Integrating Neuroimaging Measures in Nursing Research

Abstract

Background

Medical and scientific advancement worldwide has led to a longer lifespan. With the population aging comes the risk of developing cognitive decline. The incorporation of neuroimaging measures in evaluating cognitive changes is limited in nursing research. The aim of this review is to introduce nurse scientists to neuroimaging measures employed to assess the association between brain and cognitive changes.

Methods

Relevant literature was identified by searching CINAHL, Web of Science, and PubMed databases using the following keywords: “neuroimaging measures,” “aging,” “cognition,” “qualitative scoring,” “cognitive ability,” “molecular,” “structural,” and “functional.”

Results

Neuroimaging measures can be categorized into structural, functional, and molecular imaging approaches. The structural imaging technique visualizes the anatomical regions of the brain. Visual examination and volumetric segmentation of select structural sequences extract information such as white matter hyperintensities and cerebral atrophy. Functional imaging techniques evaluate brain regions and underlying processes using blood-oxygen-dependent signals. Molecular imaging technique is the real-time visualization of biological processes at the cellular and molecular levels in a given region. Examples of biological measures associated with neurodegeneration include decreased glutamine level, elevated total choline, and elevated Myo-inositol.

Discussion

Nursing is at the forefront of addressing upstream factors impacting health outcomes across a lifespan of a population at increased risk of progressive cognitive decline. Nurse researchers can become more facile in using these measures both in qualitative and quantitative methodology by leveraging previously gathered neuroimaging clinical data for research purposes to better characterize the associations between symptom progression, disease risk, and health outcomes.

Introduction

Nurse scientists are leaders in health prevention, promotion, and healthy aging research. The recent announcement of the National Institute of Nursing Research to prioritize nursing science informed by health equity lenses brings forth the critical roles of nursing scientists in bridging healthcare disparities across the lifespan. The risk of developing cognitive decline increases with a diverse aging population, and its evaluation becomes even more complex. Most studies often exclude patients with severe cognitive impairment due to their inability to participate in performance-based cognitive exams or subjective patient reports. This indirectly excludes those who may potentially benefit greater from interventions and limits the broader implication of scientific discoveries in understanding the progression of severe cognitive impairment.

Using performance-based cognitive exams and subjective patient reports is challenging when evaluating persons with known neurological deficits (i.e., motor weakness, aphasia) or with severe cognitive impairment, as the results skew cognitive exams and subjective reports. Therefore, there is a growing interest in incorporating objective neuroimaging measures in research to expand the generalizability of implications among the broader population experiencing cognitive changes. Neuroradiographic images, especially those obtained for clinical use, are utilized in various research inquiries across patient populations, as seen in persons with mild cognitive impairment (MCI) and dementia (Scheltens et al., 2016), multiple sclerosis (Filippi et al., 2016), Parkinson’s disease (Yarnall et al., 2014), traumatic brain injury (Sharp et al., 2014), Huntington’s Disease (Unschuld et al., 2012), and cancer survivors (Deprez et al., 2012).

Despite the advantages of integrating neuroimaging measures in the assessment of cognitive function, their use remains limited to scientists with radiological expertise. Recent reviews within the nursing literature describe different imaging modalities (e.g., Magnetic Resonance Imaging [MRI], Positron Emission Tomography [PET]) and discuss challenges when incorporating neuroimaging measures. The reviews, however, do not include methods of analysis (Atalla et al., 2020; Johnson et al., 2006; Kolanowski et al., 2019). Integrating neuroimaging measures in nursing research allows objective characterization of the physiological underpinnings between diseases, symptoms, and health outcomes. Clinical neuroimaging can be readily downloaded and then qualitatively and quantitatively analyzed by nurse researchers to understand and advance the research of individuals experiencing severe cognitive changes. This paper aims to build on prior nursing literature and provide an introductory overview of neuroimaging measures that can be employed to assess brain changes associated with cognitive changes.

