Lenette M. Jones1*
Emily Ginier2Joseph Debbs1
Jarrod L. Eaton1
Catherine Renner1Jaclynn Hawkins3Rosanna Rios-Spicer1
Emily Tang1
Catherine Schertzing1Bruno Giordani4- 1School of Nursing, University of Michigan, Ann Arbor, MI, United States
- 2Taubman Health Sciences Library, University of Michigan, Ann Arbor, MI, United States
- 3School of Social Work, University of Michigan, Ann Arbor, MI, United States
- 4Psychiatry, Neurology, Psychology, and Nursing, University of Michigan, Ann Arbor, MI, United States
Introduction/Purpose: Cardiovascular disease (CVD) is the leading cause of death worldwide, and in the United States alone, CVD causes nearly 840,000 deaths annually. Using functional magnetic resonance imaging (fMRI), a tool to assess brain activity, researchers have identified some brain-behavior connections and predicted several self-management behaviors. The purpose of this study was to examine the sample characteristics of individuals with CVD who participated in fMRI studies.
Methods: A literature search was conducted in PubMed, CINAHL, and Scopus. No date or language restrictions were applied and research methodology filters were used. In October 2017, 1659 titles and abstracts were identified. Inclusion criteria were: (1) utilized an empirical study design, (2) used fMRI to assess brain activity, and (3) focused on patients with CVD-related chronic illness. Articles were excluded if they: were theory or opinion articles, focused on mental or neuropathic illness, included non-human samples, or were not written in English. After duplicates were removed (230), 1,429 titles and abstracts were reviewed based on inclusion criteria; 1,243 abstracts were then excluded. A total of 186 studies were reviewed in their entirety; after additional review, 142 were further excluded for not meeting the inclusion criteria. Forty-four articles met criteria and were included in the final review. An evidence table was created to capture the demographics of each study sample.
Results: Ninety eight percent of the studies did not report the racial or ethnic composition of their sample. Most studies (66%) contained more men than women. Mean age ranged from 38 to 78 years; 77% reported mean age ≥50 years. The most frequently studied CVD was stroke (86%), while hypertension was studied the least (2%).
Conclusion: Understanding brain-behavior relationships can help researchers and practitioners tailor interventions to meet specific patient needs. These findings suggest that additional studies are needed that focus on populations historically underrepresented in fMRI research. Researchers should thoughtfully consider diversity and purposefully sample groups by including individuals that are: women, from diverse backgrounds, younger, and diagnosed with a variety of CVD-related illnesses. Identifying and addressing these gaps by studying more representative samples will help healthcare providers reduce disparities and tailor interventions for all CVD populations.
Introduction
According to the World Health Organization, cardiovascular disease (CVD) is an umbrella term which includes a number of heart and blood vessel disorders. Disorders include cerebrovascular disease (stroke) and hypertension (World Health Organization). About 121.5 million Americans are living with some form of CVD, with direct and indirect costs of total cardiovascular diseases and stroke totaling more than $351.2 billion (Benjamin et al., 2018, 2019). CVD is currently the leading cause of death in the U.S., causing nearly 836,546 deaths annually (Benjamin et al., 2018, 2019). In addition to its high mortality rate, CVD is associated with other chronic illnesses, such as end-stage renal disease and diabetes (Liu et al., 2014; Leon and Maddox, 2015). Groups that differ by race, ethnicity, education level, gender, and socioeconomic status are negatively and disproportionately affected by CVD and other chronic illnesses, and trends show that the gaps in these disparities are widening (Di Chiara et al., 2015; Havranek et al., 2015; Singh et al., 2015; Mehta et al., 2016). Initiatives that target specific social determinants of health are needed (Valero-Elizondo et al., 2018).
Some CVD-related illnesses, such as hypertension, can be controlled with lifestyle modifications such as consistently eating a healthy diet, engaging in regular physical activity, and adhering to antihypertensive medication (Nicolson et al., 2004). Such activities are often referred to as self-management behaviors. To improve upon these self-management behaviors, and therefore reduce associated risks with CVD, more studies are needed to assist practitioners to better guide patients toward consistent healthy behaviors. As such, studies that link brain activity via functional magnetic resonance imaging to self-management behaviors may establish an important foundation in achieving desirable patient outcomes.
Functional magnetic resonance imaging, or fMRI, is a tool that measures brain activity by detecting changes in blood oxygenation and flow that correspond to neural activity (Devlin et al., 2007). The brain increases oxygen demand in areas that are more active, and to meet this demand, blood flow increases to the area. In previous studies, researchers have used fMRI to predict self-management behaviors, such as sunscreen use and smoking cessation (Falk et al., 2010, 2011). Falk et al. (2010) measured neural activity in the medial prefrontal cortex (MPFC) of the brain while people watched persuasive messages about the value of using sunscreen regularly (Falk et al., 2010). They used these measurements to predict whether individuals would increase their sunscreen use, above and beyond self-report. They found that activity in the MPFC was significantly related to persuasion-induced behavior change, or increased sunscreen use, over the course of two weeks (Falk et al., 2010).
A subsequent study looked at the same area of the brain (MPFC) and tested whether neural activity in response to messages promoting smoking cessation could predict smoking cessation, above and beyond self-report (Falk et al., 2011). The researchers found that increases in MPFC activity were associated with decreases in expired carbon monoxide following exposure to professionally developed quitting ads (Falk et al., 2011). In both studies, by measuring MPFC activity while subjects viewed the persuasive messages, the researchers were able to predict the behavioral efficacy of the messages “above and beyond what participants' own self-reported attitude and intention change could predict” (Falk et al., 2010, 2011).
