Functionally related brain regions exhibit correlation of low frequency (0.01–0.1 Hz) fluctuations in the resting state fMRI, and the amplitude of low frequency fluctuations (ALFF) is higher in grey matter than in white matter (Biswal, Zerrin Yetkin, Haughton, & Hyde, 1995). Researchers use ALFF to characterize resting state blood oxygenation level dependent (BLOD) signal power (Zang et al., 2007). A distinct ALFF difference was found between eyes-open and eyes-closed conditions in the visual cortex, which suggest that ALFF may represent physiological states of the brain (Yang et al., 2007). In fact, with the application of ALFF in neurodegenerative diseases, studies have demonstrated that the specific changes of ALFF in some brain regions can be used to characterize normal and abnormal aging.
For example, inter-subject ALFF variability was larger (L. Yan, Zhuo, Wang, & Wang, 2011) and the ALFF of the default mode network demonstrated a decrease (La, Nair, et al., 2016) in older compared to younger subjects. A general pattern of ALFFAD < ALFFMCI < ALFFhealthy older was found in the posterior cingulate cortex (PCC), and the ALFF value of the PCC was positively correlated with mini-mental state examination (MMSE) score (Z. Wang et al., 2011). There was a significant positive relationship between amyloid-β, the peptide that is abnormally deposited in AD, and memory among individuals (healthy older participants and MCI) with low levels of fractional ALFF in the left insula and left inferior frontal gyrus, which suggested that the effects of AD pathology on cognitive performance is modified by fractional ALFF in frontal regions (F. Lin et al., 2017). Reduced fractional ALFF (0.009-0.08 Hz) in prefrontal/frontal regions is associated with decreased inhibitory control occurring during healthy aging (Hu, Chao, Zhang, Ide, & Li, 2014). Compared with cognitively normal older adults, older adults with excellent cognition showed higher oscillations (0.01-0.08 Hz) in the right fusiform gyrus, left middle temporal gyrus, and lower oscillations in right anterior cingulate cortex, right middle frontal gyrus, and left precentral gyrus (X. Wang et al., 2017). In addition, the ALFF in the BOLD signal is highly correlated with regional metabolic rate of glucose (Nugent et al., 2015), as well as functional connectivity within brain networks, regional homogeneity, degree of centrality, and fractional ALFF (Jiao et al., 2019), which is physiologically meaningful (Z. Wang et al., 2011; Zang et al., 2007).
Other previous studies have pointed out that the most commonly used low frequency band between 0.01-0.1 Hz may not be adequate to fully investigate resting brain activity (Zuo et al., 2010). Zuo et al. (2010) differentiated four frequency bands from 0.01-0.25 Hz, including slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz), and slow-2 (0.198–0.250 Hz). Compared with the cognitively normal older adults, amnestic MCI patients had widespread ALFF abnormalities in the slow-5 and slow-4 band (Han et al., 2011), AD patients have greater ALFF values in the slow-4 band in the ventral default mode network (i.e., cuneal cortex and lateral occipital cortex) (Veldsman et al., 2017). In comparison to healthy young adults, healthy older individuals had decreased slow-5 ALFF in the default mode network, and increased ALFF in the task-positive networks (the primary visual and sensorimotor networks), while stroke patients of between one to six months post stroke onset demonstrated a global ALFF reductions (La, Mossahebi, et al., 2016). These results indicate inherent neural integrity or adaptive reorganization in aging, stroke, and dementia (La, Nair, et al., 2016; X. Wang et al., 2017). Assessment of multiple frequency bands in resting state ALFF has also been used to investigate brain changes in other diseases, such as schizophrenia (Yu et al., 2014), social anxiety disorder (Y. Zhang et al., 2015), internet gaming disorder (X. Lin, Jia, Zang, & Dong, 2015), obsessive compulsive disorder (Giménez et al., 2017), depression (L. Wang et al., 2016), and psychosis (Gohel et al., 2018).
Recent researches has shown that cognitive changes were associated with ALFF at specific frequency bands (Giménez et al., 2017). For example, a positive linear trend was observed between phonemic verbal fluency score and fractional ALFF values within the slow-5 band in subacute stroke patients (La, Nair, et al., 2016). Greater slow-4 ALFF decline in the right putamen was significantly associated with memory decline in MCI patients (Ren et al., 2016). Poorer episodic memory in AD patients was associated with greater slow-4 band ALFF in the ventral default mode network (Veldsman et al., 2017). Therefore, studying ALFF in different frequency bands in elderly populations can help elucidate cognitive aging processes. Despite the relevance of ALFF to cognitive aging, the relationship between normal cognitive aging and altered patterns of the intrinsic brain activity has not been examined in a longitudinal design.
Based on previous studies, we hypothesized that ALFF changes associated with cognitive decline would show frequency specificity. Thus, we applied the ALFF method to investigate changes of the regional spontaneous brain activity at two-time points scans. Our study was to explore the frequency-specific characteristics of resting state ALFF, including the five specific frequency bands from slow-6 band to slow-2 band, in cognitively normal aging over four years.