Methods

Relevant literature was identified by searching CINAHL, Web of Science, and PubMed databases using the following keywords: “neuroimaging measures,” “aging,” “cognition,” “qualitative scoring,” “cognitive ability,” “molecular,” “structural,” and “functional.” The following restriction criteria were used to narrow the search to the most relevant articles: publication year from 2000 to August 31, 2021, English language, adult (≥18 years old), and humans. Relevant articles were identified first by initial screening of titles and abstracts, followed by a full-text review. Seminal studies describing the development of widely used neuroimaging measures were also included in the review. Articles not pertaining to neuroimaging measures, humans, and cognition were excluded.

Results

Neuroimaging measures can be extracted through qualitative examination and quantitative analysis. Qualitative examination or direct visual scoring refers to examining and scoring the characteristics of the brain from the neuroimaging modality (i.e., MRI or CT). Quantitative analysis refers to the extraction of the measurement of the region of interest manually, automated, or semi-automated by trained personnel.

Qualitative Visual Examination

Extracting overall global or regional structural changes from radiographic images via direct visual examination is termed qualitative examination. Common qualitative analyses for brain imaging can evaluate brain changes such as white matter hyperintensities (WMH) and cerebral atrophy. WMH refers to signal abnormality (hyperintensity) in the white matter region of the brain using the Fluid-Attenuated Inverted Recovery (FLAIR)/T2 MRI sequence (Merino, 2019). Cerebral atrophy is a general term used to describe brain tissue loss and can be characterized by the gradual widening of the cortical sulci, enlargement of the ventricles, cortical thinning, and shrinkage of subcortical structures such as the hippocampus (Harris et al., 2019). WMH and cerebral atrophy have long been associated with cognitive impairment across diverse populations (Appelman et al., 2009; Crutch et al., 2012; Debette & Markus, 2010; Mariani et al., 2007; Wang et al., 2021). The most commonly used tools for direct visual examinations of brain MRI include the Fazekas Scale (Fazekas et al., 1987), Rotterdam Scale (De Leeuw et al., 2001), Prins Scale (Prins et al., 2004), Global Cortical Atrophy (GCA) (Pasquier et al., 1996), Koedam Scale (Koedam et al., 2011), and Scheltens Scale (Scheltens et al., 1993). The Scheltens Scale has both WMH and medial temporal atrophy scoring.

Direct visualization of WMH uses the FLAIR sequence of an MRI. These WMH are often periventricular and thus can be further subcategorized into three regions: frontal and occipital caps and lateral bands (See Figure 1). Depending on the research question, deep WMH is evaluated in either general or lobar terms (De Leeuw et al., 2001; Fazekas et al., 1987). The Prins and Scheltens scales subdivide global areas into respective lobar regions, with the latter also including basal ganglia and the infra-tentorial regions. See Table 1 for the grading criteria of these scales.

Figure 1.

Regions in qualitatively assessing and grading white matter hyperintensities are the frontal caps (a), occipital caps (b), and lateral bands (c) as shown above.

Table 1.

Qualitative Examination: White Matter Hyperintensity Scales.

