Paolo Maccallini, Andrea Catenacci
In this blog post, we discuss some of the evidence of abnormal activity of the anterior cingulate cortex (ACC) in ME/CFS patients. In paragraph two there is a brief introduction to this part of the cortex with basic anatomy and functions. In paragraph three, studies on abnormal ACC activation and abnormal metabolites in ACC in ME/CFS patients are reviewed, along with papers on attention deficits in this patient population that might be related to ACC disfunction. In paragraph four, the case of a ME/CFS patient with abnormal metabolites in ACC is discussed. In paragraph five you will find some mathematical notes on how to assess statistical significance for a measure when you know only the mean and the standard deviation for the control group.
2. Anterior Cingulate Cortex
The cingulate cortex is a region of the cortex that wraps around the corpus callosum (the connection between the two hemispheres). In Figure 1, we have highlighted in red the cingulate cortex in a sagittal MRI section of the ME/CFS patient (on the left) whose brain metabolic data will be mentioned in this blog post. On the right of the same figure, you can see the cingulate cortex in orange in a photo of the right hemisphere of a human brain. As you can see, the cingulate cortex is delimited by the cingulate sulcus and the subparietal sulcus in its upper bound, while its lower limit is defined by the corpus callosum.
Here, we are interested in the anterior part of the cingulate cortex (ACC) which is the area on the left of the blue dashed line in the sagittal section (Figure 1, left). This area can be further divided into dorsal ACC (dACC) and ventral ACC (vACC) as indicated in Figure 1. While the dACC is linked to cognitive processing and decision making, the vACC is devolved to processing emotions and regulating the endocrine and autonomic response to them, as reviewed here: (Jumah F.R. et Dossani R.H. 2019).
It has been possible to recognize through functional magnetic resonance imaging (fMRI) studies that ACC is selectively activated by cognitive tasks in which there are conflicting simultaneous representations, such as in the so-called Stroop task, a test in which the subject has to name the colour a word is written in, while the word actually indicates another colour. As an example, name the colour the following word is written in:
In order to recognize the colour, you have likely engaged your ACC that has sensed a possible source of error and has recruited the dorsolateral prefrontal cortex (DLPC) to solve the conflict (Carter C.S. et van Veen V. 2007). We can state that:
Prop. 1. The Stroop task may be considered a selective measure of ACC activity.
In adults with attention deficit and hyperactive disorder (ADHD) – a condition characterized by pathological lack of attention – the ACC has reduced volume (Makris N. et al. 2010), and during Stroop task, there is a lower activation of the ACC, compared to controls (Bush G. et al. 1999).
Other clues on the possible functions of the ACC come from the studies of apathy, a disabling symptom that is shared by many neurological conditions, including Parkinson’s disease, Alzheimer’s disease, and Huntington’s disease. It is defined as reduced motivation, abulia, decreased empathy, and lack of emotional involvement (Moretti R. et Signori R. 2016) and a review of several studies involving different diseases has found that apathy is strongly associated with loss of activity in the dACC and the ventral striatum (VS, which is a part of the basal ganglia, is made up by the nucleus accumbens and the olfactory tubercle) (Le Heron C. et al. 2018). VS has a projection to the ventral pallidum (VP) which in turn projects to the dACC. Thus, VS forms loops with the dACC, via the VP, as reviewed here.
This evidence may lead to the following proposition:
Prop. 2. Abnormalities of the ACC can cause deficits in attention (particularly in cases of simultaneous conflicting representations, as in the Stroop task) and apathy.
