No consistent associations of well-being & brain correlates

A systematic review of the neural correlates of well-being reveals no consistent associations. Lianne P.de Vries, Margot P. van de Weijer, Meike Bartels. Neuroscience & Biobehavioral Reviews, January 5 2023, 105036. https://doi.org/10.1016/j.neubiorev.2023.105036

Highlights

• We performed a systematic review on the neural correlates of well-being.

• A wide range of brain regions was involved in well-being in the different studies.

• More left than right brain activation might be related to higher well-being.

• Replication of associations across studies was scarce, in strength and direction.

• Well-powered brain-wide association studies are needed to study neural correlates of well-being.

Abstract: Findings from behavioral and genetic studies indicate a potential role for the involvement of brain structures and brain functioning in well-being. We performed a systematic review on the association between brain structures or brain functioning and well-being, including 56 studies. The 11 electroencephalography (EEG) studies suggest a larger alpha asymmetry (more left than right brain activation) to be related to higher well-being. The 18 Magnetic Resonance Imaging (MRI) studies, 26 resting-state functional MRI studies and two functional near-infrared spectroscopy (fNIRS) studies identified a wide range of brain regions involved in well-being, but replication across studies was scarce, both in direction and strength of the associations. The inconsistency could result from small sample sizes of most studies and a possible wide-spread network of brain regions with small effects involved in well-being. Future directions include well-powered brain-wide association studies and innovative methods to more reliably measure brain activity in daily life.

Keywords: well-beingbrainneural correlatesbrain-wide associations

Discussion

To understand observed differences in well-being between people in more detail, it is essential to identify the biological and neural factors related to well-being. The goal of this systematic review was to bring together the available literature on well-being and brain structures and brain functioning. We first summarize and discuss the findings and based on the results, we propose directions for future research.

Brain structure

The systematic review of the brain areas where grey matter volume was associated with well-being revealed large inconsistencies. While the grey matter volumes of the (medial) PFC, ACC, the precuneus, hippocampus, and brainstem were related to well-being in multiple studies, for all these areas, there was inconsistency in the direction of the associations. Whereas in some studies smaller grey matter volume of the PFC, ACC, precuneus, hippocampus, or brainstem was related to higher levels of well-being, in other studies a larger grey matter volume of these areas was related to higher well-being.

These discrepancies might be the result of the small sample sizes (ranging from 15 to 724) in most structural MRI studies. Especially if the effect sizes are small, a large sample size is needed to have enough power to detect associations (Marek et al., 2022). Only two studies included more than 700 participants and only five studies more than 200 participants, indicating the need for larger sample sizes before we are able to reliably test for an association between the structure and volume of brain areas and well-being.

More recent studies went beyond brain volume and reported for example that well-being was associated with higher orientation dispersion, i.e., brain development and more dendritic complexity (Cabeen et al., 2021). This suggests that other, more detailed, features of brain structures might be related to well-being. The development and application of higher resolution imaging sequences allows us to, for example, investigate the cortical microstructure and complexity of brain structures in relation to phenotypes in more detail (Zhang et al., 2012).

Brain functioning

EEG

Three studies related well-being to the profiles of resting EEG power. Resting EEG power measures spontaneous brain activity, which can be divided into different frequencies. In single studies, the slower frequency signals, theta and alpha, were negatively related to well-being, whereas delta power, a faster brain oscillation, was positively related to well-being. A recent and larger study reported only a relation between the interaction between alpha, beta, and delta power and well-being, whereas the relation between the power of the single frequency bands and well-being was not significant. This could indicate that the relative amplitude of different frequency bands is important for well-being instead of the absolute power of single frequency bands. However, replication in studies with larger sample sizes is needed to draw a conclusion on the association between well-being and the different frequency bands in the brain.

(Frontal) alpha asymmetry was examined in more studies and positively associated with a measure of well-being in seven of the nine studies, whereas the other studies did not report a significant effect. Additionally, the small meta-analysis of alpha asymmetry and well-being indicated a positive relation (r=.19), but also suggested a possible publication bias. If replicated, greater left than right frontal activation is associated with well-being. This is in line with theory that alpha asymmetry is related to approach motivation and therefore the experience of positive feelings (Angus and Harmon-Jones, 2016). The opposite asymmetry, greater right-frontal activity, is assumed to be involved in withdrawal motivation, and some studies have found a relation with depression (but see (Olbrich and Arns, 2013) for a discussion about the unsuccessful replications). Noteworthy is that most studies on alpha asymmetry included measures of positive affect and/or life satisfaction, whereas the psychological well-being scale was only included in one study. More research on the moderating effect of the well-being scale used is therefore needed in future studies.