Other studies have examined the antagonistic relationship between analytic and socio-emotional neuroprocessing. Jack et al. (2013) found that individuals who were better able to process both analytic and socioemotional prompts, were better able to make plans, and act on the plans that they had developed (Jack et al., 2013). The analytic network, also known as the task positive network, pertains to skills, problem solving, and goal-directed actions (Duncan and Owen, 2000; Jack et al., 2013). Thus, it is activated by attention-demanding tasks (Fox et al., 2005; Uddin et al., 2009; Bressler and Menon, 2010). By contrast, the empathetic network, also referred to as the default mode network, encompasses emotional management and self-awareness, and is activated during periods of wakeful rest (Denny et al., 2012; Eisenberger and Cole, 2012; Marstaller et al., 2016).
Analytic information and emotional information are processed in different areas of the brain and are anti-correlated (Jack et al., 2013). The analytic information is processed in prefrontal and parietal areas of the brain, while the emotional information is processed in the posterior cingulate and medial prefrontal cortices (Duncan and Owen, 2000; Fox et al., 2005). This means that responses to different types of information varies depending on individual characteristics (Singh et al., 2015). One study reported findings that socio-emotional processing was positively associated with sharing of health information with others (Jones et al., 2019). Better understanding how the brain processes different types of information will help to develop individualized, and potentially more effective self-management interventions.
The purpose of this systematic review was to examine the demographic characteristics presented in fMRI studies that have been conducted with patients with CVD-related illnesses. Specifically, we aimed to evaluate studies on brain activity in participants with CVD to determine which patient populations the findings were applicable to. Results from the studies reviewed in this paper can be used to guide future studies to explore brain activity patterns to predict specific behaviors. For example, fMRI studies that focus on patients with CVD would be useful in helping researchers and clinicians better understand how brain activity patterns can be used to predict self-management of CVD, and which interventions may be more useful to individuals with certain patterns of brain activity (Moore et al., 2019).
Methods
Team
The team that conducted this work consisted of a doctorally-prepared nurse scientist, a health sciences librarian, and four undergraduate students. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used to guide reporting for this review.
Search Strategy
The databases PubMed, CINAHL, and Scopus were searched on October 2, 2017 to retrieve articles on use of functional magnetic resonance imaging (fMRI) in populations with chronic illnesses (see Appendix A). Controlled vocabulary (i.e., Medical Subject Headings and CINAHL Headings) and keywords were used to identify related terms for chronic illness and functional magnetic resonance imaging (fMRI). No date or language restrictions were applied to the search. In order to limit retrieval to treatment and diagnostic studies that contain empirical evidence, therapy, and diagnosis research methodology filters were used in PubMed and adapted for use in CINAHL and Scopus (Lokker et al., 2011). A total of 1,659 titles and abstracts were identified for review.
Study Selection
Two reviewers from the study team independently assessed study eligibility using Covidence systematic review software [Covidence Systematic Review Software]. Studies were selected for further review if they met the following criteria: (1) utilized an empirical study design, (2) used fMRI to assess brain activity, and (3) focused on patients with CVD-related chronic illness.
Articles were excluded that were not original research (e.g., theory or opinion articles), focused on mental or neuropathic illness, included non-human samples, or were not written in English.
Search Results
The search strategies retrieved a total of 1,659 articles. After the removal of duplicates, 1,429 articles remained for screening, of which 1,243 were excluded at the title and abstract level. Following a full-text review of 186 articles, 142 were excluded for not meeting criteria. Forty-four articles were included in the final review (see Figure 1 for a flow diagram).
Figure 1. Study eligibility flow diagram.
Presentation of Results
A table that categorized the studies based on geographical location was created to examine the variety of settings in which the studies had been conducted (see Table 1). Another table was developed to provide brief quantitative and descriptive summaries of the studies' design, gender distribution, mean age, and brain regions of interests (see Table 2). Lastly, a third table was created to determine which demographic characteristics (race, age, gender, income, and education) were examined in each study sample (see Table 3). The studies were categorized by the first author's last name and year, the chronic illness being studied, and the number of participants in each sample.
Table 1. Geographic location.
Table 2. Summary of studies.
Table 3. Study characteristics.
Results
Cardiovascular-Related Diseases
There were 38 studies that focused on patients that had strokes (86%) (Pineiro et al., 2001; Carey et al., 2002; Kato et al., 2002; Schaechter et al., 2002, 2007; Kimberley et al., 2004; Luft et al., 2004a,b, 2008; Tombari et al., 2004; Kim et al., 2006; Kwon et al., 2007; Shin et al., 2008; Takahashi et al., 2008; Fair et al., 2009; Menke et al., 2009; von Lewinski et al., 2009; Meehan et al., 2011; Michielsen et al., 2011; Rijntjes et al., 2011; Szaflarski et al., 2011; Whitall et al., 2011; Deng et al., 2012; Kononen et al., 2012; Veverka et al., 2012, 2014; Lazaridou et al., 2013; Pinter et al., 2013; Ramos-Murguialday et al., 2013; Sun et al., 2013; Brownsett et al., 2014; Hodgson et al., 2014; Mattioli et al., 2014; Milot et al., 2014; Rehme et al., 2015; Kielar et al., 2016; Landsmann et al., 2016; Pelicioni et al., 2016). Of the 38 stoke studies, the majority (84%) of the studies were randomized controlled trials (see Table 2). Four cohort studies (9%) had participants with chronic kidney disease (Lux et al., 2010; Jahanian et al., 2014; Zhang et al., 2015; Li et al., 2016). One cohort study had participants who had been diagnosed with type 2 diabetes (2%) and one cohort study had participants who had been diagnosed with hypertension (2%) (Gold et al., 2005; He et al., 2015).
