What Can fMRI Teach Us About Gender & Body Image?: A Focus on Owens et al.

Functional Magnetic Resonance Imaging (fMRI) has gained a lot of traction in the field of cognitive neuroscience. And that’s all well and good, but cognitive neuroscience is not something the average person regularly thinks about. Let’s look at some real-world applications of fMRI to my favourite subject area – body image and see what fMRI can add to this complex research area.

fMRI for Fitspos

If you open your Instagram, and your echo chamber is anything like mine, you may be faced with ads for questionable mental health companies that use fMRI scans like fat loss coaches use transformation photos – e.g. your brain normally (weak, limp, lifeless à là Chezza Cole in the L’Oreal ads of years ago), and your brain using our ambiguous app (lighting up like Clerys at Christmas, never seen such activity).

Stick an fMRI scan in your marketing and you already have your product seeming fancier and evidence-based. Doesn’t matter that no one in your marketing team knows what an MRI scan is supposed to look like, and your “before” scan is of the scalp and not brain parenchyma, because the general public don’t know either. Score!

This rant aside, fMRI is also quoted by many as unequivocal “proof” in psychology studies that X causes Y. This is more often than not, completely untrue, and often acknowledged by the researchers in these very studies. But, how can you arm yourself against these charlatans? Why, you can use their kryptonite – scientific evidence.

fMRI for Dummies: An Overview

Now, before we call everyone idiots, we must first know what we are talking about. Then, we can call everyone idiots. I work as a clinical MRI radiographer (read: I actually take MRI scans and don’t just slap them on my content to seem evidence-based), and there are a number of considerations from clinical practice we can apply to how we appraise the body image literature.

Most of the information in this section comes from the excellent overview article from Glover (2012) [1].

Brain Metabolism & Imaging

Functional MRI is used to demonstrate regional, time varying changes in brain metabolism. Whilst the brain makes up a tiny bit of total body mass, she loves to be the drama – accounting for over 20% of total oxygen metabolism [2]. She’s fuelled primarily by glucose, and different parts of the brain (neurotransmitter etc) use varying amounts of energy depending on what parts are activated and what you’re doing [3].

To activate an area of the brain, an increase in neuronal signalling requires a local increase in energy requirement. Consequently, we see an upregulated cerebral metabolic oxygen rate in the activated area. To supply this demand for local oxygen, we see a local increase in cerebral blood flow (CBF) [1]. We see a haemodynamic response to this – a build-up of deoxygenated haemoglobin (Hb) and then subsequent increase of oxygenated Hb to fill this gap.

So, there are two consequences of increased neural activity detectable by MRI:

  1. Local cerebral blood flow (CBF)
  2. Changes in oxygenation concentration (Blood Oxygen Level Dependent [BOLD].

BOLD Imaging

We typically see BOLD sequences used in fMRI studies. CBF sequences require the administration of contrast agent (expensive, need to get IV access, subjects may experience allergic reactions to contrast), are more likely to be degraded by patient motion and have longer scan times compared to BOLD sequences [4].

Enter BOLD imaging. BOLD imaging records the change in magnetic field surrounding red blood cells depending on the oxygenation status of haemoglobin [1]. Oxygenated Hb is diamagnetic (and therefore indistinguishable from brain tissue). Conversely, fully deoxygenated Hb is paramagnetic and has roughly four unpaired electrons [5]. As a result, we see local gradients in magnetic fields in the brain, that we can pick up on T2 imaging.

As with any MRI scan, nothing will degrade image quality like a patient flapping their head like a fish, so limiting patient motion is paramount. Researchers also advise limiting the physiological processes of breathing and cardiovascular functions [1] (tell me you’ve never worked in clinical without actually telling me – just tell the patient to stop all major life-giving processes for the purposes of research).

So, with a brief whistlestop tour through fMRI completed, let’s move on to the literature.

Owens et al. (2011): Sex Differences in Body Image Using fMRI

This is a truly excellent paper, that makes a huge deal of effort to cover all possible confounding variables[6]. The authors avoid jumping to conclusions, which is very easily done in fMRI studies. As the good saying goes, fMRI activation does not imply causality. So, let’s get into it.

Introduction

As the authors state, body image is relatively under-researched area in cognitive neuroscience. Most research tends to focus on the behavioural causes of body image disturbances, but very little studies have examined what happens at a brain functionality level.

