Short communicationAn improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF
Introduction
Recently, there has been increasing interest in using functional magnetic resonance imaging (fMRI) to investigate the ongoing neuronal processes at the “resting” state. Biswal and colleagues first reported that the spontaneous low-frequency (0.01–0.08 Hz) fluctuations (LFFs) in fMRI were highly synchronous between the right and left primary motor cortex at rest. The functional connectivity pattern of LFFs was quite similar to the activation pattern obtained from a bilateral finger-tapping task (Biswal et al., 1995), suggesting that LFFs might contain physiologically meaningful information. Combining electroneurophysiological recordings and fMRI, many studies have suggested that the LFFs of blood oxygenation level-dependent (BOLD) fMRI signals are closely related to the spontaneous neuronal activities (Goldman et al., 2002, Logothetis et al., 2001, Lu et al., 2007, Mantini et al., 2007). Numerous resting-state fMRI studies have revealed high synchronization of LFFs between the bilateral visual areas (Lowe et al., 1998), between the bilateral auditory areas (Cordes et al., 2000), within the language systems (Hampson et al., 2002) and within the default mode network (Greicius et al., 2003). Abnormal functional connectivity was found in a wide range of brain disorders including Alzheimer's disease (Greicius et al., 2004, Wang et al., 2006), attention deficit hyperactivity disorder (Castellanos et al., 2008, Tian et al., 2006, Uddin et al., 2008), depression (Anand et al., 2005), schizophrenia (Jafri et al., 2008) and so on. Although functional connectivity analysis can provide us with more holistic information of a set of brain regions within a network, it does not reveal the BOLD signals change of the regional spontaneous activity. Moreover, abnormal connectivity among brain regions could not tell us precisely in which brain region the spontaneous activity is abnormal.
Detection of regional abnormalities is crucial to the clinical studies and even clinical applications. Resting-state positron emission tomography (PET) and electroencephalography (EEG) are two fundamental neuroimaging tools that are widely used in clinical practice. PET is used to evaluate the abnormality of regional brain metabolism and EEG can be used to reveal the changes of the power spectrum at some specific electrode positions. For the resting-state fMRI, such a robust index for detecting regional activity is lacking. Nevertheless, a few resting-state fMRI studies have investigated the regional brain activities. For example, it was reported that the root mean square (rms) of the LFFs in white matter was lower than that in gray matter by about 60% (Biswal et al., 1995, Li et al., 2000). Kiviniemi et al. (2000) used relative power of the peak over the noise fit to generate “resting activation” map in the visual cortex, and the results were similar to that obtained from correlation analysis. Using a discrete cosine basis set containing 120 regressors that spanned the frequency range of 0–0.1 Hz, Fransson (2005) found strong spontaneous fluctuation within the default mode network. Furthermore, Fransson (2006) indicated that the mean power spectral density of intrinsic LFFs in the default mode network was significantly decreased during a working memory task compared to rest. Based on the previous studies, Zang et al. (2007) developed an index, amplitude of LFFs (ALFF) in which the square root of power spectrum was integrated in a low-frequency range, for detecting the regional intensity of spontaneous fluctuations in BOLD signal. This index was subsequently used to differentiate two resting states, i.e., eyes open vs. eyes closed (Yang et al., 2007). Furthermore, ALFF has already been applied to patient studies including attention deficit hyperactivity disorder (Zang et al., 2007) and early Alzheimer's disease (He et al., 2007).
Although ALFF seems to be a promising method for detecting regional signals change of spontaneous activity, an important issue remains unclear. Raichle et al. (2001) have demonstrated that the posterior cingulate cortex/precuneus (PCC/PCu), medial prefrontal cortex (MPFC) and bilateral inferior parietal lobule (IPL), which constitute the so-called default mode network, consistently showed significantly higher activity (i.e., oxygen consumption and blood flow) than the global mean at rest. Similarly, Zang et al. (2007) performed a one-sample t-test and found that the PCC/PCu and MPFC showed significant higher ALFF than the global mean ALFF. However, some cistern areas also showed significant higher ALFF (Zang et al., 2007), probably due to higher physiological noise in these areas.
The aim of the current study is to examine resting-state fMRI signals in cisterns and cortical areas, and seek to improve the ALFF analysis method. We propose a fractional ALFF (fALFF) approach, in which the ratio of power spectrum of low-frequency (0.01–0.08 Hz) range to that of the entire frequency range was computed. We further test this improved approach on two-independent data sets.
Section snippets
Subjects
Subjects comprised of two groups: 24 boys and 16 adults (8 females), respectively. The data of the boy group was from the control group of a previous study (Cao et al., 2006) and the adult group data was also used in a previous study (Zang et al., 2004). All subjects were right-handed healthy volunteers with no history of head trauma, neurological or psychiatric disorders. Data from 2 boys and 2 adults were excluded from further analysis due to excessive head motion (see details in Section 2.3
Results and discussion
As predicted, the one-sample t-tests of the original ALFF analysis show significant higher ALFF in the PCC, PCu and MPFC (Fig. 1a and c). These results are consistent with previous resting-state PET studies (Raichle et al., 2001) and resting-state fMRI studies (Fransson, 2005, He et al., 2004), indicating that the higher ALFF within these areas may reflect higher spontaneous neuronal activity during resting but awake state. However, significantly higher ALFF can also been seen in the
Acknowledgements
This work was supported by Natural Science Foundation of China (30470575, 30530290, 30621130074 and 30770594) and by National Key Basic Research and Development Program (973) (2003CB716101).
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