Measure	Periventricular	Deep White Matter	Examples of cognitive changes and correlation	References
Fazekas scale	Periventricular white matter (PVWM) 0 = Absent 1 = “Caps” or pencil-thin lining 2 = Smooth “halo” 3 = Irregular periventricular signal extending into the deep white matter	0 = Absent 1 = Punctate foci 2 = Beginning confluence 3 = Large confluent areas
Schelten’s scale (WMH)	Periventricular hyperintensities (PVH 0-6)	White matter hyperintensities (WMH 0-24)
	0 = Absent 1= ≤ 5 mm 2 = > 5 mm and <10 mm Caps Occipital 0/1/2 Frontal 0/1/2 Bands Lat ventricles 0/l/2	0 = no abnormalities 1 = <3 mm, n ≤ 5 2 = <3 mm, n > 6 3 = 4–10 mm, n ≤ 5 4 = 4 mm, n > 6 5 = > 11 mm, n > 1 6 = confluent Frontal 0/1/2/3/4/5/6 Parietal 0/1/2/3/4/5/6 Occipital 0/1/2/3/4/5/6 Temporal 0/1/2/3/4/5/6 Basal ganglia hyperintensities (BG 0-30) Caudate nucleus 0/1/2/3/4/5/6 Putamen 0/1/2/3/4/5/6 Globus Pallidus 0/1/2/3/4/5/6 Thalamus 0/1/2/3/4/5/6 Internal capsule 0/1/2/3/4/5/6 Infra-tentorial foci of Hyperintensity (ITF 0-24) Cerebellum 0/1/2/3/4/5/6 Mesencephalon 0/1/2/3/4/5/6 Pons 0/1/2/3/4/5/6 Medulla 0/1/2/3/4/5/6 N refers to the number of lesions.	Increasing score (increasing size and volume) of WMH is associated with decreasing cognitive function (such as psychomotor speed), increasing cognitive impairment (i.e., executive function, memory, verbal fluency).	(Debette & Markus, 2010; Defrancesco et al., 2013; Dufouil et al., 2009; Mariani et al., 2007)
Rotterdam scan study	Periventricular (0–9) 0 = No white matter lesions 1 = Pencil thin periventricular lining 2 = Smooth halo or thick lining 3 = large confluent white matter lesions Frontal capping Occipital capping Lateral ventricles (bands)	White matter Lesions (Categorized based on number and size) Small: < 3 mm Medium: 3–10 mm Large: > 10 mm
Prins scale (WML Change scale)	Rated as:	Rates:
		Frontal
	−1 Decrease	Parietal
	0 No change	Temporal
	+1 Increase	Occipital
	Scored in 3 periventricular locations	Resulting in a subcortical score of −4 to +4.
	- Frontal caps - Lateral bands - Occipital caps Results in a periventricular score of −3 to +3.	An increase is defined as the occurrence of a new focal lesion or the enlargement of a previously visible lesion; a decrease is defined as the reverse (i.e., disappearance or shrinkage).

Cerebral atrophy can also be assessed qualitatively through direct visual analysis. The Koedam scale (Koedam et al., 2011) examines regional sulcal widening and gyral atrophy, whereas the Global Cortical Atrophy Scale (Pasquier et al., 1996) also evaluates ventricular enlargement. Alternatively, Schelten’s Medial Temporal Atrophy (MTA) scoring evaluates the width of the choroid fissure, the temporal horn of the lateral ventricles, and the height of the hippocampus (Scheltens et al., 1995). See Table 2 for the grading criteria of these scales.

Table 2.

Qualitative Examination: Cerebral Atrophy Scales.

Measure	Regions	Grading scale	Examples of cognitive changes/impairment and correlation	References
Koedam scale (Posterior Atrophy score)	Sagittal plane - Posterior cingulate sulcus - Parieto-occipital sulcus - Precuneus gyrus Coronal plane - Posterior cingulate sulcus - Parietal gyrus Axial plane - Posterior cingulate sulcus - Parietal lobes The worse features are used to generate a grade of 0–3	0: Closed sulci, no gyral atrophy 1: Mild sulcal widening, mild gyral atrophy 2: Substantial sulcal widening, substantial gyral atrophy 3: Marked sulcal widening, knife-blade gyral atrophy	Increasing posterior cortical atrophy is associated with progressive decline in visuospatial, visuoperceptual, literacy, and praxic skills.	(Crutch et al., 2012; Mimenza-Alvarado et al., 2018)
Pasquier scale (Global cortical atrophy scale)	Sulcal dilatation - Frontal (right and left) - Parieto-occipital (right and left) - Temporal (right and left) Ventricular dilatation - Frontal (right and left) - Prieto-occipital (right and left) - Temporal (right and left) - Third ventricle	0: Normal volume/no ventricular enlargement 1: Opening of sulci/mild ventricular enlargement 2: Volume loss of gyri/moderate ventricular enlargement 3: ‘Knife blade’ atrophy/severe ventricular enlargement	Progressive decline in global cortical atrophy rate is associated with progressive cognitive decline in psychomotor speed and executive function.	(Jokinen et al., 2012)
Scheltens’s scale (medial temporal Atrophy scale)	Width of the choroid fissure Width of the temporal horn of the lateral ventricle Height of the hippocampus	0: No CSF is visible around the hippocampus 1: Choroid fissure is slightly widened 2: Moderate widening of the choroid fissure, mild enlargement of the temporal horn and mild loss of hippocampal height 3: Marked widening of the choroid fissure, moderate enlargement of the temporal horn, and moderate loss of hippocampal height 4: Marked widening of the choroid fissure, marked enlargement of the temporal horn, and the hippocampus is markedly atrophied, and internal structure is lost	Increasing atrophy of the medial temporal is associated with increasing forgetfulness or memory deficits	(Mimenza-Alvarado et al., 2018)

