White Matter Differences Between Mild Cognitive Impairment Patients and Control Patients
Clinical Neuroscience | Milly Darragh
Alzheimer’s Disease is an increasingly common condition, and is often identified too late for patients. How can micro-structural changes influence our understanding in the early stages of cognitive decline, and what role will biomarkers play in this?
Introduction
Mild cognitive impairment (MCI) is a condition in which an individual experiences cognitive decline at a more rapid rate than ageing, but has not yet progressed to the severity of Alzheimer’s disease (AD) [1]. It is characterised by a mild decline in memory and cognitive ability, and affects approximately 6%–8% of the population at any given time [2]. MCI patients have a significantly higher rate of conversion into clinical dementia when compared to control conversion rates, observed at 10%–15% and 1%–2% respectively [3]. Some patients with MCI will progress to dementia, but there are currently no accurate ways to predict whether or not a patient will convert to more severe symptoms and diseases [4]. Current research aims to address this issue by investigating possible biomarkers via neuroimaging methods [5] and has yielded promising results. Amid this uncertainty is a consensus that white matter reduction occurs with MCI. White matter abnormalities have been noted in previous studies exploring MCI, but definitive correlations are still being explored within the literature [8]. White matter itself is subcortical, consists of axons and myelin, and is heavily involved with cognition, processing, memory, and intelligence [9]. When white matter reduction occurs, the death of neurons results in decreased cognition, memory, and neural health [10].
Previous research has suggested that the fornix is involved in memory functions, thus playing a part in MCI [11]. The fornix itself is a c-shaped white-matter tract working as part of the basal ganglia system and is closely associated with the hippocampus and cingulum [12]. The fornix is heavily involved in memory and cognition, with studies showing that fornix transections and damage are linked to amnesia and episodic memory impairment [12]. Furthermore, autobiographical memory has been enhanced when bilateral deep brain stimulation occurred to the limbic system, again suggesting the fornix’s role in memory and processing [13]. Previous studies have analysed how the fornix may be used to identify and monitor MCI and AD progression in patients, with significant fornix abnormalities found in MCI patients compared to controls [14].
Another region of interest for MCI white matter integrity is the cingulum, a white matter fibre tract involved in connecting the lobes of the brain [14]. Atrophy of the cingulum has been shown in MCI and AD patients when compared to control participants, as have links to the hippocampus atrophy experienced by many MCI and AD patients [15]. Higher degrees of posterior cingulum atrophy have also been observed in memory impaired patients when compared to functioning memory patients, further suggesting the cingulum’s role in memory and its possible interactions with MCI pathology [16].
Voxel-based analysis (VBA) is currently the predominant method for neuroimaging research and diagnoses [17]. However, there are limitations to understanding white matter organisation with VBA; the immediate concern is the inability to correctly reproduce crossing fibres in white matter, leading to spurious results and understanding—especially in more complex structures [18]. This issue occurs when fibres cross within a single voxel, meaning the orientation of said fibres is no longer individual but can easily be misinterpreted as such [19]. Behrens et al. [20] found that approximately one-third of all white matter voxels comprised at least one fibre population, suggesting that the VBA dilemma may have had significant effects in both previous and present studies that use this method.
Secondly, the partial volume effect can cause notable issues when using VBA. This effect occurs when more than one tissue type appears in a voxel. Intensity then becomes dependent on the proportions of each tissue type in a voxel, causing issues with volumetric output [17]. This creates an average output of the many cells within the voxel and is unrepresentative of the numerous components comprising a true voxel. Ignoring this effect may produce significant measurement estimation errors of white matter.
These disadvantages were addressed by the production of a new method that would allow for correct representation with crossing fibres and multiple tissue types: fixel-based analysis (FBA) [21]. ‘Fixel’ refers to fibres within a population of a voxel, and an FBA uses spherical deconvolution to allow for accurate and consistent analysis measures of white matter crossing fibres [21]. However, this method presents its own set of problems. FBA results fail to accommodate their bias towards positive results, hence increasing the rate of type I errors [22]. This claim is based upon FBA’s increased specificity showing a clear preference for larger fibre bundles, therefore often overestimating the significance detected in data. Furthermore, recent research suggests that the complexity of white matter fibres is not necessarily an issue with VBA that can be fixed with FBA, but rather an issue of diffusion MRI (dMRI) itself [18]. Another considerable challenge with FBA is its increased complexity and the extra steps required to use it [21, 24]. Both VBA and FBA have their own advantages and disadvantages, yet voxel-based approaches still remain the most widely used and accepted technique within neuroimaging literature [23].
