Quantitative magnetic resonance imaging in Alzheimer’s disease: a narrative review

Introduction

Alzheimer’s disease (AD) presents one of the most significant challenges in global health, being the leading cause of dementia in the elderly population (1). Worldwide, AD accounts for 60–80% of all cases of dementia (2,3). In the US, the number of people over 65 years of age affected by Alzheimer’s dementia is estimated at around 6.7 million (2), significantly impacting patients’ lives, their families and the healthcare system as a whole. It is projected that by 2060, the number of people over 65 years of age with Alzheimer’s dementia will reach 13.8 million (4). AD typically results in a prolonged illness characterized by severe disability and dependence, further contributing to the public health impact of the disease. The global impact of AD is becoming progressively more relevant due to the worldwide population aging, making it a crucial field for research. For this reason, the interest in AD research keeps growing, with the Funding for AD, including AD related dementias (ADRD), almost reaching 4 billion dollars in 2023 (5).

The clinical diagnosis of AD is an intricate process that involves various factors, including the patient’s medical history, clinical tests, neuroimaging, and biomarkers. However, a fully certain diagnosis is challenging due to the numerous co-morbidities often linked with dementia, such as cerebrovascular disease, cerebral amyloid angiopathy, hippocampal sclerosis (HS), and other region-specific findings (6,7). Additionally, the presence of different pathologies associated with other dementias, such as Lewy body dementia, further complicates the diagnosis. Therefore, the variability of the clinical conditions associated with AD can make clinical diagnosis extremely challenging, especially in the early phases, showing a low concordance with post-mortem pathological assessment (8). A definitive diagnosis of AD can be established only post-mortem, by looking for Beta-amyloid plaques, neurofibrillary tangles (NFTs) and neuritic plaques (9,10).

While AD has traditionally been regarded as a grey matter (GM) disorder, studies have indicated that myelin damage is a central feature in AD and may even precede the development of Aβ and tau pathologies (11-13). Myelin damage has been found to occur prior to the deposition of Aβ plaques and NFTs in the brains of murine models of AD (14,15). One recent study by Couttas et al. (13) revealed a defect in ceramide synthase 2 activity—an enzyme involved the biosynthesis of myelin lipids in the preclinical stages of the disease, e.g., Braak stage I/II within the temporal cortex, which suggests that myelin dysfunction precedes amyloid pathology. There is also evidence that tau protein hyperphosphorylation may occur later, during the remyelination process (16).

The relationship between white matter (WM) and GM changes has been extensively investigated. Myelin basic protein (MBP), which is a structural protein component of myelin, has been proven to have a regulating role in the deposition of Aβ 42 by preventing the formation of amyloid plaques in the brain (17,18). On the other hand, the deposition of Aβ has been shown to induce oligodendrocyte death and inhibit myelin formation (19-21). There is a strong association between decreased MBP levels and increased Aβ in the brain tissue of AD patients (22).

Neuroimaging, particularly magnetic resonance imaging (MRI), serves as a crucial tool for in vivo diagnosis. It enables clinicians to make an early diagnosis and choose the most appropriate therapeutic path to prevent or delay cognitive decline. The literature widely supports the pivotal role of clinical MRI (with a magnetic field between 1.5 T and 3 T) in detecting most pathological changes related to AD before the patient’s death, showing high sensitivity and moderate specificity (8,23).

Cerebral atrophy in the antemortem brain appears to be the strongest indicator of underlying AD neuropathology along with other incidental neurodegenerative findings such as HS and TDP-43 encephalopathy (8,24). White matter hyperintensity (WMH), on the other hand, exhibits a better correlation with concomitant cerebrovascular disease observed at autopsy (8). International guidelines recommend using MRI in the clinical context for patients with dementia and mild cognitive impairment (MCI) to rule out reversible brain abnormalities, such as tumors or subdural hematomas (25). MRI can also occasionally provide important information concerning vascular pathology or the pattern of cortical atrophy (26). Positron emission tomography (PET) has shown higher specificity than MRI, but MRI is cheaper and noninvasive, which makes it a desirable substitute for nuclear medicine (27). A recent study involving patients with cognitive impairment and AD has shown associations between brain MRI phenotypes and single nucleotide polymorphisms (SNPs) (28).