Visual rating analyses using neuroimaging scales are relatively easy to perform. Numerically defining WMH changes has facilitated grading and agreement between raters, as seen in Scheltens WMH (Scheltens et al., 1993) and Rotterdam Study Scale (De Leeuw et al., 2001). However, visual rating is prone to a ceiling effect due to its categorical nature; therefore, the severity of WMH and cerebral atrophy is often not fully appreciated when using such methods (De Leeuw et al., 2001; Fazekas et al., 1987; Kapeller et al., 2003; Pasquier et al., 1996; Prins et al., 2004; Scheltens et al., 1995). In addition, qualitative assessment of WMH or cerebral atrophy can either be under or over-estimated or dependent on the examiner’s expertise and experience. While there is good interrater reliability when used cross-sectionally (Prins et al., 2004), this reliability is difficult to replicate when images are evaluated longitudinally. The Prins Scale can detect WMH change over time but, unfortunately, does not consider the extent of WMH (Prins et al., 2004).

Quantitative Interrogation

Quantitative interrogation obtains objective measurements of specific neuroimage properties. Measurements are commonly derived in three categories of imaging approaches: structural, functional, and molecular. All three can be used in conjunction with visual scoring. A summary of these neuroimaging measures is described in Table 3. For a more comprehensive overview of these three neuroimaging methods, please refer to the 2020 review by Attala and colleagues which also includes a summary of neuroimaging vocabularies for nurse scientists. In the next section, we expand upon the Attala review by introducing neuroimaging measures that can be obtained from these modalities.

Table 3.

Quantitative Interrogation.

Imaging Method	Examples of neuroimaging measures to evaluate cognitive function	Examples of cognitive changes/impairment and correlation	References
Structural
Structural MRI	Regions of interest (i.e., cortical and subcortical structures)	Volume atrophy in neocortical, caudate, putamen, globus pallidus, thalamus and nucleus accumbens are associated with decreased information-processing speed in patients with multiple sclerosis.	(Batista et al., 2012)
Diffusion tensor imaging	Fractional Anisotropy (FA) and mean diffusivity (MD) (Can estimate changes in white matter fiber organization and illustrate the fiber bundles connecting different brain areas.)	Reduced FA of the genu of the corpus callosum is associated with the rate of global cognitive decline (memory, language, attention/executive and visuospatial function) in individuals with mild cognitive impairment.	(Raghavan et al., 2020)
Functional
fMRI	Blood-oxygen-dependent signals during a task (Fluctuations in the BOLD signal can signify decreased or increased neuronal activity during the tasks and infer brain functioning)	Higher levels of BOLD-signal variability in the left inferior frontal area are associated with reduced errors during task switching and thus infer cognitive flexibility.	(Armbruster-Genc et al., 2016)
rs-fMRI	BOLD signals during a resting state	BOLD signal variability located in the posterior medial temporal lobes, hippocampus, visual cortex, and striatum showed that in healthy individuals (normal score on Montreal Cognitive Assessment [MOCA]), higher BOLD signal variability at rest was related to higher scores on the cognitive control/speed and intelligence factors. This relationship is reversed among those who are at-risk (those who score below normal threshold <26 in MOCA.	(Good et al., 2020)
Perfusion studies	Cerebral blood flow	Lower total global cerebral perfusion is associated with accelerated decline in global cognition, particularly in memory and executive function and at increased risk for developing dementia.	(Wolters et al., 2017a)
Molecular
PET Ligand-dependent	Fluorodeoxyglucose (FDG), β-Amyloid, Tau	Lower FDG- ROIs (right and left angular gyri, bilateral posterior cingulate, right and left inferior temporal gyri) is associated with greater decline in cognitive function (language, memory, praxis, and comprehension) and is predictive of functional decline (daily tasks such as shopping, preparing meals, handling finances, and understanding current events).	(Landau et al., 2011)
MR spectroscopy	Biologic metabolites, such as lactate, glutamine, glutamate, choline, creatinine, N-acetylaspartate and Myo-inositol in a region of interest depending on the research query.	Lower concentration of whole-brain N-acetylaspartate is associated with mild cognitive impairment among older adults.	(Glodzik et al., 2015)
Single-photon emission computed tomography	Estimates regional blood volume flow via tracer uptake.	Hypoperfusion in the left middle temporal gyrus, right inferior frontal gyrus, right lingual gyrus, left lingual gyrus, right postcentral gyrus, right cingulate gyrus, left thalamus is associated with lower visuospatial functioning in older individuals at risk for developing Alzheimer’s disease.	(Yoon et al., 2012)

