[스크랩] fMRI of AD

 

Clinical fMRI studies of AD

Human fMRI studies of AD have led to some promising findings in recent years. These studies can be broadly divided into two categories: those examining brain network activity during the resting state (so-called resting state fMRI) (Chhatwal and Sperling, 2013; Dennis and Thompson, 2014; Dickerson and Sperling, 2008; Liu et al., 2008; Weiner et al., 2012) and those that measure responses to specific tasks or stimuli (Chhatwal and Sperling, 2013; Dickerson and Sperling, 2008; Li et al., 2015; Sugarman et al., 2012; Weiner et al., 2012).

Resting state fMRI in AD patients has revealed decreased functional connectivity between numerous cortical brain regions and the hippocampus (Greicius et al., 2004; Sheline et al., 2010). Decreased connectivity has also been seen in the default mode network (Box 1), specifically in brain regions such as the posterior cingulate and medial prefrontal cortices (Sorg et al., 2007; Zhang et al., 2009). Conversely, some brain regions exhibit increased connectivity, which some believe to be caused by compensatory mechanisms in the brains of AD patients (Bäckman et al., 2000; Grady et al., 2003; He et al., 2007; Prvulovic et al., 2002; Zhang et al., 2009). Activity changes in the default mode network have also been used to build predictive models of the progression from mild cognitive impairment to AD (Petrella et al., 2011). The authors compared the default mode networks from patients with mild cognitive impairment with those from healthy controls. They followed up with the the patients a few years later, and found that those patients whose default mode networks were least similar to those of healthy controls were more likely to have progressed to AD (Petrella et al., 2011).

In addition to resting state imaging, fMRI can be performed while patients undertake various tasks. For instance, during a visual discrimination task, AD patients were found to have increased activity in the occipitotemporal cortex and decreased activity in the superior parietal lobule relative to healthy controls (Prvulovic et al., 2002). In another study using a verbal encoding and recognition task, the AD group had reduced activation in the medial temporal lobe and increased activation in the left prefrontal brain regions relative to healthy controls (Rémy et al., 2005). But, even though proven fruitful for studying AD, fMRI also has important limitations that need to be considered.

 

 

Confounding factors in fMRI studies

As a clinical imaging tool, fMRI is both powerful and widespread, and able to show changes in brain activity during specific tasks and/or while the patient is at rest. The key advantages of this imaging technique include its noninvasive nature and its ability to image the entire brain. These benefits do, however, come with some tradeoffs. For instance, fMRI is a relatively slow method, measuring responses in the order of seconds compared with the millisecond precision of neuronal firing (Lee et al., 2010).

Although fMRI is often used to indirectly measure neural activation, other factors can affect the observed signals and must be considered (Uludağ and Blinder, 2018). This is partly because many parameters, such as magnetic field strength, can vary between studies, and changes in these parameters can result in different temporal or spatial brain activation patterns. Additionally, because fMRI relies on hemodynamic measurements (Box 1) as a proxy for neural function (Logothetis et al., 2001; Ogawa et al., 1992), conditions that affect blood flow can lead to an incorrect interpretation of the observed changes in brain activity. Cerebral amyloid angiopathy is one such condition associated with AD, whereby Aβ buildup on cerebral blood vessels leads to impairments in vascular dilation, to a degree that is detectable by fMRI (Princz-Kranz et al., 2010). Other vascular pathologies in AD (Klohs et al., 2014) include decreased vascular density (Ielacqua et al., 2016), reduced cerebral blood volume (Zerbi et al., 2013) and cerebral hypoperfusion (Weidensteiner et al., 2009). When interpreting fMRI results, it is therefore important to consider how both neural and vascular factors are contributing to the changes observed. This concerns comparisons between different animal models, but also when comparing human and rodent fMRI studies.

Alternatively, electroencephalography (EEG; Box 1) can be used to measure brain activity at a higher temporal resolution than fMRI, but at a lower spatial resolution (Engel et al., 2005). Implantable electrodes can record signals from specific areas of the brain, thereby increasing spatial resolution, but are invasive (Engel et al., 2005). Tools such as PET (Box 1) can be used to measure metabolic function in the brain via the injection of radioactive compounds, such as 18-fluorodeoxyglucose, but this method also has low temporal resolution and is invasive, owing to the injection of a radioactive substance (Nordberg, 2004). That said, a variety of measuring methods can be used in parallel to paint a more complete picture of AD.