In iatrogenic systemic inflammation, ACC is activated more than normal, during tasks with conflicting representations. In one study, typhoid vaccination was administered to healthy volunteers (while placebo was administered to a matched control group). Both groups underwent fMRI of the brain while performing the Stroop task: the typhoid group activated the ACC bilaterally more than control, along with the right DLPC (Harrison N.A. et al. 2009). Similarly, patients with hepatitis C virus treated with INF α, exhibited increased activation of dACC (bilaterally) in comparison with a control group made up by untreated patients with hepatitis C virus, while performing a task with conflicting representations (the position of a dot on a two dimensional space had to be recognized by pressing keys displayed on a row) (Capuron L. et al. 2005). In both cases, one may infer that iatrogenic inflammation leads to a greater effort in solving cognitive tasks which is demonstrated by greater activation of the ACC. The reason why the brain has to use greater resources to accomplish the same task if there is an inflammatory process is unclear, but it is interesting to note that in the first study, the activation of left ACC during the Stroop task was directly proportional to the level of perceived fatigue and confusion.
This leads to the following proposition:
Prop. 3. In iatrogenic systemic inflammation, there is greater activation of the ACC than in controls, during tasks with conflicting representations. Moreover, left ACC activation correlates with perceived fatigue and confusion.
3. Anterior Cingulate Cortex in ME/CFS
During Stroop task, 43 CFS patients exhibited a different pattern of activation of the brain as measured by fMRI. In particular, several regions of the ACC cortex were activated in ME/CFS patients and not in healthy controls. Namely bilateral A24rv (rostroventral area 24), left A24cd (caudodorsal area 24), right A23c (caudal area 24), left A32p (pregenual area 32) (Shan Z.Y. et al. 2018).
In two recent studies, ME/CFS patients showed slower processing speed than the healthy controls in Stroop task (Shan Z.Y. et al. 2018), (Robinson L.J. et al. 2019). A previous review about cognitive functions in ME/CFS patients concluded that the assessment of executive functioning using variants of the Stroop task has consistently identified slowed response speeds in patients compared to healthy controls (Cvejic E. et al. 2016).
The cingulate cortex is one of the areas in which ME/CFS patients exhibited more microglia activation than healthy controls in a study on 9 patients and 10 healthy controls. The measure was made using the PET ligand 11C-(R)-PK11195, a molecule that specifically binds the 18 kDa translocator protein (TSPO), a receptor that is expressed by activated microglia or astrocytes (Nakatomi Y. et al. 2014). In a study employing whole-brain magnetic resonance spectroscopy (MRS), the main difference between ME/CFS patients and healthy controls was an increase in the ratio of choline on creatine (Cho/Cr) in the ACC, mainly in the left hemisphere (Mueller C. et al. 2019). This metabolite is usually considered a marker for neuroinflammation because of its relationship to glial activation and blood-brain barrier integrity (Albrecht D.S. et al. 2016).
From these experimental results we can state that:
Prop. 4. In ME/CFS there is microglia activation in ACC and at the same time ACC is overactive and yet inefficient during Stroop task, compared to healthy controls.
So, it is not surprising to find in the review on ME/CFS published by the Academy of Medicine in 2015 an observation about ACC: after mentioning all the experimental results on attentional deficits in this patient population, the reviewers suggested that structural differences in ACC could be a biomarker of conditions such as ME/CFS (R, bottom of page 103).
Interesting enough, poor processing speed in the Stroop task was associated with reduced autonomic control of cardiovascular function, in ME/CFS patients (Robinson L.J. et al. 2019). This might be due to the role that vACC plays in regulating autonomic response to emotions (Jumah F.R. et Dossani R.H. 2019). So ACC may play a role in orthostatic intolerance, which is a common feature of ME/CFS.
All these data can be interpreted in several ways, given the lack of consensus on the pathologic mechanism underlying ME/CFS. We propose a model in Figure 2 which takes into account not only what has been here discussed about ACC, but also the amount of evidence about brainstem dysfunction (as recently reviewed by VanElzakker M.B. et al.), which include hypoperfusion (SPECT) (Costa DC et al. 1995) and hypometabolism (PET) (Tirelli U et al. 1998) in whole brainstem, reduced volume (vMR) in midbrain (Barden LR et al. 2010), and microglia activation in pons and midbrain (Nakatomi Y. et al. 2014). Recently, brainstem pathology in ME/CFS (midbrain serotoninergic neurons alteration, in particular) has been theorized as part of a mathematical model on disrupted tryptophan metabolism (Kashi A.A. et al. 2019), (R). We recognize that our model in Figure 2 is based on largely arbitrary assumptions.