Diffuse atrophy is the main MRI feature in AD, with the entorhinal cortex and the hippocampus being the two most common regions of interest in both in vivo and post-mortem investigations into AD pathophysiology (29). Although its ability to predict cognitive deterioration in MCI patients either alone or in conjunction with other metrics is still being explored, structural MRI might not have enough specificity to distinguish AD from other disorders (30). However, a visual qualitative assessment of MRI is insufficient to estimate the rate of tissue loss in affected areas, necessitating quantitative measures for accurate assessment. Additionally, the human eye cannot perceive the minimum degree of atrophy, and without quantitative measurements, image evaluation becomes subjective (29).

Although hippocampal volumetry is the most widely studied structural MRI biomarker of AD (31), recent studies suggest texture analysis has advantages over volume and shape approaches, and it may be used to detect microstructural changes earlier with better accuracy (32-34).

In recent decades, quantitative MRI (qMRI) has revolutionized neuroimaging, providing a detailed and advanced approach to studying AD. It allows for the investigation of the complex and subtle changes in the brain involved in the AD pathogenetic process, leading to improvements in diagnosis, follow-up, and the identification of new therapeutic targets. In this review, we aim to summarise the qMRI techniques which can noninvasively measure a variety of parameters, including T1, T1ρ, T2, T2*, susceptibility, magnetization transfer ratio (MTR), myelin water fraction (MWF), and non-aqueous myelin proton density (PD), which are thought to represent different pathophysiological elements of brain damage and may have distinct functions at different phases of the disease. Moreover, these parameters, being quantitative, are useful for statistical comparisons and longitudinal studies, offering the opportunity to analyze a vast amount of new data. We present this article in accordance with the Narrative Review reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1602/rc).

Methods

This review conducted between April and May 2024 aimed to provide a thorough description of the current qMRI studies in AD. The literature search was performed in PubMed, Web of Science, and Google Scholar databases for studies published between 1990 to 2024. The results were screened first automatically and then through title and abstract reading to exclude non quantitative studies, volumetric analyses, non-English language reports, non-peer reviewed studies and animal studies. Table 1 summarises the search strategy.

Discussion

T1

T1 relaxation time is also known as spin-lattice relaxation or longitudinal relaxation time, which characterizes how quickly the longitudinal magnetization (M_z) recovers back to 63% of its initial value after rotation into the transverse plane. T1 relaxation time changes are related to water content, amyloid burden, iron load, and myelin loss, among other factors; hence, inconsistent T1 results have been reported in AD patients (35-37).

Gouw et al. (38) performed a histologically correlated study on ex-vivo brain samples using a 3D fast low-angle shot (FLASH) for T1 quantification of WM. In AD brain samples, both normal-appearing white matter (NAWM) and WMH exhibited higher T1 values compared to control samples for NAWM and WMH, respectively. The difference between T1 of NAWM and WMH was higher in AD brains than in controls. These changes were likely due to microglial activation, which was demonstrated pathologically and is an independent factor causing T1 increase. The T1 times were also independently related to axonal loss and myelin loss.

Su et al. (39) used an inversion recovery based echo planar imaging (EPI) sequence to study GM changes in AD. They found a reduction in T1 values in bilateral temporal and parietal lobes, hippocampus, and basal ganglia. The T1 values in the AD group increased over the 12-month follow-up period. Lou et al. (40), on the other hand, used a novel synthetic MRI sequence and found similar results for the caudate nucleus but increased T1 values in the right insular cortex. Figure 1 illustrates the steps involved in the post-processing and measurement (40,41) of synthetic MRI data. A recent study by Parent et al. (42) argued that T1 was not a sensitive measure of microstructural tissue alterations and its clinical relevance was limited. However, these studies were conducted with limited sample sizes, emphasizing the need for additional research to provide a clearer understanding.

Figure 1 Schematic diagram of post-processing and measurements of synthetic MRI data. To obtain brain regional relaxation values, parcelation in subject’s native space using FreeSurfer1 with Destrieux atlas (41) based on the 3D-FSPGR images was performed. After rigid registration between 3D-FSPGR and synthetic MRI images using MATLAB R2016a (MathWorks, Natick, MA, USA) and SPM12 brain regional T1, T2, and PD values were extracted. The figure was reused from an open access article (40), under the terms of the Creative Commons Attribution License 4.0 (CCBY). 3D-FSPGR, 3 dimensional fast spoiled gradient recalled echo; CSF, cerebrospinal fluid; GM, grey matter; MAGiC, magnetic resonance image compilation; MRI, magnetic resonance imaging; PD, Parkinson’s disease; WM, white matter.