Two of the leading measures in neuroimaging research are Fractional Anisotropy (FA) and Mean Diffusivity (MD). FA refers to a value between 0 and 1 produced by measuring the diffusion directionality of water molecules in white matter, in which a value of 0 refers to isotropic diffusion and a value of 1 refers to anisotropic diffusion [24]. FA values closer to 0 represent a general loss of white matter via decreased axonal myelination, while FA values closer to 1 represent healthier axons and, therefore, more white matter present [24]. Mean Diffusivity (MD) refers to the overall diffusion observed in neurons. A high MD value would therefore suggest more space for diffusion to occur—indicating loss of matter. In turn, a low MD value would imply little room for diffusion and a high number of functional axons [24]. Sexton et al. [25] investigated the white matter abnormalities in patients with MCI and noted decreased white matter consistent with previous studies [31, 32] via increased MD and decreased FA values compared to control patients. Furthermore, recent findings support these results by confirming decreased FA and increased MD values in the fornix and cingulum of aMCI patients [27]. Specifically, the relationship between the fornix and hippocampus has been reviewed in literature, as AD patients with hippocampal atrophy have been reported to present decreased FA values of the fornix [28]. MCI patients experienced significant FA decreases of the cingulum when compared to control patients, and AD patients experienced significant FA decreases when compared to MCI patients and control patients—which conforms with the progressive nature of these diseases [29]. Interestingly, FA has been found to be highly sensitive to any structural changes, but has little ability to determine the type of change occurring [30].
MCI is clearly prevalent in wider populations, and expanding our knowledge of the underlying mechanisms occurring can provide significant insights into our understanding of the disease. By exploring the components of MCI successfully observed using FSL (a software library for MRI data), we may be able to strengthen predictive prognoses for patients who experience rapid cognitive decline. We can expect to see decreased FA and increased MD in patients diagnosed with MCI based on previous studies and our current understanding of the relationship between MCI and neuronal atrophy.
Participants:
There were 30 participants in this project, all recruited via the Dementia Research Prevention Clinic (DPRC), of which 15 were control patients and 15 were MCI patients. The aMCI subgroup consisted of 10 female subjects and 5 male subjects, with a mean age of 72.8 years (SD = 6.3). The control subgroup consisted of 3 female subjects and 12 male subjects, with a mean age of 67.0 years (SD = 7.6). Addenbrooke Cognitive Examination III (ACE-III) (Beishon et al., 2019) scores were obtained from participants in both groups. The aMCI group had a mean ACE-III score of 85.5 (SD = 7.0), and the control group had a mean score of 94.7 (SD = 3.6). The difference in these values was significant (p < .001). Estimated Total Cranial Volume (eTIV) was also calculated for both groups, with a control group mean eTIV of 1470348.2 (SD = 92448.8) and an aMCI group mean eTIV of 1575569.1 (SD = 158009.0). Statistical significance was also found with differences in eTIV scores (p = .034). Intracranial volume measurements were also taken between groups, but no significant difference was found (p > .05).
Apparatus/measures:
Diffusion Tensor Imaging (DTI) refers to the type of method used to measure diffusivity and composition within neural structures [30]. The data from these patients were accessed from the DPRC as part of Brain Research New Zealand. Diffusion-weighted images were provided as data after Magnetic Resonance Imaging (MRI) had occurred on all participants. The data was acquired using 3 shells b0 s/ mm₂, b1000 s/mm₂ and b2000 s/mm₂ , in 105 diffusion volumes with a b0 s/mm₂ volume as volume 1, 22, 43, 64 and 85. Preprocessing of the images used the FSL tool ‘fsleyes’ to inspect our data, then continued to remove sources of noise within our data. This process included distortion or warping effects that may occur within our diffusion data.
