, Xing, & Zuo, 2015). It decomposed the time series into the six slow bands as illustrated in Fig.…”

Nomogram for prediction of hearing rehabilitation outcome in children with congenital sensorineural hearing loss after cochlear implantation

“…The time series of rs-fMRI data were filtered into four frequency bands with the decoding rhythms of the brain system (DREAM) software [ 34 ]: slow-5 (0.012–0.030 Hz), slow-4 (0.030–0.082 Hz), slow-3 (0.082–0.224 Hz), and slow-2 (0.224–0.25 Hz). Then, rs-fMRI data were preprocessed with Data Processing & Analysis for Brain Imaging package (DPABI, V6.1, http://rfmri.org/dpabi ) based on the MATLAB 2021b platform [ 35 ].…”