Race, Ethnicity, and Geographic Location
The vast majority of studies (98%) did not report the racial or ethnic composition of the sample (Table 3). In the one study that did examine race, the report stated that over half of the participants were “Black,” followed by “White” participants, and a small percentage of “Hispanic/Other” participants (Luft et al., 2008). Most of the stroke studies were conducted in the United States (Table 1). Half of the renal studies were conducted in China. The type 2 diabetes study was conducted in China. The hypertension study was conducted in the United States. See Table 1 for additional details on where the studies were conducted.
Age
The majority (95%) of the studies provided the age of the participants (Table 3). There was variation in the manner which age was presented in each study. Some studies provided an overall mean for all of the participants, while others reported means for the intervention or affected groups compared to the control or “healthy” groups. The mean age of the participants ranged from 34 to 73 years; 77% of the studies reported mean age ≥50 years.
Gender
The majority (91%) of the studies reported the gender distribution of the sample. Most studies (66%) contained more men than women (Table 2). When examining the samples by chronic illness, studies focused on stroke patients had more men than women. Studies of chronic renal patients had more men than women. The study of patients with type 2 diabetes had more men than women, while the study of participants with hypertension had more women than men.
Income
None of the studies included in this review presented information on the participants' income.
Education
A total of 10 studies (23%) provided information on the participants' education levels, overall (Table 3). In terms of stroke studies specifically, six out of 38 studies (16%) reported on the education of participants. The mean years of education ranged from 6 to 16.8 years for five of the studies. An additional study reported that four of its participants received 14–17 years of education, however, no education data was available for the remainder of the participants. With regards to chronic kidney disease, three out of the four studies (75%) reported on the education of participants. The mean education ranged from 11.4 to 13.1 years. Regarding the sole type 2 diabetes study, mean participant education was reported as 9.8 years. Lastly, the singular hypertension study included in this review did not include information with respect to years of education of participants.
Discussion
The objective of this study was to examine the sample characteristics of individuals with CVD who participated in fMRI studies. A total of 44 studies were included in the review. Our findings demonstrate that race and ethnicity and socioeconomic status of participants are not often considered, as demonstrated in the inconsistency in reporting demographic characteristics from across the studies. This review highlights the need for more stringent and detailed collection of demographic data from participants enrolled in fMRI studies. Additional reviews are needed that evaluate fMRI studies sample sizes and stricter statistical threshold. Future studies are needed that focus on populations that have been historically underrepresented in fMRI/CVD-related research. Only one study mentioned the racial or ethnic composition of participants. Additionally, in 66% of the studies, the majority of participants were male. By studying a sample that is more representative of the general population and by expanding the type of CVD studied, researchers can identify practices that are relevant for populations that are disproportionately impacted by hypertension, such as African Americans. Diverse populations (i.e., racial and ethnic, gender, and age groups) vary greatly with respect to health risks such as CVD, as well as access to health care and other health disparities (Leigh et al., 2016). Patients with lower incomes and education levels may increased CVD risk (Marshall et al., 2015; Khaing et al., 2017). Additional research is needed to explore differences in brain activity patterns and related behaviors among diverse patients with CVD.
Of the studies identified as having reported CVD-related outcomes, 84% examined stroke, while only 2% examined hypertension or type 2 diabetes. It is important to note that understanding brain-behavior relationships has the potential to help researchers and practitioners tailor interventions to meet specific patient needs. These points further demonstrate a need for additional studies that use fMRI to better understanding brain-behavior relationships among patients with specific CVD-related diseases.
Limitations
The primary limitation of this study was the variety in reporting of demographic characteristics across the studies. As a result, it limited the summaries and conclusions that could be made. However, this lack of reporting supports the idea that future fMRI research needs to consider and prioritize racial/ethnic background, income, and education during the recruitment and sampling process. Additionally, the original goal of this literature review was to examine the fMRI studies that had be conducted with participants who self-identified as African American and were diagnosed with hypertension. Given that only one study met these criteria, the search strategy was expanded to include studies with conditions associated with hypertension (stroke, ESRD, diabetes). Given the large number of stroke studies in this review, a meta-analysis of these studies would be an interesting contribution to the literature.
Conclusion
This review suggests that certain groups with CVD disease (women, younger adults, racial/ethnicity minorities) are underrepresented in fMRI research. Therefore, there is a knowledge gap with respect to evidence about brain-behavior connections in groups that are of different races, ethnicities, or genders. Researchers should consider diversity when selecting sampling methods to include individuals from underrepresented groups, such as: women, individuals from diverse backgrounds, younger adults (age <50 years), and those diagnosed with hypertension. When recruiting participants with CVD for fMRI studies, researchers need to consider barriers that prevent these populations from participating, such as socioeconomic status, distrust of the scientific community, cultural barriers, and lack of knowledge related to fMRI research. Identifying and addressing these gaps will lead to the reduction of disparities in fMRI research and improve interventions for all CVD populations.