fMRI/fNIRS

The results of the included studies on the associations of well-being and brain activity and functional connectivity across brain regions/networks are very heterogenous. As can be seen in Fig. 4, many brain regions across the whole brain were associated with well-being in the different studies. However, replication of the associations across multiple studies was mostly absent. Furthermore, if a brain region was associated with well-being in multiple studies, the direction of the association was inconsistent. For example, in the fMRI studies that associated the activity or functional connectivity of the PFC, orbitofrontal cortex or precuneus to well-being (respectively 14, 5 and 4 studies in total, see Supplementary Table S1), for all brain areas half of the associations with well-being were negative, whereas the other half were positive. The most consistent finding in fMRI studies that investigated the connectivity between brain areas in relation to well-being is that a stronger functional connectivity within the default mode network (DMN) is related to lower well-being. The DMN consists of brain regions in the ventral and dorsal medial PFC, and the PCC. This network of cooperating brain regions is active when a person is in resting state or when not focused on the outside world (Raichle, 2015). The DMN has been involved in daydreaming and mind wandering. The positive correlation between connectivity in the DMN and well-being suggests that the activity of several brain areas related to thinking spontaneously is connected stronger in happier people.

Interpretation

The results of the reviewed studies on the neural correlates of well-being are very heterogeneous. Across all studies and methods, brain areas most often associated with well-being were the PFC, ACC, insula, default mode network, orbitofrontal cortex, visual networks, precuneus, and somatosensory networks (see supplementary Table S1). The association between well-being and the structure and/or functioning of the PFC, ACC, insula, and precuneus was reported in studies using different techniques (e.g., fMRI, MRI and EEG). However, the direction and strength of these associations differed to a great extent and many other brain areas have been identified in single studies, but not replicated in other studies. We replicated part of the conclusions of (Machado and Cantilino, 2016) and (King, 2019) about the relations between well-being and various brain areas. However, the involvement of networks, like the DMN, visual, and somatosensory networks emerged mostly in more recent studies included in the current review.

A first explanation of the inconsistent results is the large differences in brain imaging methods and analysis techniques. Different brain functioning imaging methods might lead to different results, e.g., EEG and fMRI both assess brain functioning, but are completely different techniques with different temporal and spatial characteristics. The brain areas covered with these techniques are at the surface with EEG assessment, but include the whole brain with fMRI assessments. Furthermore, when using the same imaging technique, the analysis techniques differed a lot. For example, the resting state fMRI studies either applied fALFF, functional connectivity analyses, or regional homogeneity (ReHo) to assess the regional neural activity or connectivity between brain areas. Lastly, although it has been shown that the function of a brain area and its structure are related (Sui et al., 2014, Toosy et al., 2004), the findings of MRI and fMRI are not completely comparable. These differences in methods and analyses add a first difficulty in comparing the results of the studies and harmonization is needed in future studies.

In addition, a limitation in the field of imaging is the small sample sizes, mainly due to the costs, leading to low power of the study and low reproducibility of results (Button et al., 2013). As discussed in more detail in the section below, the small samples in combination with potential small effects of the involvement of single brain regions in well-being can explain the failure to replicate findings.

Brain-wide association studies

Similar to the inconsistent results of our review, meta-analyses and reviews on the association of brain regions with other behaviors or complex traits reported largely inconsistent results and a wide range of potentially associated brain regions as well. For example, in a meta-analysis of resting state fMRI studies of attention deficit hyperactivity disorder (ADHD), (Cortese et al., 2021) did not find any convergence of connectivity patterns across studies. The same replication problem was shown in a meta-analysis of brain regions in relation to depression ((Müller et al., 2017). Across 99 neuroimaging experiments there were large inconsistencies in results.

In light of these wide-spread replication problems across behaviors and traits, and related to the small sample sizes used in neuroimaging research, an explanation for the diversity in results could be the small effects of the involvement of single brain regions in well-being and other human behaviors and traits. Similar to findings of genome-wide association studies (GWAS) that indicate that there are no “well-being genes” with a large effect on well-being, but many genes with small effects (Baselmans et al., 2019a, Okbay et al., 2016), it is unlikely that there is a “well-being brain region” or a few brain areas that have large effects on well-being. In contrast, a wide network of brain areas that all have small effects on well-being is to be expected. Using GWAS as example, brain-wide association studies (BWAS) have been proposed to reliably and without a priori hypotheses investigate the involvement of the brain in human behavior and traits (Gong et al., 2018, Marek et al., 2022). BWAS with sufficiently large sample sizes, i.e., samples with thousands of individuals, are needed improve reproducibility and the reliability of the brain–behavioral phenotype associations. Following the example of the genetic community, neuroimaging research should start large-scale collaborations to create the needed large sample sizes that are mostly missing in brain-wide association analyses (Poldrack et al., 2017).

An example of an already worldwide collaborative network is the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium, including 100k+ participants and 45 countries that focuses on disorders (Thompson et al., 2014). For Major Depressive Disorder (MDD), this consortium has already led to reproducible results, including a smaller hippocampal volume in MDD participants (n= 1728) compared with healthy controls (n=7199) and lower cortical thickness in the cingulate cortex, bilateral medial OFC and insula (Schmaal et al., 2020). Similarly, for other disorders like ADHD, bipolar disorder, and schizophrenia, robust brain correlates have been found in the large ENIGMA samples. In a recent application of BWAS to cognitive ability and psychopathology with more than 10 thousand participants, (Marek et al., 2022) showed a widely distributed circuitry of associations. These patterns indicate the involvement of many brain areas not detected in studies with the typical smaller sample sizes. This approach of well-powered brain-wide association studies is needed to investigate the brain-well-being associations as well.