Structural Imaging

Structural imaging refers to the visualization of the anatomical regions of the brain. For this purpose, MRI is more commonly used than Computerized tomography (CT) in research settings (Hasan et al., 2019; Merino, 2019; Rocca et al., 2015). Visual qualitative examination and volumetric quantitative segmentation of select structural sequences extract information such as WMH and cerebral atrophy, both globally and regionally. Advanced MRI techniques such as diffusion tensor imaging (DTI) are employed to interrogate changes in white matter microstructure using mean diffusivity (MD) and fractional anisotropy (FA). MD is the global movement of water diffusion within a brain tissue, whereas FA refers to the directional dependence of water diffusion in one direction over another (Aung et al., 2013). Water molecules more readily diffuse along the direction of axons compared to other directions (i.e., perpendicular to the axon); thus, DTI describes the directionality and magnitude of water molecules in the brain tissue and can estimate changes in white matter fiber organization and illustrate the fiber bundles connecting different brain areas. Significant differences in FA and MD, particularly in the corpus callosum, have been described among patients with Alzheimer’s disease, mild cognitive impairment, and healthy controls (Douaud et al., 2011). DTI can also be particularly useful in further elucidating microstructural alterations in the brain when a routine MRI is deemed unremarkable (Mac Donald et al., 2017).

Quantitative structural imaging approaches often use the segmentation method (demarcation and labeling of brain structure), which can be performed manually, automated, or a combination of both (semi-automated). The manual technique refers to the hand segmentation and labeling of an image. Manual segmentation is intensive, time-consuming, and prone to errors with wide intra and inter-operator variability (Collier et al., 2003). In contrast, the automated approach refers to the processing of the image using a software pipeline and brain atlas to perform the segmentation. Automated segmentation, though it can be processed in a relatively shorter amount of time, requires knowledge of the limitations of the method. For example, the accuracy of automated segmentation pipelines is highly dependent on image quality. The optimum resolution of the 3D T1 weighted image is used for segmentation with the following voxel dimensions: 1 x 1 x 1 mm (Despotovic et al., 2015). In addition, the field strength of the MRI machine and the presence of significant noise from MRI artifacts can affect intensity distribution and lead to the misclassification of brain structures (Despotovic et al., 2015). Automated software uses atlas-based segmentation of generally healthy participants; therefore, it is also prone to mislabeling structures when the images have significant structural abnormalities (i.e., large resection cavities or edema present). Techniques to increase accuracy with post-processing software include the use of a probabilistic atlas in image registration and spatial context or neighboring system. The spatial context in image registration refers to the software labeling an area of the brain based on its surrounding region (Fischl et al., 2002). A few examples of automated segmentation software include Statistical Parametric Mapping (SPM) (Ashburner, 2012), FMRIB Software Library (FSL) (Jokinen et al., 2012), Multi-atlas-based multi-image segmentation (MABMIS) (Jia et al., 2012), and FreeSurfer (Fischl, 2012). Software programs such as FreeSurfer use a probabilistic atlas in labeling data sets and integrate manual correction in the three orthogonal planes after automated segmentation (See Figure 2). It is prudent to know the limitations and strengths of each software processing package when designing a study to account for them during image acquisition and post-processing analyses.