T1

T1ρ relaxation time describes spin-lattice relaxation in the rotation frame in the presence of an external radio frequency (RF) pulse (also called spin-lock pulse), which is applied along the magnetization after RF excitation. The spins are considered stationary relative to this external RF field in the rotating frame and undergo slower relaxation than T2 but faster than T1. T1ρ is sensitive to low frequency motional processes (100 Hz to a few kHz) and provides useful information about macromolecular composition, which is not directly studied by T1 and T2 imaging (43). T1ρ displays a greater range of values than T2 in the human brain (44).

Borthakur et al. compared the medial temporal lobe region of AD and control brains and found increased T1ρ relaxation times for white and GM (45). The same group also reported 10% higher T1ρ values in the hippocampus of patients with AD as compared to age-matched controls (46). Figure 2 compares T1ρ maps in the brain of a healthy volunteer and an AD patient. Combining T1ρ with cerebrospinal fluid (CSF) biomarkers like T-tau, P-tau, and Aβ-42 improves the prediction rate for AD and MCI. Compared to CSF markers (77%), T1ρ showed higher sensitivity (82%) in delineating AD from controls (47).

Figure 2 T1ρ maps of brain of control (61-year-old female) and AD (57-year-old female) brain. Pixels with higher T1ρ are more prominent in AD patients especially in the medial temporal lobe region as indicated by the white circle on the 57-year-old AD patient. The absence of T1ρ signal from CSF indicates that the higher T1ρ values are not due to fluid. This figure was reused from (45) with permission. AD, Alzheimer’s disease; CSF, cerebrospinal fluid.

Another unique feature of T1ρ MRI is its sensitivity to pH (48-51). Multiple studies have demonstrated that higher T1ρ relaxation times indicate a lower pH, signifying increased acidity. The pathological cascades associated with the development of AD, like vasculopathy, amyloid pathology, tau pathology, metabolic disturbances, oxidative stress, etc, have a common element: acidosis. T1ρ, thus could possibly serve as a biomarker reflecting a composite picture of the different pathologies involved. In their study, Boles Ponto et al. found that a higher global T1ρ was associated with amyloid burden, abnormal cerebral glucose metabolism, and poor memory and cognitive function (52).

Various factors, including field inhomogeneities, influence quantitative imaging, and it is imperative to minimize image artifacts to ensure accurate quantification. In a recent study by Wang et al. (53), high test-retest repeatability for T1ρ measurements in the human brain in vivo was demonstrated, which suggests that T1ρ may be a reliable technique for quantitative brain analyses within clinically relevant scan times. However, it is important to note that the study involved a moderate-sized cohort of 30 patients and utilized a spin lock frequency of 300 Hz, as opposed to the more commonly used 500 Hz. As a result, further research is necessary to validate and assess the repeatability of T1ρ techniques.

T2

T2 relaxation time is also known as spin-spin relaxation or transverse relaxation time. It characterizes how quickly the transverse magnetization (M_xy) decays to 37% of its initial value in the magnetic resonance system. Quantitative T2 relaxometry analysis was one of the earliest approaches to studying AD quantitatively.

Multiple studies have found increased T2 values in the hippocampus (54-58). The hippocampus is the site of early pathological changes in AD (59). It is closely associated with cognitive functions in the brain, including memory and neurogenesis. Neuropathological abnormalities in AD include neuronal loss and gliosis in the hippocampus (12). The major pathophysiological changes described in AD are the accumulation of amyloid beta, phosphorylated tau, and neuroinflammation. The neurodegeneration starts in the entorhinal cortex of the hippocampal formation and gradually extends into the temporal cortex, frontoparietal cortex, and subcortical nuclei (60). Over time, this leads to disruptions in the normal brain pathways responsible for learning and memory deficits. The increase in T2 can be explained by multiple pathological changes in AD, namely loss of pyramidal neurons, gliosis or glioma, and increased CSF in the atrophied region. However, early quantitative T2 studies used mono-exponential analysis to study the region, assuming a CSF-free pixel. This assumption may not be valid in populations with cerebral atrophy, such as healthy elderly and AD patients, where CSF constitutes a more significant portion of the total intracranial volume. Even if appearing to contain only tissue, the pixel may have a small but significant CSF signal component. Haley et al. (61) performed a biexponential spectroscopic T2 analysis to separate the tissue (hippocampus) and CSF signals to better study the effect of AD on T2 values in the hippocampus. They reported a shortening of T2 values with age and AD, as shown in Figure 3. Subsequent studies by other groups have reported similar findings (36,39,62). The reported increase in T2 in the older studies was probably due to the presence of CSF, which led to signal averaging of tissue and CSF T2 values, resulting in greater T2 values than true brain tissue. However, it is also well known that AD is associated with iron accumulation (63-65). Tissue iron accumulation is associated with extracellular amyloid plaques (66-68) and intracellular NFTs (69,70). Schenck et al. (71) performed an iron extraction study in which they measured T2 values in the hippocampus before and after iron extraction and found a global increase in T2 values in the brain, including the hippocampus. Therefore, it is these two competing factors, the mobile water component and the iron, that determine T2 values in the hippocampus.

