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The Neural Correlates of Emotion

  • 11 Pages
  • Published On: 27-11-2023

Various studies have addressed the importance of both the emotion and cognition processes. The human brain is a complex system of various regions that can make sense of information from various sources. The brain also changes following the situational needs for the well-functioning of the body. Emotion cognition interactions of the body are essential for the normal function of the body. This is an example of the integrative processing of the brain. Lately, the neuronal correlations that examine the effects of emotion on cognition need to be explored, thus increasing the need for various studies on the topic. In the previous studies, the involvement of emotion and cognition related brain structures have been established. These structures have been found to include the parietal and the prefrontal cortices and the limbic regions. In this study, a whole-brain event related functional magnetic resonance imaging is employed during an affective number Stroop task. This is targeted at replicating the previous studies through an adaptation of already existing 40 healthy middle-aged adults. The Stroop task indicated cognitive control and allowed quantifying the interference due to varied cognitive loads. Through emotional primes before the Stroop task performance, an emotional variation has been added. Negative primes not only delay but also interrupted the cognitive processing of the brain. This study has used fMRI to assess the neural correlates of emotion. The paper has also detailed the analysis process that will be used for the research study.

Literature review

Distinct models of emotion discuss the brain's strategic regions' role in emotion identification, response, and regulation. The emotional brain consists of the cortical, subcortical, and limbic structures (Taylor & Liberzon, 2007). In individuals with attention, hyperactivity disorder, emotion regulation, and reactivity are frequently impaired (Beauregard et al., 2017). For the healthy functioning within the daily lives of individuals, adequate handling of emotional information is inevitable. The specific processing and regulation of emotion impact an individual's cognition, behavior, and overall well-being (Beauregard et al., 2017). Emotion processing not only impacts cognitive control but also controls the effects of specific emotions. The delicate balance of the emotion and cognition networks results in the right functioning (Taylor & Liberzon, 2007). On the contrary, the inability to control the process or regulate emotions correctly leads to different mental health disorders. These disorders are inclusive of disruptive behavior disorders and attention hyperactivity disorder apart from psychosis (Beauregard et al., 2017)

Emotional stimuli have been noted to impact cognitive processing, either positively or negatively. For instance, working memory performances have been noted to be disrupted by presenting the emotional stimuli (Beauregard et al., 2017). Task accuracy and reaction times during Stroop task performances have also been reduced by the presence of an emotional stimulus (Miller et al., 2018). This reflects the cognitive control mechanisms of the brain of an individual. Auditory induced emotions or visually represented emotions have been noted to impact an individual's cognition and, consequently result in more accuracy and shorter reaction times in tasks that include conflict processing, decision making, or even visual attention (Beauregard et al., 2017). Various factors influence the interaction of cognitive and emotional processes. Studies have noted that these are inclusive of cognitive load, the level of threat an individual faces, the physical stimulus properties, the position of emotional distractors, individual differences, and lastly, the availability of conflict resolving brain resources (Beauregard et al., 2017). Distinct brain regions are responsible for cognitive and emotional processing. There is an interaction that researchers have noted between within the bilateral anterior insula, somatosensory cortices, and the front parietal regions. The left and the right lateral prefrontal cortex are the sites of emotional and cognition association (Beauregard et al., 2017). Increased cognitive demand leads to decreased neural activation in response to an emotional stimulus. This is usually within the prefrontal cortex, the amygdala, and the insular cortex (Miller et al., 2018). Emotional primes significantly impact cognitive performances and increase cognitive demand, leading to decreased neuronal activation in emotions related to brain regions (Miller et al., 2018). Emotion and cognition are significantly related to each other, as indicated by the previous studies that have documented shared neural networks that take part in both emotion and cognition (Beauregard et al., 2017). For healthy functioning, emotion processing, cognitive control, and their interactions are incredibly crucial. Lack of them leads to various psychiatric disorders like disruptive behavior disorder.

Emotion regulation has been termed as the process that involves the initiation, maintenance, and modification of the occurrence, intensity, and duration of feelings (Beauregard et al., 2017). Regulating emotions has been noted to be useful in modulating emotional responses (Miller et al., 2018). The impact of regulation on emotions is comparatively modest and linked with cognitive loss besides the physiological resource depletion. Understanding the neural basis of emotional regulation and cognition is crucial to the relations between emotions and cognition. This study aims to investigate the neural correlates of emotions in middle-aged adults.