Body image is hypothesised to play a higher role in self-worth for women than men [7[, and men typically deem themselves to be better-looking and have higher regard for their bodies [8]. Obviously, from the audacity of straight men on dating apps, cishet women can attest to this. All joking aside however, male body image concerns are no less valid, and can also severely negatively impact quality of life. It might not impact their life as much as the average woman, but it’s still a major downbuzz.

How self-awareness/self-reflection manifests in terms of brain activation has been studied in several fMRI papers. In these studies, it is primarily the medial aspect of the prefrontal cortex (mPFC) (espesh the anterior portion) that is activated during self-reflective thought [9].

This paper hypothesised there would be a gender difference in mPFC activation, as the gals are typically more self-critical of their body, and more sensitive to social judgement (see: Andrew Tate videos – we women are just objects) [10,11].

Seems legit, right? But does it hold true? Let’s dig into the methodology and find out.

Methodology

Participants

We had a lowly 19 participants (10 females, 9 male), who were right-handed, native English speakers and had at least one year of college education. Notably, the researchers screened for known psychological, neurological or eating disorders. This is really important, but I also wonder whether researchers should have screened for known respiratory or haematological disorders that affect oxygen or Hb concentration?

We can’t be too critical of the sample size, because fMRI studies are notoriously expensive to run, and many participants are eliminated due to claustrophobia or other no-goes for scanning in a giant magnetic field. However, I do have my qualms about the age range. It is well-documented in literature that body image concerns peak typically peak in adulthood around collegiate years and levels off as we age [12]. Perhaps a narrower age-range would have been more appropriate.

Procedure

Researchers used computer generated images of both “thin” and “fat” images (they also have a lovely little disclaimer where they note these are intended as labels for experimental conditions and not judgements on the body shape – cancel culture extends to neuroscience research too bbz). Researchers also ensured to use different skin texture, skin tones and hair colour to keep participants interested (and also weed out the racists).  Participants were placed in the MRI scanner and viewed these images on an angled mirror whilst in the scanner.

Why the angled mirror you say? Well, to pick up signal from the brain tissue on an MRI scan, radiofrequency coils are placed across the patients face using a mask that Hannibal Lector himself would be jealous of (seriously, have a look ). Participants were shown Thin, Fat and Control images (holy trinity of fMRI) for 2 seconds per image, and asked to imagine someone saying that their body resembled the picture displayed. Participants only saw images from their own gender. The control group were only shown the images and given no instruction.

Image Acquisition

Care was taken by the researchers to limit other anatomical variants influencing BOLD scanning, by asking a neuroradiologist to look at the sequences (T2 and stealth [basically a super high-res brain scan]). This is an extremely important step that is normally unaccounted for in other fMRI studies.

Results

The results of this study are hella interesting.

Men showed no higher levels of activation than women, and no areas were activated in men than weren’t activated in women. The female brain was a lot more active in these studies, and several areas were activated in women that weren’t in men:

  • Bilateral middle frontal gyri
  • Bilateral precentral gyri
  • Right vermis of cerebellum
  • Right calcarine cortex
  • Right dorsal percuneus

When comparing “fat” bodies to their own, females had higher peaks of activation than men, most notable in the bilateral prefrontal cortex and our old pal the left mPFC. Much to think about.

Discussion

Interestingly, females only engaged in this self-reflective activation pathway in a significant way only when viewing “fat” images, and males didn’t engage in this self-reflection pathway at all!

As noted by the researchers, it is possible that nobody followed the instructions until it got to the “Fat” images, but this is unlikely. More likely, the experimental conditions didn’t generate enough deep and meaningful self-reflection. This is a really important distinction, and very well pointed out by the researchers. Absence of evidence is not evidence of absence. We know from existing research that men do engage in self-reflection about their bodies and also suffer body image concerns [13,14]. We also know that the male “ideal” is not necessarily thin, but rather muscular [15]. Perhaps if the researchers had shown muscular images to men they would have elicited different responses, or maybe men just don’t care that much about comparing their body to others.

Women in this study only appeared “bothered” when their appearance was compared to the “Fat” image, but again these women were all of normal BMI. It is unlikely that being compared to the Thin image would have affected them, if they were relatively close to this image anyway. Given the evidence of weight stigma amongst the general population, this is not surprising [16], however whether the same results would be obtained in those of elevated BMI remains to be seen.

