Probing Neural Oscillations of Developmental Disorders From a Multi-band Perspective.

The brain oscillates over a wide frequency range and generates oscillations in multiple frequency bands. These oscillations comprise a high-order complex system that maintains the functioning of mental and behavioral activities. In the last century, neuroscientists adopting electrophysiological techniques have progressively revealed that brain oscillations at different frequency bands are associated with different functions and mental states ( Buzsaki and Draguhn, 2004 Buzsaki G. Draguhn A. Neuronal oscillations in cortical networks. Science. 2004; 304: 1926-1929 Crossref PubMed Scopus (4351) Google Scholar ). A frequency hierarchy of brain oscillations has gradually emerged ( Penttonen and Buzsáki, 2003 Penttonen M. Buzsáki G. Natural logarithmic relationship between brain oscillators. Thalamus Relat Syst. 2003; 2: 145-152 Crossref Scopus (0) Google Scholar ). Although electrophysiological techniques have high frequency resolution to distinguish the characteristics among frequency bands, their spatial resolution is insufficient to pinpoint the precise source of oscillations. Meanwhile, by measuring blood oxygen level-dependent (BOLD) oscillations, functional magnetic resonance imaging (fMRI) has made it possible to study the high-precision spatial distribution pattern of neural oscillations. However, restricted by the sampling rate and the signal-to-noise ratio of the technique, early fMRI studies mainly focused on a single frequency range of approximately 0.01–0.1 Hz. In the past decade, with the improvement of instrument performance, the signal detectable by fMRI has been able to cover practically the entire range of slow oscillations (slow-1 to slow-6) in the frequency hierarchy. Thus, the time has come to age to investigate the neural significance of multiple frequency BOLD oscillations. Piecing together the results of existing multi-band fMRI studies, different frequency bands exhibit distinct characteristics and associated functions. High-frequency oscillations are more localized in the primary areas and are associated with primary sensorimotor functions, whereas low-frequency oscillations show higher density in associative areas, have more long-distance connections and are associated with high-order functions ( Gohel and Biswal, 2015 Gohel S.R. Biswal B.B. Functional integration between brain regions at rest occurs in multiple-frequency bands. Brain Connect. 2015; 5: 23-34 Crossref PubMed Scopus (137) Google Scholar , Gong et al., 2021 Gong Z.Q. Gao P. Jiang C. Xing X.X. Zuo X.N. DREAM: a toolbox to decode rhythms of the brain system. Neuroinformatics. 2021; 19: 529-545 Crossref PubMed Scopus (14) Google Scholar , Thompson and Fransson, 2015 Thompson W.H. Fransson P. The frequency dimension of fMRI dynamic connectivity: network connectivity, functional hubs and integration in the resting brain. Neuroimage. 2015; 121: 227-242 Crossref PubMed Scopus (89) Google Scholar , Zuo et al., 2010 Zuo X.N. Di Martino A. Kelly C. Shehzad Z.E. Gee D.G. Klein D.F. Castellanos F.X. Biswal B.B. Milham M.P. The oscillating brain: complex and reliable. Neuroimage. 2010; 49: 1432-1445 Crossref PubMed Scopus (1078) Google Scholar , Gong, 2023 Gong Z.-Q. Zuo X.-N. Connectivity gradients in spontaneous brain activity at multiple frequency bands. Cereb Cortex. 2023; 33: 9718-9728 Crossref PubMed Scopus (1) Google Scholar ). Furthermore, numerous clinical studies have demonstrated that functional alterations are frequency-specific in a variety of neurological and mental diseases ( Martino et al., 2016 Martino M. Magioncalda P. Huang Z. Conio B. Piaggio N. Duncan N.W. Rocchi G. Escelsior A. Marozzi V. Wolff A. Inglese M. Amore M. Northoff G. Contrasting variability patterns in the default mode and sensorimotor networks balance in bipolar depression and mania. Proc Natl Acad Sci U S A. 2016; 113: 4824-4829 Crossref PubMed Scopus (156) Google Scholar , Veldsman et al., 2017 Veldsman M. Egorova N. Singh B. Mungas D. DeCarli C. Brodtmann A. Low-frequency oscillations in default mode subnetworks are associated with episodic memory impairments in Alzheimer's disease. Neurobiol Aging. 2017; 59: 98-106 Crossref PubMed Scopus (9) Google Scholar , Yu et al., 2016 Yu X. Yuan B. Cao Q. An L. Wang P. Vance A. Silk T.J. Zang Y. Wang Y. Sun L. Frequency-specific abnormalities in regional homogeneity among children with attention deficit hyperactivity disorder: a resting-state fMRI study. Sci Bull. 2016; 61: 682-692 Crossref Scopus (14) Google Scholar , Wang, 2020 Wang Z. Liu Y. Ruan X. Li Y. Li E. Zhang G. Li M. Wei X. Aberrant amplitude of low-frequency fluctuations in different frequency bands in patients with Parkinson’s disease. Front Aging Neurosci. 2020; 12: 576682 Crossref PubMed Scopus (17) Google Scholar , Zhou, 2021 Zhou J. Ma X. Li C. Liao A. Yang Z. Ren H. Tang J. Li J. Li Z. He Y. Chen X. Frequency-specific changes in the fractional amplitude of the low-frequency fluctuations in the default mode network in medication-free patients with bipolar II depression: a longitudinal functional MRI study. Frontiers Psychiatry. 2021; 11: 574819 Crossref PubMed Scopus (10) Google Scholar ). Therefore, it is essential to conduct research from a multi-band perspective to describe brain function in a more comprehensive and accurate manner. Frequency Dependent Changes of Regional Homogeneity in Children with Growth Hormone DeficiencyNeuroscienceVol. 530PreviewGrowth hormone (GH) is produced in the pituitary gland, which not only has a significant effect on growth, but also has a certain effect on brain development and neuroprotection (Tang et al., 2022). Growth hormone deficiency (GHD) is a recognized endocrine disease and a common symptom of pathological short stature, which is usually diagnosed in early childhood (Murray et al., 2016). The main clinical manifestations in children with GHD are growth retardation and bone age delay, often accompanied with short stature and GH levels of <10 μg/L, and its incidence rate is 1/4000 (Society, 2000; Murray et al., 2016). Full-Text PDF