The participants for this study will include forty health and English speaking volunteers within the UK. The participants' mean age will be 21.74 years with the age ranges of 19-24 four years. The participants will include 20 males and 20 females. The participants will be individuals with no record of psychological or neurological history. The participants will participate in one testing session that would include psychometric testing, one functional neuroimaging task, and T1-weighted structural image acquisition. All the participants will be right-handed, with normal or corrected to normal vision. All the participants will be provided with a written consent as approved by the local and national ethics and regulation committee. The participants will complete a battery of standardized tests that will include verbal and non-verbal IQ, their then moods, behavioral and emotional functioning, psychopathic traits, and handedness.


fMRI scanner

fMRI Procedure

The fMRI will be used for this study. The fMRI uses a magnetic field to take pictures of the brain in action. The technique is safe and pretty comfortable for human beings. However, the methodology is considered unsafe for individuals with shrapnel or metal or electronic implants in the bodies (Miller et al., 2018). Besides, those who are pregnant and with a history of trauma are also advised not to use the fMRI (Dolcos et al., 2011). Individuals with significant medical, neurological, and physiological disorders cannot undergo the functional magnetic resonance imaging procedure. Individuals in continuous use of sleeping pills such as aspirin amongst other agents that affect brain function such as the decongestants and the antihistamines are advised to avoid the fMRI (Raschle et al., 2017).

Before scanning, the participants will be asked to complete a detailed health history questionnaire to validate their viability for the procedure. The participants will also provide specific information regarding their health, amongst other information (Dolcos et al., 2011). Before entering the scanner, the participants will be needed to remove all metal items from their body since fMRI uses a strong magnetic field.

The session will include event-related functional neuroimaging alongside the performance of an emotional number Stroop task. T1-weighted structural images will be required for each of the study participants (Raschle et al., 2017). Based on the research design, the Stroop task will be modified and adopted for the study. Each trial will start with an emotional prime of the neutral and negative valence. Participants will be offered an array of 1-4 digits and will be asked to indicate the number of items presented (Dolcos et al., 2020). Emotional stimuli will be adapted from the developmental affective photosystem that uses the adults' international affective picture system. DAPS will be implemented since the task will be designed to be employed in young adults. A list of the images used will be offered in the supplemental information. Before starting the experiment, an optimal stochastic trial order will be determined using the Optseq2 tool (Dolcos et al., 2011). This schedules event for the rapid presentation event-related fMRI experiments automatically. A total of 300 Stroop trials will be administered during the study. The scan time will be about 12 seconds, and the complete experiment will be performed in two runs. The participants will be asked to perform valence ratings of the scanner's images using the Likert scale after the neuroimaging session. The Likert scale will range from -2 to 2. In the scale, -2 will represent the very negative valance, 0 will represent the neutral valance, while two will be for the stimulus's high attractiveness.

On the image acquisition and analysis, the whole brain blood-oxygen-level-dependent (BOLD) fMRI data and T1-weighted mprage images will be acquired on a Siemens 3T MR imaging system and a 20-channel phased-array radiofrequency head coil (Dolcos et al., 2011). A high resolution T1-weighted structural image will be gotten using the specifications of the TR = 1,900.0 ms; TE = 3.42 ms; FOV = 256; image matrix = 256 × 256; voxel size = 1 mm. besides, the T1-weighted mprage structural neuroimaging data will be used for the co-registration to determine the total intracranial volume (TIV) through arithmetic calculation (Dolcos et al., 2011). The fMRI data will be analyzed using the SPM8. Contrast images will be created to explore the main impacts of emotion and cognition and the influence of emotion and cognition along with an upsurge in the cognitive demand based on a t-test (Raschle et al., 2017). Mean peak activation scores will be extracted to characterize the effects of cognitive load on emotion's neural basis. This will be based on the FEW-corrected findings from the two main contrasts that will be targeting the emotion and the cognition networks of the brain (Raschle et al., 2017). The marsbar toolbox will be used to extract the signal changes at the local peak activation scores for the bilateral amygdala, the right insula, and the bilateral precentral gyrus. These will further be assessed using the t-test.

Justification of methodology

The emotional number Stroop task will be used for the study based on the rationale that it is specifically developed for use in the MR environment (Mejia et al., 2020). Besides, it has been used previously in neuroimaging research studies and was termed to be successful. fMRI was chosen as a methodology of the study based on the fact that it does not use radiations like X-rays, the computed tomography (CT), or positron emission tomography (PET) scans (Raschle et al., 2017). The fMRI has virtually no risk in instances that it is done correctly. The methodology can also evaluate brain function safely, noninvasively, and virtually (Miller et al., 2018). It is easy to use. Besides, another vantage of the fMRI is that the images it produces are of exceptionally high resolution (Raschle et al., 2017). Relative to the traditional questionnaire methods of psychological evaluation, fMRI has been far more objective.