The idea of an increase in ACC activation as a compensatory measure comes from the experiments on iatrogenic systemic inflammation, in which one of the possible explanations for the observations is that ACC has to compensate for a diminished efficiency of the brain due to the ongoing inflammatory processes. The idea that microglial activation might be due to greater activity in ACC comes from recent studies on reduced deformability of erythrocytes in ME/CFS patients (Saha A.K. et al 2019) and the notion that microglia inflammation can be a consequence of poor oxygen supply (Kiernan E. A. et al. 2016). The presence of a positive feedback might explain why in the latest MRS study, only ACC displayed a significant increase in Cho/Cr (Mueller C. et al. 2019).
4. A case study
The levels of metabolites in four brain regions of a young male with a diagnosis of ME/CFS and an illness duration of about 5 years are reported in Table 1, along with mean values and standard deviations for the same measures relative to a healthy control group. Since the distributions of the measures in the control group were not provided, we used Cantelli’s inequality (see paragraph 5, Eq. 1) to decide whether a metabolite is significantly altered (elevate or reduced) in our patient in comparison with the control group. That said, the only statistically significant alteration in our patient is the level of Cho/Cr in ACC which is 5.4 standard deviations above the mean of the control group. According to Cantelli’s inequality, this means that the probability for a healthy individual to have a value greater or equal to this is at most 0.033. In other words, the percentage of healthy individuals with a value of Cho/Cr in ACC greater or equal to the one found in our patient is at most 3.3%.
So, this patient has an increase in the level of choline/creatine in the ACC, in agreement with what recently found in ME/CFS patients (Mueller C. et al. 2019). A few weeks before undergoing the MRS, our patient performed a set of cognitive testing and showed a reduced processing speed in the Stroop task, further suggesting a disfunction of the ACC and in agreement with what consistently found in ME/CFS patients (Cvejic E. et al. 2016), (Shan Z.Y. et al. 2018), (Robinson L.J. et al. 2019). The exact localization of the volume of the ACC in which the metabolites have been measured is reported in Figure 3.
5. Cantelli’s inequality
To assess the statistical significance of the experimental data in Table 1 we have used Cantelli’s inequality, also known as one-tailed Chebyshev’s inequality. Given the random variable X whose distribution has mean E[X] and variance Var[X], then Cantelli’s inequality states that:
for any η>0. The importance of these two inequalities is that they are true whatever the distribution is. In the case of our patient’s MRS data, we only knew mean values and standard deviations (which is the square root of variance) of the distributions of the metabolic values of the control group. So one way to assess significance was to use this inequality (the other way would be to use the less precise Chebyshev’s inequality). To prove Eq. 1 and Eq. 2 we have first to prove Markov’s inequality, which states that
for any a>0. In order to prove that, consider that for the probability on the left of the inequality we can write
At the same time, the expectation (or mean) of the distribution can be written
Thus we have
and Markov’s inequality is proved. Let’s now come back to the proof of Cantelli’s inequality. If we consider the random variable Y = X – E[X] we have that P(Y≥η) = P(Y+t≥η+t) and assuming that η+t > 0 we have
That said, Markov’s inequality gives
For the expectation on the right we have
and knowing that E[Y²] = Var[X] and that E[Y] = 0, we can write
The function on the right of the inequality is represented in Figure 4. It is easy to recognize that it assumes its lower value for t = Var(X)/η and this proves Eq. 1. The other inequality (Eq. 2) can be proved in the same way, considering the random variable Z = E[X] – X.