Author Contributions
We are pleased to submit this manuscript, entitled Exploring Representation of Diverse Samples in fMRI Studies Conducted in Patients with Cardiac-Related Chronic Illness: A Focused Systematic Review to be considered for publication. This paper highlights findings of a focused review on the demographics of patients with cardiovascular disease who participated in fMRI studies. This manuscript has not been published and is not under submission elsewhere. There are no conflict of interests that exist. All authors contributed substantively to the content of this manuscript and are in agreement for its readiness to be considered for publication: LJ and EG: development/implementation of methods, study review, and manuscript preparation. JD, JE, CR, and BG: manuscript preparation. JH: study review and manuscript preparation. RR-S: implementation of methods and manuscript preparation. ET and CS: implementation of methods, study review, and manuscript preparation.
Funding
This research was supported by the National Institute of Nursing Research, the National Heart, Lung, and Blood Institute, and the National Institute on Aging under Award Numbers P20NR015331, 2P30AG015281, 5P30AG053760, 1K01HL145366, and 5P30AG053760-S1.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
Benjamin, E. J., Muntner, P., and Bittencourt, M. S. (2019). Heart disease and stroke statistics-2019 update: A report from the American Heart Association. Circulation. 139, e56–e528. doi: 10.1161/CIR.0000000000000659
Benjamin, E. J., Virani, S. S., Callaway, C. W., Chamberlain, A. M., Chang, A. R., Cheng, S., et al. (2018). Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation 137:e67. doi: 10.1161/CIR.0000000000000558
Bressler, S. L., and Menon, V. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14, 277–290. doi: 10.1016/j.tics.2010.04.004
Brownsett, S. L., Warren, J. E., Geranmayeh, F., Woodhead, Z., Leech, R., and Wise, R. J. (2014). Cognitive control and its impact on recovery from aphasic stroke. Brain. 137(Pt 1), 242–254. doi: 10.1093/brain/awt289
Carey, J. R., Kimberley, T. J., Lewis, S. M., Auerbach, E. J., Dorsey, L., Rundquist, P., et al. (2002). Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain 125(Pt 4), 773–788. doi: 10.1093/brain/awf091
Covidence Systematic Review Software (2019). Veritas Health Innovation. Melbourne. Available online at: www.covidence.org
Deng, H., Durfee, W. K., Nuckley, D. J., Rheude, B. S., Severson, A. E., Skluzacek, K. M., et al. (2012). Complex versus simple ankle movement training in stroke using telerehabilitation: a randomized controlled trial. Phys. Ther. 92, 197–209. doi: 10.2522/ptj.20110018
Denny, B. T., Kober, H., Wager, T. D., and Ochsner, K. N. (2012). A meta-analysis of functional neuroimaging studies of self- and other judgments reveals a spatial gradient for mentalizing in medial prefrontal cortex. J. Cogn. Neurosci. 24, 1742–1752. doi: 10.1162/jocn_a_00233
Devlin, H., Tracey, I., Johansen-Berg, H., and Clare, S. (2007). What Is Functional Magnetic Resonance Imaging (fMRI). Oxford: FMRIB Centre; Department of Clinical Neurology; University of Oxford.
Di Chiara, T., Scaglione, A., Corrao, S., Argano, C., Pinto, A., and Scaglione, R. (2015). Association between low education and higher global cardiovascular risk. J. Clin. Hypertens. 17:332337. doi: 10.1111/jch.12506
Duncan, J., and Owen, A. M. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci. 23:475483. doi: 10.1016/S0166-2236(00)01633-7
Eisenberger, N. I., and Cole, S. W. (2012). Social neuroscience and health: neurophysiological mechanisms linking social ties with physical health. Nat. Neurosci. 15, 669–674. doi: 10.1038/nn.3086
Fair, D. A., Snyder, A. Z., Connor, L. T., Nardos, B., and Corbetta, M. (2009). Task-evoked BOLD responses are normal in areas of diaschisis after stroke. Neurorehabil. Neural Repair 23, 52–57. doi: 10.1177/1545968308317699
Falk, E. B., Berkman, E. T., Mann, T., Harrison, B., and Lieberman, M. D. (2010). Predicting persuasion-induced behavior change from the brain. J.Neurosci. 30, 8421–8424. doi: 10.1523/JNEUROSCI.0063-10.2010
Falk, E. B., Berkman, E. T., Whalen, D., and Lieberman, M. D. (2011). Neural activity during health messaging predicts reductions in smoking above and beyond self-report. Health Psychol. 30:177. doi: 10.1037/a0022259
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., and Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U.S.A. 102, 9673–9678. doi: 10.1073/pnas.0504136102
Gold, S. M., Dziobek, I., Rogers, K., Bayoumy, A., McHugh, P. F., and Convit, A. (2005). Hypertension and hypothalamo-pituitary-adrenal axis hyperactivity affect frontal lobe integrity. J. Clin. Endocrinol. Metab. 90, 3262–3267. doi: 10.1210/jc.2004-2181
Havranek, E. P., Mujahid, M. S., Barr, D. A., Blair, I. V., Cohen, M. S., Cruz-Flores, S., et al. (2015). Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation 132, 873–898. doi: 10.1161/CIR.0000000000000228