Tract-Based Spatial Statistics (TBSS) is a methodology that was designed to improve the specificity and interpretability of dMRI studies [31]. TBSS follows non-linear registration, in which all FA data images are aligned to an anatomical reference which consists of a previously decided target [31]. Then, an average of FA images in the data set is produced and thinning occurs. The second step is the projection onto a pre-existing anatomical reference, which is formed by the mean FA values in the data [31]. This allows for differences to be seen within subgroups of datasets.
TBSS provides a more sensitive visualisation and analysis of white matter structure changes, which previous studies have displayed when comparing with other analysis tools such as Statistical Parametric Mapping [32].
Methods
Results
This research project aimed to assess the use of FSL as a reliable and concise method of observing MCI white matter abnormalities and has successfully suggested the use of FSL as a comprehensive image analysis method. This research has also examined the involvement of the fornix fimbriae with white matter abnormalities in MCI patients and found consistent decreases of white matter in MCI patients compared to control patients.
As shown in our results, significant white matter changes between the aMCI group and the control group were observed. These differences are reflected in the purple-coloured tracts of our results, which are present in a range of areas. Noticeably, the fornix fimbriae experience a significant difference in white matter tracts. This aligns with previous knowledge suggesting their role in memory and cognition, as decreased fornix fimbria integrity has been observed with patients experiencing aMCI.
Specifically, the fornix fimbriae have recorded involvement with spatial learning and memory, with evidence that damage/removal of the fornix fimbriae causes an inability to complete spatial memory tasks in rats [33]. These findings were expanded when Gaffan and Parker [34] showed episodic memory was severely impaired in monkeys after a unilateral fornix-fimbriae transection. This experiment also observed the role the fornix fimbriae may play in processing visual information in episodic memory and learnt skills. Furthermore, forn- fimbriae transection surgeries interrupted spatial and episodic memory in rats [35]. These studies reflect the link in literature between damaged fornix fimbriae and decreased or absent memory and cognition. From this literature, studies turned to how this knowledge could translate to clinical MCI or AD, and whether decreased fornix fimbriae white matter in MCI patients can be approached with the same understanding. Recent research has shown that decreased fornix fimbria integrity (specifically FA) was present in aMCI patients, but not naMCI patients [14]. Again, this would imply that part of aMCI patients’ symptoms could be due to decreased fornix fimbriae integrity. MCI patients have also been recorded to have significant atrophy within limbic pathways compared to healthy ageing counterparts, and white matter degeneration has been suggested to occur in MCI patients prior to grey matter degradation [11, 42].
Another region of interest in this project was the cingulum, which experienced significant white matter changes as well. This area of the brain has been associated with memory processing before, specifically episodic memory [37]. The cingulum bundle has shown clear cognitive detriment in traumatic brain injury patients, who experience memory and episodic verbal learning difficulties [38]. Furthermore, decreased FA was observed in the cingulum bundle in these patients using the DTI methodology. Further work implicating the cingulum in memory and cognition describes decreased object recognition and object location skills in rats with cingulum lesions, as well as decreased cingulum integrity in older healthy adults associated with decreased FA [45, 46].
However, the role of cingulum integrity in aMCI patients has only recently been explored. [40] explored the relationship between cognitive decline in aMCI patients and the severity of cingulum tract atrophy. Interestingly, cingulum bundle integrity was able to predict cognitive impairment and memory skills in aMCI patients. Furthermore, Chang et al. [41] found cingulum disruption correlated with cognitive impairments across types of MCI, stating that multiple-domain aMCI and single-domain aMCI were both significantly impaired on all memory tasks.