Furthermore, following the successful polygenic scores in the field of genetics (Wray et al., 2007), recently, the use of polyneuro scores has been proposed (Mooney et al., 2021). Polyneuro scores are summary scores of the cumulative effect of brain-wide measures that capture effects across widely distributed brain systems and regions that are involved in different human traits (Mooney et al., 2021). Applied to ADHD, (Mooney et al., 2021) showed that such summary scores of functional connectivity across the brain have increased predictive power for ADHD symptoms. The scores explained around 8 times more variation than the variation explained by the most significant connection in the brain. However, the explained variance is still small, ~4% of the variation in symptoms is explained by the polyneuro scores.

Returning to the results of our review on well-being, this idea of brain wide associations for well-being is supported by the wide range of brain areas potentially associated with well-being. Furthermore, some specific findings of the more recent studies are in line with brain wide associations. For example, the association of neural diversity or variability in functional connectivity and higher well-being suggests that a higher complexity or more connectivity, i.e., more collaboration between brain areas leads to higher well-being (Cabeen et al., 2021). However, future brain-wide association studies are needed to support this idea for well-being and to be able to create polyneuro scores for well-being. To create the large sample sizes that are needed for brain-wide association studies, existing brain consortia could either include a well-being questionnaire, or brain and well-being researcher could combine their efforts in large consortia.

Innovative methods and analyses

The rapid development in the methods and techniques that measure brain structure and functioning at smaller temporal and spatial resolution give rise to new opportunities as well. For example, recent studies on the microstructure of the brain enables investigations of the relation between well-being and more detailed aspects of brain structures and functioning (Cabeen et al., 2021). However, also with such research to microstructures, a brain-wide approach should be applied to prevent non-replicable results.

Another direction for future research is the improvement of ecological validity in neuroscience research. At the moment, most brain imaging research is conducted in MRI scanners or in a controlled setting in the lab. This raises the question of the ecological validity, i.e., the generalizability to daily life or to which extent the results predict behaviour outside the testing environment (Matusz et al., 2019). Various solutions have been thought of to enhance the ecological validity in neuroscience. As an example of using more naturalistic stimuli and tasks, (Reggente et al., 2018) reviewed the use of virtual reality in fMRI research to memory. The results suggest the neural correlates associated with virtual reality (VR) images are more likely to generalize to real-world behaviors. This might help to find the relevant neural correlates for daily life, without introducing more variation because of uncontrollable external influences that exist in daily life. Another way to enhance ecological validity is by using portable devices, such as portable EEG and fNIRS caps, to measure neural correlates in real life and daily life (Balardin et al., 2017). Recently a portable MEG helmet has been developed as well (Boto et al., 2018). These mobile methods lead to many more possibilities to measure and understand brain activity and functioning in real-life settings and scenarios. EEG has already been recorded during walking, cycling, sports, and even skateboarding (Ladouce et al., 2019, Park et al., 2015, Robles et al., 2021, Scanlon et al., 2019). Furthermore, people from all ages, including babies and children can participate more easily, as movement is less of an issue with the portable devices.

Related to innovations in the methods for neuroimaging, there are also rapid developments in the approaches to analyse (big) data. Using the developments in the artificial intelligence and machine learning fields, patterns can be detected in imaging data that we would not predict or expect. These approaches enable us to focus more on data-driven research instead of hypothesis driven research (Scheel et al., 2020). Using a data driving approach, (Liu et al., 2020) analysed fMRI data from major depressive patients and healthy controls. Their models could distinguish between the patients and healthy controls (accuracy=0.77) and the authors reported several brain-wide features that differed between patients and controls.

Limitations

Because of the inconsistency in study design, measures, neuroimaging analyses, and reported results, a meta-analysis on the association of brain areas and well-being was mostly not possible. Conclusions should therefore be drawn with caution. Although more studies are being performed currently, future research should be harmonised to allow meta-analyses and to reach the desired sample size of thousands of participants. Whereas we did perform a small meta-analysis on the association between frontal asymmetry and well-being, the estimate was based on only a few studies and the analysis pointed towards a potential publication bias. Therefore, these results should be interpreted with caution as well.

Furthermore, it is difficult to compare earlier neuroimaging studies to the more recent studies, because of the rapid technological advancements and changing guidelines and methods in the neuroscience field. In earlier research, regions of interest (ROIs) were decided up front, whereas nowadays a voxel-wise whole brain analysis is preferred. However, as mentioned before, most brain-wide studies did not include sufficiently large sample sizes. The often small sample sizes in neuroscience research are a limitation for interpreting the results reliably, since this increases the variability and reduces statistical power. As proposed, future studies should perform power calculations before running imaging studies and start collaborations to reach the required sample sizes for brain-wide association studies (Marek et al., 2022, Szucs and Ioannidis, 2020).