He, X. S., Wang, Z. X., Zhu, Y. Z., Wang, N., Hu, X., Zhang, D. R., et al. (2015). Hyperactivation of working memory-related brain circuits in newly diagnosed middle-aged type 2 diabetics. Acta Diabetol. 52, 133–142. doi: 10.1007/s00592-014-0618-7
Hodgson, J. C., Benattayallah, A., and Hodgson, T. L. (2014). The role of the dominant versus the non-dominant hemisphere: an fMRI study of Aphasia recovery following stroke. Aphasiology 28, 1426–1447. doi: 10.1080/02687038.2014.933640
Jack, A. I., Dawson, A. J., Begany, K. L., Leckie, R. L., Barry, K. P., Ciccia, A. H., et al. (2013). fMRI reveals reciprocal inhibition between social and physical cognitive domains. Neuroimage 66C, 385–401. doi: 10.1016/j.neuroimage.2012.10.061
Jahanian, H., Ni, W. W., Christen, T., Moseley, M. E., Kurella Tamura, M., and Zaharchuk, G. (2014). Spontaneous BOLD signal fluctuations in young healthy subjects and elderly patients with chronic kidney disease. PLoS ONE. 9:e92539. doi: 10.1371/journal.pone.0092539
Jones, L. M., Wright, K. D., Jack, A. I., Friedman, J. P., Fresco, D. M., Veinot, T., et al. (2019). The relationships between health information behavior and neural processing in african americans with prehypertension. J. Assoc. Information Sci. Technol. 70, 968–980. doi: 10.1002/asi.24098
Kato, H., Izumiyama, M., Koizumi, H., Takahashi, A., and Itoyama, Y. (2002). Near-infrared spectroscopic topography as a tool to monitor motor reorganization after hemiparetic stroke: a comparison with functional MRI. Stroke 33, 2032–2036. doi: 10.1161/01.STR.0000021903.52901.97
Khaing, W., Vallibhakara, S. A., Attia, J., McEvoy, M., and Thakkinstian, A. (2017). Effects of education and income on cardiovascular outcomes: a systematic review and meta-analysis. Eur. J. Prev. Cardiol. 24, 1032–1042. doi: 10.1177/2047487317705916
Kielar, A., Deschamps, T., Chu, R. K., Jokel, R., Khatamian, Y. B., Chen, J. J., et al. (2016). Identifying dysfunctional cortex: dissociable effects of stroke and aging on resting state dynamics in MEG and fMRI. Front. Aging Neurosci 8:40. doi: 10.3389/fnagi.2016.00040
Kim, Y. H., You, S. H., Kwon, Y. H., Hallett, M., Kim, J. H., and Jang, S. H. (2006). Longitudinal fMRI study for locomotor recovery in patients with stroke. Neurology 67, 330–333. doi: 10.1212/01.wnl.0000225178.85833.0d
Kimberley, T. J., Lewis, S. M., Auerbach, E. J., Dorsey, L. L., Lojovich, J. M., and Carey, J. R. (2004). Electrical stimulation driving functional improvements and cortical changes in subjects with stroke. Exp. Brain Res. 154, 450–460. doi: 10.1007/s00221-003-1695-y
Kononen, M., Tarkka, I. M., Niskanen, E., Pihlajamaki, M., Mervaala, E., Pitkanen, K., et al. (2012). Functional MRI and motor behavioral changes obtained with constraint-induced movement therapy in chronic stroke. Eur. J. Neurol. 19, 578–586. doi: 10.1111/j.1468-1331.2011.03572.x
Kwon, Y. H., Lee, M. Y., Park, J. W., Kang, J. H., Yang, D. S., Kim, Y. H., et al. (2007). Differences of cortical activation pattern between cortical and corona radiata infarct. Neurosci. Lett. 417, 138–142. doi: 10.1016/j.neulet.2007.01.084
Landsmann, B., Pinter, D., Pirker, E., Pichler, G., Schippinger, W., Weiss, E. M., et al. (2016). An exploratory intervention study suggests clinical benefits of training in chronic stroke to be paralleled by changes in brain activity using repeated fMRI. Clin. Intervent. Aging 11, 97–103. doi: 10.2147/CIA.S95632
Lazaridou, A., Astrakas, L., Mintzopoulos, D., Khanchiceh, A., Singhal, A., Moskowitz, M., et al. (2013). fMRI as a molecular imaging procedure for the functional reorganization of motor systems in chronic stroke. Mol. Med. Rep. 8, 775–779. doi: 10.3892/mmr.2013.1603
Leigh, J. A., Alvarez, M., and Rodriguez, C. J. (2016). Ethnic minorities and coronary heart disease: an update and future directions. Curr. Atheroscl. Rep. 18:9. doi: 10.1007/s11883-016-0559-4
Leon, B. M., and Maddox, T. M. (2015). Diabetes and cardiovascular disease: epidemiology, biological mechanisms, treatment recommendations and future research. World J. Diabetes 6:1246. doi: 10.4239/wjd.v6.i13.1246
Li, S., Ma, X., Huang, R., Li, M., Tian, J., Wen, H., et al. (2016). Abnormal degree centrality in neurologically asymptomatic patients with end-stage renal disease: A resting-state fMRI study. Clin. Neurophysiol. 127, 602–609. doi: 10.1016/j.clinph.2015.06.022
Liu, M., Li, X. C., Lu, L., Cao, Y., Sun, R. R., Chen, S., et al. (2014). Cardiovascular disease and its relationship with chronic kidney disease. Eur. Rev. Med. Pharmacol. Sci. 18, 2918–2926. Available online at: https://www.europeanreview.org/wp/wp-content/uploads/2918-2926.pdf
Lokker, C., Haynes, R. B., Wilczynski, N. L., McKibbon, K. A., and Walter, S. D. (2011). Retrieval of diagnostic and treatment studies for clinical use through PubMed and PubMed's Clinical Queries filters. J. Am. Med. Informatics Assoc. 18, 652–659. doi: 10.1136/amiajnl-2011-000233