Justification of the study

This study is useful and essential in understanding the association between the emotion and cognition processes as they are vital to an individual's healthy living.

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Ethical considerations

The primary ethical considerations are those related to the recruitment of vulnerable groups; ethics states that only those viable for the study should be recruited (Ulmer et al., 2020). As a result, the study will set inclusion and exclusion criteria to ensure only those non-vulnerable are included in the study. Besides, the participants should have informed consent of what the study is about and how the study's data will be used (Ulmer et al., 2020). The study will also ensure the participants' safety by addressing all the potential risks that may be countered. The confidentiality and privacy of the participants will be ensured at all costs. The researcher will ensure that the participants are not harmed in any way (Ulmer et al., 2020). The study will also adhere to the law and regulations that govern functional magnetic resonance imaging (Ulmer et al., 2020).


Beauregard, M., Lévesque, J., & Bourgouin, P. (2017). Neural correlates of conscious self-regulation of emotion. Journal of Neuroscience, 21(18), RC165-RC165.

Dolcos, F., Iordan, A. D., & Dolcos, S. (2011). Neural correlates of emotion–cognition interactions: A review of evidence from brain imaging investigations. Journal of Cognitive Psychology, 23(6), 669-694.

Dolcos, F., Katsumi, Y., Moore, M., Berggren, N., de Gelder, B., Derakshan, N., ... & Pegna, A. J. (2020). Neural correlates of emotion-attention interactions: From perception, learning, and memory to social cognition, individual differences, and training interventions. Neuroscience & Biobehavioral Reviews, 108, 559-601.

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Lugo-Candelas, C., Flegenheimer, C., Harvey, E., & McDermott, J. M. (2017). Neural correlates of emotion reactivity and regulation in young children with ADHD symptoms. Journal of abnormal child psychology, 45(7), 1311-1324.

Lukow, P., Kempton, M., Turkheimer, F., & Modinos, G. (2020). T133. NEURAL CORRELATES OF EMOTIONAL PROCESSING IN PSYCHOSIS RISK AND ONSET–A SYSTEMATIC REVIEW AND META-ANALYSIS OF FMRI STUDIES. Schizophrenia Bulletin, 46(Supplement_1), S281-S281.

Mejia, A. F., Yue, Y., Bolin, D., Lindgren, F., & Lindquist, M. A. (2020). A Bayesian general linear modeling approach to cortical surface fMRI data analysis. Journal of the American Statistical Association, 115(530), 501-520.

Miller, A. B., McLaughlin, K. A., Busso, D. S., Brueck, S., Peverill, M., & Sheridan, M. A. (2018). Neural correlates of emotion regulation and adolescent suicidal ideation. Biological psychiatry: cognitive neuroscience and neuroimaging, 3(2), 125-132.

Pozzi, E., Simmons, J. G., Bousman, C. A., Vijayakumar, N., Bray, K. O., Dandash, O., ... & Yap, M. B. (2020). The influence of maternal parenting style on the neural correlates of emotion processing in children. Journal of the American Academy of Child & Adolescent Psychiatry, 59(2), 274-282.

Raschle, N. M., Fehlbaum, L. V., Menks, W. M., Euler, F., Sterzer, P., & Stadler, C. (2017). Investigating the Neural Correlates of Emotion-Cognition Interaction Using an Affective Stroop Task. Frontiers in psychology, 8, 1489.

Rubin-Falcone, H., Weber, J., Kishon, R., Ochsner, K., Delaparte, L., Doré, B., ... & Miller, J. M. (2018). Longitudinal effects of cognitive behavioral therapy for depression on the neural correlates of emotion regulation. Psychiatry Research: Neuroimaging, 271, 82-90.

Sasai, S., Koike, T., Sugawara, S. K., Hamano, Y. H., Sumiya, M., Okazaki, S., ... & Sadato, N. (2020). Frequency-specific task modulation of human brain functional networks: A fast fMRI study. NeuroImage, 224, 117375.

Taylor, S. F., & Liberzon, I. (2007). Neural correlates of emotion regulation in psychopathology. Trends in cognitive sciences, 11(10), 413-418.

Ulmer, S., Booth, T. C., Widdershoven, G., Jansen, O., Fesl, G., von Kummer, R., & Reiter-Theil, S. (2020). Incidental findings in neuroimaging research: ethical considerations. In fMRI (pp. 433-440). Springer, Cham.

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