Additionally, significant white matter differences were observed in areas such as the superior fronto-occipital fasciculus and the uncinate fasciculus. The superior frontooccipital fasciculus has been noted in spatial awareness and processing cognition in prior studies [42]. This tract runs from the frontal lobe to the occipital lobe, and a significant decrease in this tract was observed in our data. The uncinate fasciculus is also a white matter tract, running from the frontal lobe to the temporal lobe. This tract has assumed declarative memory involvement in human cognition [42]. Declarative memory refers to the conscious retrieval of experiences and information, which aligns with symptoms of aMCI [50, 51]. While these findings are consistent with the expected pathology of aMCI patients, it is surprising to observe these effects using a rather conservative method. It is important then, to consider factors that may have contributed to these findings, which may suggest slight biases or amplification of results.
Our subject group was predominantly female (n = 17) for both control and MCI patients, which may have influenced our results. Gender and sex differences have been observed and reported widely in literature—with varying explanations. MCI has been suggested to affect male populations more than female populations in prevalence. However, it is well established that females experience AD at a significantly disproportionate rate when compared to males [52, 53]. Furthermore, there have been multiple reports of female patients with MCI experiencing faster cognitive decline and neural atrophy related to MCI and AD [45]. This has been attributed to the disproportionate healthcare females often experience when compared to their male counterparts and suggests that the reason more rapid rates of conversion to AD occur in women is potentially due to the cultural and social factors regarding women’s healthcare [46]. Since the subjects were primarily female for the MCI patient group, this may have skewed the symptoms and anatomical abnormalities towards higher severities. This could imply that the degree of significance observed in the results is due to the above-average severity present in our sample when compared to the general population. Furthermore, the gender imbalance in the participant populations was reflected through other measures. Demographic measures showed a significant difference of eTIV between male and female participants (p < .001). Gender differences in eTIV have been well established in neuroscientific research, with males displaying significantly higher eTIV values than females [47]. The effects this may have in our project are likely reflected in our results, with disproportionate gender ratios in both sub-groups.
This project used TBSS as part of FSL software during our analysis, which is traditionally considered a conservative method to analyse any data. However, using TBSS as part of FSL did not seem to face the issues that an overly cautious analysis may face, as we still gained significant expected results.
Being able to correctly identify patients with MCI via biomarkers (such as the neuroimaging data presented in the project) could provide opportunities to diagnose and treat MCI in the where treatment is far more effective. Furthermore, using biomarkers to identify MCI could allow for diagnosis before severe symptoms set in, as well as increase our understanding of how MCI mechanisms occur in different sections of the brain. There are many calls for more biomarker-based studies of MCI and progressive neurodegenerative diseases, with hopes this could become a screening tool or objective diagnosis technique [5]. Mito et al. [5] also considered Diffusion tensor imaging (DTI) and FT as reliable approaches in neurodegenerative biomarker studies. DTI uses anisotropic diffusion to analyse and observe white matter tissue and tracts, providing insight into cerebral organisation [6]. Once this data has been collected, FT can be used to reproduce and analyse DTI data using 3D modelling. This method has been used to identify diseases affecting the nervous system and in exploratory research of conditions like MCI, AD, and dementia [7]. By using a fibre-tract-specific model instead of a diffusion tensor model, we are able to analyse white matter tracts that contain multiple fibre populations and crossing fibres. This also allows us to use more measurements that are fibre specific, such as fibre density (FD) and fibre cross-section (FC) measurements. While this method can successfully explore the mechanisms of neuronal atrophy relative to neurological diseases, there are still limitations that come with using DTI and FT, related to the assumptions this method holds.
Despite conflicting literature regarding the ability of voxel-based imaging techniques to accurately analyse complex fibres, this project has shown that this method can be effective and did not express any issues with complex fibres.
Discussion
This research project was supported by Brain Research New Zealand and the Dementia Prevention Research Clinic, which provided all data, participants, and patients. This research project was supervised by Professor Ian Kirk in the School of Psychology and was submitted to the Faculty of Science in partial fulfilment of the Bachelor of Advanced Science (Honours) program.
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Milly Darragh - BAdvSci (Hons), Cognitive Neuroscience
Milly is entering her 4th year of the Bachelor of Advanced Sciences (Honours) programme in cognitive neuroscience. As an honours student she is fascinated by translational neurology and neuroscience, specifically neurodegenerative diseases. She is the current vice-president of Scientific, and the president of UoA Campus Neuroscience Society.