Luft, A. R., Macko, R. F., Forrester, L. W., Villagra, F., Ivey, F., Sorkin, J. D., et al. (2008). Treadmill exercise activates subcortical neural networks and improves walking after stroke: a randomized controlled trial. Stroke 39, 3341–3350. doi: 10.1161/STROKEAHA.108.527531
Luft, A. R., McCombe-Waller, S., Whitall, J., Forrester, L. W., Macko, R., Sorkin, J. D., et al. (2004a). Repetitive bilateral arm training and motor cortex activation in chronic stroke: a randomized controlled trial. JAMA 292:185361. doi: 10.1001/jama.292.15.1853
Luft, A. R., Waller, S., Forrester, L., Smith, G. V., Whitall, J., Macko, R. F., et al. (2004b). Lesion location alters brain activation in chronically impaired stroke survivors. NeuroImage 21, 924–935. doi: 10.1016/j.neuroimage.2003.10.026
Lux, S., Mirzazade, S., Kuzmanovic, B., Plewan, T., Eickhoff, S. B., Shah, N. J., et al. (2010). Differential activation of memory-relevant brain regions during a dialysis cycle. Kidney Int. 78, 794–802. doi: 10.1038/ki.2010.253
Marshall, I. J., Wang, Y., Crichton, S., McKevitt, C., Rudd, A. G., and Wolfe, C. D. (2015). The effects of socioeconomic status on stroke risk and outcomes. Lancet Neurol. 14, 1206–1218. doi: 10.1016/S1474-4422(15)00200-8
Marstaller, L., Burianová, H., and Reutens, D. C. (2016). Adaptive contextualization: a new role for the default mode network in affective learning. Hum. Brain Mapp. 38, 1082–1091. doi: 10.1002/hbm.23442
Mattioli, F., Ambrosi, C., Mascaro, L., Scarpazza, C., Pasquali, P., Frugoni, M., et al. (2014). Early aphasia rehabilitation is associated with functional reactivation of the left inferior frontal gyrus: a pilot study. Stroke 45, 545–552. doi: 10.1161/STROKEAHA.113.003192
Meehan, S. K., Randhawa, B., Wessel, B., and Boyd, L. A. (2011). Implicit sequence-specific motor learning after subcortical stroke is associated with increased prefrontal brain activations: an fMRI study. Hum. Brain Mapp. 32, 290–303. doi: 10.1002/hbm.21019
Mehta, L. S., Beckie, T. M., DeVon, H. A., Grines, C. L., Krumholz, H. M., Johnson, M. N., et al. (2016). Acute myocardial infarction in women: a scientific statement from the American Heart Association. Circulation 133, 916–947. doi: 10.1161/CIR.0000000000000351
Menke, R., Meinzer, M., Kugel, H., Deppe, M., Baumgartner, A., Schiffbauer, H., et al. (2009). Imaging short- and long-term training success in chronic aphasia. BMC Neurosci. 10:118. doi: 10.1186/1471-2202-10-118
Michielsen, M. E., Selles, R. W., van der Geest, J. N., Eckhardt, M., Yavuzer, G., Stam, H. J., et al. (2011). Motor recovery and cortical reorganization after mirror therapy in chronic stroke patients: a phase II randomized controlled trial. Neurorehabil. Neural Repair 25, 223–233. doi: 10.1177/1545968310385127
Milot, M. H., Spencer, S. J., Chan, V., Allington, J. P., Klein, J., Chou, C., et al. (2014). Corticospinal excitability as a predictor of functional gains at the affected upper limb following robotic training in chronic stroke survivors. Neurorehab. Neural Repair 28, 819–827. doi: 10.1177/1545968314527351
Moore, S. M., Musil, C. M., Jack, A. I., Alder, M. L., Fresco, D. M., Webel, A., et al. (2019). Characterization of brain signatures to add precision to self-management health information interventions. Nurs. Res. 68, 127–134. doi: 10.1097/NNR.0000000000000331
Nicolson, D. J., Dickinson, H. O., Campbell, F., and Mason, J. M. (2004). Lifestyle interventions or drugs for patients with essential hypertension: a systematic review. J Hypertesn. 22, 2043–2048. doi: 10.1097/00004872-200411000-00001
Pelicioni, M. C., Novaes, M. M., Peres, A. S., Lino de Souza, A. A., Minelli, C., Fabio, S. R., et al. (2016). Functional versus nonfunctional rehabilitation in chronic ischemic stroke: evidences from a randomized functional MRI study. Neural Plasticity 2016:6353218. doi: 10.1155/2016/6353218
Pineiro, R., Pendlebury, S., Johansen-Berg, H., and Matthews, P. M. (2001). Functional MRI detects posterior shifts in primary sensorimotor cortex activation after stroke: evidence of local adaptive reorganization? Stroke 32, 1134–1139. doi: 10.1161/01.STR.32.5.1134
Pinter, D., Pegritz, S., Pargfrieder, C., Reiter, G., Wurm, W., Gattringer, T., et al. (2013). Exploratory study on the effects of a robotic hand rehabilitation device on changes in grip strength and brain activity after stroke. Top. Stroke Rehabil. 20, 308–316. doi: 10.1310/tsr2004-308
Ramos-Murguialday, A., Broetz, D., Rea, M., Laer, L., Yilmaz, O., Brasil, F. L., et al. (2013). Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100–108. doi: 10.1002/ana.23879
Rehme, A. K., Volz, L. J., Feis, D. L., Eickhoff, S. B., Fink, G. R., and Grefkes, C. (2015). Individual prediction of chronic motor outcome in the acute post-stroke stage: behavioral parameters versus functional imaging. Hum. Brain Mapp. 36, 4553–4565. doi: 10.1002/hbm.22936