Figure 2.

Using FreeSurfer software to automatically segment and manually correct structures of the brain using 3D view (d) in 3 orthogonal planes: Axial (a), Sagittal (b), and Coronal (c).

Functional Imaging

Imaging techniques such as functional MRI (fMRI) evaluate brain regions and underlying processes associated with performing a particular cognitive or behavioral task using blood-oxygen-dependent signals (Johnson et al., 2006; Sala-Llonch et al., 2015). Fluctuations in the BOLD signal can signify decreased or increased neuronal activity during the task, inferring brain functioning (Belliveau et al., 1991; Kwong et al., 1992) or compensatory mechanisms (Park & Reuter-Lorenz, 2009; Sala-Llonch et al., 2015). Studies have investigated resting-state functional MRI (rs-fMRI), and low fluctuations in BOLD signaling to characterize brain connectivity at rest, without a cognitive or directed task, and describe neuronal activity patterns among groups across the life span (Ferreira & Busatto, 2013). Abnormalities in the amplitude of low-frequency fluctuations of BOLD signals at rest, specifically in the posterior cingulate cortex, precuneus, right lingual gyrus, and thalamus have been identified in patients with early and late mild cognitive impairment and Alzheimer’s disease (Liang et al., 2014).

Other advanced imaging techniques include perfusion studies, commonly employing arterial spin labeling or phase-contrast imaging (Dolui et al., 2016). Perfusion studies evaluate cerebrovascular blood flow and can provide a surrogate marker for cerebrovascular function and health (Dolui et al., 2016; Wolters et al., 2017a). Cerebral blood flow is the rate of delivery of arterial blood to a capillary bed in the brain tissue per minute (Liu & Brown, 2007). Cerebral hypoperfusion is associated with a higher risk of developing dementia in the general population (Wolters et al., 2017b).

Molecular Imaging

Molecular imaging refers to the real-time visualization of biological processes at cellular and molecular levels in a given region. Examples of molecular imaging approaches include PET, single-photon emission computed tomography (SPECT), and Magnetic Resonance Spectroscopy. PET with radiotracers assesses the uptake distribution of a specific ligand to localize tissues with altered cellular or metabolic processes. For example, Fluorodeoxyglucose (FDG) radiotracer is used to evaluate glucose metabolism. In patients with amnesic mild cognitive impairment, PET using FDG has revealed bilateral glucose hypometabolism in several brain regions, including the limbic system, posterior cingulate cortex, parahippocampal gyri, and temporal lobes (Landau et al., 2011). Other ligands for β-Amyloid (Villemagne et al., 2013) and Tau (Schöll et al., 2016) proteins are used to evaluate and predict the conversion of mild cognitive impairment to Alzheimer’s disease. The Food and Drug Administration recently approved Tauvid radiotracer to clinically diagnose Alzheimer’s disease (Jie et al., 2021). This indication could transform clinical prognostication, assessment, and research for the aging brain. Thus, PET imaging should be considered a potential modality when designing studies that include neuroimaging measures.

SPECT is a type of perfusion imaging that uses an intravenously injected reagent, hexamethyl propylene amine oxime, to estimate regional blood volume flow via tracer uptake (Duncan et al., 1996). Decreased regional cerebral blood flow has been observed in specific brain regions in a broad spectrum of patients with cognitive decline. For example, in patients with depression and cognitive impairment, decreased perfusion was noted in the right thalamus, right lentiform nucleus, and the left medial temporal cortex (Staffen et al., 2009). Lastly, MR Spectroscopy measures biological metabolites, such as lactate, glutamine, glutamate, choline, creatinine, N-acetylaspartate and Myo-inositol in a region of interest or globally (Griffith et al., 2009; Öz et al., 2014). Whole-brain N-acetylaspartate was found to be significantly decreased in patients with cognitive impairment, and individuals with Alzheimer’s disease compared with healthy controls, and thus has been hypothesized to be an indicator for early Alzheimer’s Disease (Glodzik et al., 2015). Examples of other biologic measures that can be accessed via SPECT include glutamine (Rupsingh et al., 2011; Unschuld et al., 2012), choline (Kantarci et al., 2000), and Myo-inositol, which can be used in global or specific regions depending on the research question (Miller et al., 1993; Öz et al., 2014).