We also must consider the weaknesses of BOLD imaging for looking at this area of the brain. Owing to the 9ppm difference in magnetic susceptibility at junctions between air and brain parenchyma, there exists the potential for signal dropout or spatial distortion in frontal orbital and lateral parietal regions [1]. As a result, sometimes there is signal nullification from the ventral, temporal and PFC regions [1]. Obviously, this is a big yikes for this study, and it is possible this equipment error gave a false negative reading for some participants. A larger sample size would have helped eliminate this having a major effect on results.

Overall, we cannot generalise a lot of the results of this study. The researchers conclude that a typical, non-eating disordered woman in the US shows a relatively high-level of concern for body shape. Now, anyone could have told you that without an entire fMRI study, but it’s interesting to see it validated at a cognitive level. Overall, I think this study leaves us with more questions than answers (as all good studies do), but it’s extremely interesting to see the direction of this novel research field. 11/10 also for the researchers for not overreporting and generalising the results and findings for the sake of gaining traction. What’s rare is wonderful.

References

  1. Glover, GH (2012) Overview of Functional Magnetic Resonance Imaging. Neurosurg Clin N Am 22(2), pp. 133-139.
  2. Hyder F, Rothman DL, Bennett MR (2013) Cortical energy demands of signalling and nonsignaling components in brain are conserved across mammalian species and activity levels. Proc Nat Acad Sci USA 110, pp. 3549-3554.
  3. Watts ME, Pocock R, Claudianos C (2018) Brain Energy and Oxygen Metabolism: Emerging Role in Normal Function and Disease. Front Mol Neurosci 11.
  4. Buxton R, Frank L (1997) A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J Cereb Blood Flow Metabl 17(64).
  5. Bren KL, Eisenberg R, Gray HB (2015) Discovery of the magnetic behavior of hemoglobin: A beginning of bioinorganic chemistry. Proc Nat Acad Sci USA 112(43), pp. 13123-13127.
  6. Owens TE, Allen MD, Spangler DL (2010) An fMRI study of self-reflection about body image: Sex differences. Pers Individ Diff 48(7), pp. 849-854.
  7. Grossbard JR, Lee CM, Neighbors C et al. (2008) Body Image Concerns and Contingent Self-Esteem in Male and Female College Students. Sex Roles 60(3), pp. 198-207.
  8. Voges MM, Giabbiconi CM, Schöne B et al. (2019) Gender Differences in Body Evaluation: Do Men Show More Self-Serving Double Standards Than Women? Front Psychol 10(544).
  9. Mitchell JP, Mahzarin RB, Macrae CN (2005) The link between social cognition and self-referential thought in the Medial Prefrontal Cortex. J Cog Neurosci 17(8), pp. 1306-1315.
  10.  Hagger MS, Stevenson A (2010) Social physique anxiety and physical self-esteem: Gender and age effects. Psychol Health 25(1), pp. 89-110.
  11.  He J, Sun S, Zickgraf HF et al. (2020) Meta-analysis of gender differences in body appreciation. Body Image 33, pp. 90-100.
  12.  Tiggmann M (2004) Body image across the adult life span: stability and change. Body Image 1(1), pp. 29-41.
  13.  Burlew LD, Shurts WM (2013) Men and Body Image: Current Issues and Counseling Implications. J Couns Dev 91(4), pp. 428-435.
  14.  Barlett CP, Vowels CL, Saucier DA (2008) Meta-analyses of the effects of media images on men’s body image concerns. J Soc Clin Psychol 27(3), pp. 279-310.
  15.  Edwards C, Tod D, Molnar G (2014) Systematic review of the drive for muscularity research area. Int Rev Sport Ex Psychol 7(1), pp. 18-41.
  16.  Fruh SM, Nadglowski J, Hall HR et al. (2016) Obesity Stigma and Bias. J Nurse Pract 12(7), pp. 425-432.

Published by Michelle Carroll

I am an online coach (MSc Sports & Exercise Nutrition, EQF Level 4 Personal Trainer, PN Level 1) and radiographer (BSc). I believe in empowering others to make better choices for their health through education. I think that the fitness industry has created a disconnect between best practices and “evidence-based” practices. I hope by chronicling my experience as a healthcare professional and my education as a fitness professional I can assist others on the path to bettering themselves.

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