Rijntjes, M., Hamzei, F., Glauche, V., Saur, D., and Weiller, C. (2011). Activation changes in sensorimotor cortex during improvement due to CIMT in chronic stroke. Restorative Neurol. Neurosci. 29, 299–310. doi: 10.3233/RNN-2011-0600
Schaechter, J. D., Connell, B. D., Stason, W. B., Kaptchuk, T. J., Krebs, D. E., Macklin, E. A., et al. (2007). Correlated change in upper limb function and motor cortex activation after verum and sham acupuncture in patients with chronic stroke. J. Alternative Complement. Med. 13, 527–532. doi: 10.1089/acm.2007.6316
Schaechter, J. D., Kraft, E., Hilliard, T. S., Dijkhuizen, R. M., Benner, T., Finklestein, S. P., et al. (2002). Motor recovery and cortical reorganization after constraint-induced movement therapy in stroke patients: a preliminary study. Neurorehabil. Neural Repair 16, 326–338. doi: 10.1177/154596830201600403
Shin, H. K., Cho, S. H., Jeon, H. S., Lee, Y. H., Song, J. C., Jang, S. H., et al. (2008). Cortical effect and functional recovery by the electromyography-triggered neuromuscular stimulation in chronic stroke patients. Neurosci. Lett. 442, 174–179. doi: 10.1016/j.neulet.2008.07.026
Singh, G. K., Siahpush, M., Azuine, R. E., and Williams, S. D. (2015). Widening socioeconomic and racial disparities in cardiovascular disease mortality in the United States, 1969-2013. Int. J. MCH AIDS 3:106. doi: 10.21106/ijma.44
Sun, L., Yin, D., Zhu, Y., Fan, M., Zang, L., Wu, Y., et al. (2013). Cortical reorganization after motor imagery training in chronic stroke patients with severe motor impairment: a longitudinal fMRI study. Neuroradiology 55, 913–925. doi: 10.1007/s00234-013-1188-z
Szaflarski, J. P., Eaton, K., Ball, A. L., Banks, C., Vannest, J., Allendorfer, J. B., et al. (2011). Poststroke aphasia recovery assessed with functional magnetic resonance imaging and a picture identification task. J. Stroke Cerebrovasc. Dis. 20, 336–345. doi: 10.1016/j.jstrokecerebrovasdis.2010.02.003
Takahashi, C. D., Der-Yeghiaian, L., Le, V., Motiwala, R. R., and Cramer, S. C. (2008). Robot-based hand motor therapy after stroke. Brain 131(Pt 2), 425–437. doi: 10.1093/brain/awm311
Tombari, D., Loubinoux, I., Pariente, J., Gerdelat, A., Albucher, J. F., Tardy, J., et al. (2004). A longitudinal fMRI study: in recovering and then in clinically stable sub-cortical stroke patients. NeuroImag. 23:82739. doi: 10.1016/j.neuroimage.2004.07.058
Uddin, L. Q., Kelly, A. M., Biswal, B. B., Castellanos, F. X., and Milham, M. P. (2009). Functional connectivity of default mode network components: correlation, anticorrelation, and causality. Hum. Brain Mapp. 30, 625–637. doi: 10.1002/hbm.20531
Valero-Elizondo, J., Hong, J. C., Spatz, E. S., Salami, J. A., Desai, N. R., Rana, J. S., et al. (2018). Persistent socioeconomic disparities in cardiovascular risk factors and health in the United States: Medical Expenditure Panel Survey 2002-2013. Atherosclerosis 269, 301–305. doi: 10.1016/j.atherosclerosis.2017.12.014
Veverka, T., Hlustik, P., Hok, P., Otruba, P., Tudos, Z., Zapletalova, J., et al. (2014). Cortical activity modulation by botulinum toxin type A in patients with post-stroke arm spasticity: real and imagined hand movement. J. Neurol. Sci. 346, 276–283. doi: 10.1016/j.jns.2014.09.009
Veverka, T., Hlustik, P., Tomasova, Z., Hok, P., Otruba, P., Kral, M., et al. (2012). BoNT-A related changes of cortical activity in patients suffering from severe hand paralysis with arm spasticity following ischemic stroke. J. Neurol. Sci. 319, 89–95. doi: 10.1016/j.jns.2012.05.008
von Lewinski, F., Hofer, S., Kaus, J., Merboldt, K. D., Rothkegel, H., Schweizer, R., et al. (2009). Efficacy of EMG-triggered electrical arm stimulation in chronic hemiparetic stroke patients. Restorative Neurol. Neurosci. 27, 189–197. doi: 10.3233/RNN-2009-0469
Whitall, J., Waller, S. M., Sorkin, J. D., Forrester, L. W., Macko, R. F., Hanley, D. F., et al. (2011). Bilateral and unilateral arm training improve motor function through differing neuroplastic mechanisms: a single-blinded randomized controlled trial. Neurorehabil. Neural Repair 25, 118–129. doi: 10.1177/1545968310380685
World Health Organization. Definition of Cardiovascular Diseases. Available online at: http://www.euro.who.int/en/health-topics/noncommunicablediseases/cardiovascular-diseases/cardiovascular-diseases2/definition-ofcardiovascular-diseases
Zhang, X. D., Wen, J. Q., Xu, Q., Qi, R., Chen, H. J., Kong, X., et al. (2015). Altered long- and short-range functional connectivity in the patients with end-stage renal disease: a resting-state functional MRI study. Metab. Brain Dis. 30, 1175–1186. doi: 10.1007/s11011-015-9683-z
Appendix A
Search Strategies by Database
PUBMED
#1. (“fMRI” OR “functional magnetic resonance imaging” OR “functional MRI” OR ((“brain imaging” OR neuroimag*) AND functional) OR neuroprocess*).