Nursing Research Implication

Neuroimaging measures can expand the evaluation of cognitive impairment and include those with severe cognitive impairment and others unable to participate in routine neuropsychological evaluation and subjective reporting. Incorporating neuroimaging measures allows the inclusion and participation of these individuals, bridging the known scientific gap and addressing the challenges in cognitive tests and patient reports. Neuroimaging measures, as described above, indirectly measures cognitive function and allow for consistency of evaluation across participants.

To date, limited research and behavioral interventions are in place to prevent or stabilize brain changes associated with cognitive decline. As such, an opportunity to harness the expertise of nursing scientists in biobehavioral and social science research to develop interventions that will provide stability of cognitive changes and preservation of function among individuals at risk for progressive neurological decline. Nursing scientists have long been leaders in evaluating the intersection between the environment (societal, structural, social, as well as behavioral factors) and the individual’s health (i.e., progression of symptoms, functionality, quality of life, and overall well-being). With neuroimaging measures, nursing scientists can elucidate how, for example, certain behaviors (i.e., physical activity, diet, work, social determinants of health) influence the progression of brain changes before the onset of symptoms or progression of symptoms. Nurse scientists can also investigate if certain cognitive stimulations (e.g., music therapy, speech therapy, mindfulness) induce brain changes that facilitate stability or improve cognitive symptoms. Lastly, nurse researchers can strongly advocate for program planning, community resources, and policy change for the potential impact of their interventions at the physiological level among all individuals across lifespans.

Discussion

Clinical neuroimaging is a valuable data source for nursing scientists to understand the pathophysiology underlying cognitive impairment and brain aging. While clinical images are limited compared to ones obtained explicitly for research purposes, the use of clinically obtained studies decreases the participation burden and study costs. These neuroimages can be qualitatively examined, processed, and segmented for research purposes. Qualitative examination or visual scoring refers to an expert in the field examining and scoring the characteristics of the brain. Quantitatively interrogating images refers to segmenting and measuring the voxel signal intensity using manual, automated, or semi-automated methods.

The use of neuroimaging modalities is not without disadvantages. Accessibility and availability of imaging techniques such as MRI and PET are scarce in rural communities (Khaliq et al., 2014), requiring patients to travel long distances for diagnostic evaluation and/or research participation. Similarly, required technical expertise in image acquisition and interpretation by expert neuroradiologists are also limited in rural and underserved communities (Barreto et al., 2021; Culler et al., 2006; Khaliq et al., 2015), exacerbating these disparities. Insurance coverage also poses an issue in performing these modalities. When using neuroimaging modality for the sole purpose of research, the cost is an important consideration as this requires a substantial budget. MRI machines also use magnets and are thus contraindicated in patients with a magnetic implantation device. And lastly, qualitative scoring and quantitative segmentation methods to extract the data have their weaknesses, as described in this review.

Nursing is at the forefront of advancing health prevention, promotion, and healthy aging research with a particular focus on health equity, addressing upstream factors impacting health outcomes across a population’s lifespan. Nurse scientists are proficient and well-versed in using performance-based examinations and subjective reports to evaluate cognitive changes. While some nurse scientists have spearheaded the use of neuroimaging in their research, they remain limited in number. Nurse researchers can become more facile in using these measures both in qualitative and quantitative methodology. Integrating and leveraging previously gathered neuroimaging clinical data for research purposes can better characterize the associations between environmental factors, symptom progression, disease risk, and health outcomes. This scientific foundation will allow for the development of biobehavioral and social interventions inclusive of all patients, regardless of the severity of their cognitive impairment. Neuroimaging can bridge the limitations of performance-based cognitive exams and subjective patient reports and allows for inclusion, evaluation, and understanding of complex neurological and severe cognitive impairment among a diverse aging population.

ORCID iDs

Karl Cristie F. Figuracion https://orcid.org/0000-0003-1360-5749

Hilaire J. Thompson https://orcid.org/0000-0002-5472-478X