#2. (“Chronic Disease”[Mesh] OR chronically OR chronic).
#3. ((sensitiv*[Title/Abstract] OR sensitivity and specificity[MeSH Terms] OR diagnose[Title/Abstract] OR diagnosed[Title/Abstract] OR diagnoses[Title/Abstract] OR diagnosing[Title/Abstract] OR diagnosis[Title/Abstract] OR diagnostic[Title/Abstract] OR diagnosis[MeSH:noexp] OR diagnostic * [MeSH:noexp] OR diagnosis,differential[MeSH:noexp] OR diagnosis[Subheading:noexp]) OR ((clinical[Title/Abstract] AND trial[Title/Abstract]) OR clinical trials as topic[MeSH Terms] OR clinical trial[Publication Type] OR random*[Title/Abstract] OR random allocation[MeSH Terms] OR therapeutic use[MeSH Subheading])).
#4. #1 AND #2 AND #3.
CINAHL
S1. (“fMRI” OR “functional magnetic resonance imaging” OR “functional MRI” OR ((“brain imaging” OR neuroimag*) AND functional) OR neuroprocess*).
S2. (MH “Chronic Disease” OR chronically OR chronic).
S3. ((TI sensitiv* OR AB sensitiv* OR MH “Sensitivity and Specificity” OR TI diagnose OR AB diagnose OR TI diagnosed OR AB diagnosed OR TI diagnoses OR AB diagnoses OR TI diagnosing OR AB diagnosing OR TI diagnosis OR AB diagnosis OR TI diagnostic OR AB diagnostic OR MH “Diagnosis” OR MH “Diagnostic Imaging” OR MH “Diagnostic Services” OR MH “Diagnostic Errors” OR MH “Diagnosis, Differential” OR MW DI) OR (((TI clinical OR AB clinical) AND (TI trial OR AB trial)) OR MH “Clinical Trials+” OR TI random* OR AB random* OR MH “Random Assignment” OR MW TH)).
S4. S1 AND S2 AND S3.
SCOPUS
#1. TITLE-ABS-KEY(“fMRI” OR “functional magnetic resonance imaging” OR “functional MRI” OR ((“brain imaging” OR neuroimag*) AND functional) OR neuroprocess*).
#2. TITLE-ABS-KEY (chronic OR chronically).
#3. TITLE-ABS-KEY (cardiovascular OR CVD OR heart OR circulatory OR myocardial OR myocardium OR “blood vessel” OR hypertension OR hypertensive OR “blood pressure”).
#4. ALL(sensitiv* OR Specificity OR diagnose OR diagnosed OR diagnoses OR diagnosing OR diagnosis OR diagnostic OR (clinical AND trial*) OR random* OR “therapeutic use”).
#5. #1 AND #2 AND #3 AND #4.
Keywords: fMRI, cardiovascular disease, sample demographics, health disparities, chronic illness
Citation: Jones LM, Ginier E, Debbs J, Eaton JL, Renner C, Hawkins J, Rios-Spicer R, Tang E, Schertzing C and Giordani B (2020) Exploring Representation of Diverse Samples in fMRI Studies Conducted in Patients With Cardiac-Related Chronic Illness: A Focused Systematic Review. Front. Hum. Neurosci. 14:108. doi: 10.3389/fnhum.2020.00108
Received: 29 July 2019; Accepted: 11 March 2020;
Published: 14 May 2020.
Edited by:
M. Natasha Rajah, McGill University, CanadaReviewed by:
Andy Wai Kan Yeung, The University of Hong Kong, Hong KongKelly Wierenga, Indiana University, United States
Copyright © 2020 Jones, Ginier, Debbs, Eaton, Renner, Hawkins, Rios-Spicer, Tang, Schertzing and Giordani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Lenette M. Jones, bGVuZXR0ZXcmI3gwMDA0MDt1bWljaC5lZHU=