Figure 3 Box-and-whiskers plot of hippocampal T2 relaxation times using magnetic resonance spectroscopy. T2 values decreased with age and AD (whiskers mark the outermost value within 2.5 SDs). The figure was reused from (61) with permission. AD, Alzheimer’s disease; HE, healthy elderly adults; HM, healthy middle-aged adults; HY, healthy young adults; SD, standard deviation; N, sample size; X_age, mean age.

Susceptibility

Magnetic susceptibility is a macroscopic physical property that describes the tissue’s own magnetization when placed in an external magnetic field. The magnetic susceptibility of brain tissue is dominated by four major constituents: water, myelin, iron and calcium (72). Diamagnetic myelin and paramagnetic iron are the dominant sources of susceptibility contrast. Myelin is the insulating sheath surrounding nerves in the brain and spinal cord. It is composed of about 40% water, and the dry mass comprises 80% lipids and 20% protein. The lipids and proteins in the myelin sheath are major contributors to its diamagnetic susceptibility. Iron in the brain is regulated by iron regulatory proteins (IRPs)—transferrin (Tf), transferrin receptor 1 (TfR1), ferritin, ferroportin 1 (FPN1), divalent metal transporter 1 (DMT1), etc. (73). In addition to these proteins, APP and tau also act to regulate iron. APP interacts with FPN to regulate the efflux of ferrous ions (74). APP knockout mice show increased levels of iron deposition and oxidative stress in cortical neurons (75). Tau functions as an intracellular microtubule-associated protein, facilitating the transportation of the produced APP to the cell surface, thereby promoting iron output (76). Notably, mice lacking tau exhibit age-dependent iron accumulation and brain atrophy. Additionally, the reduced surface trafficking of APP in primary cultured neurons leads to iron retention, suggesting that tau-mediated iron homeostasis might depend on APP (77,78).

Another key factor, recently explored in more detail, is the permeability of the blood-brain barrier (BBB). Through BBB imaging, researchers have found that increased BBB permeability may contribute to higher iron and Aβ accumulation in the brains of individuals with Alzheimer’s (79). Uchida et al. (80) in their study on iron deposition in brain measured using quantitative susceptibility mapping (QSM) and BBB function measured using diffusion-prepared pseudo-continuous arterial spin labeling were able to demonstrate relationship between brain iron dynamics and BBB function in children. Also in another study, Uchida et al. (81) found that increased APO ɛ4 dose (APOE ɛ4 non-carriers>heterozygotes>homozygotes) is associated with decreased effective brain-waste clearance, such as iron and β-amyloid, through the BBB.

When compared to traditional contrast weightings, T2* mapping, and susceptibility weighted imaging (SWI), QSM provides a superior contrast to noise ratio (CNR) and quantitative measurement of iron and myelin without dependence on orientation and non-local effects (82). Iron accumulation increases R2* and QSM. Breakdown of intact myelin decreases R2* but does not change QSM, whereas degradation and removal of myelin increase susceptibility measured on QSM. QSM values do not represent absolute magnetic susceptibilities and must be interpreted as a relative measure that must be normalized to a reference region.

Acosta-Cabronero et al. (83) first described a complete framework for magnetic susceptibility measurements in AD patients and found increased QSM in the putamen of patients compared to controls. The statistical effect of QSM data in separating AD from controls in the putamen was as strong as that of hippocampal atrophy. This study, although having a small sample size, demonstrated the potential of QSM as an MRI biomarker. QSM can be used to differentiate AD from healthy controls (84) as well as MCI from AD (85). Gong et al. successfully showcased the diamagnetic properties of Amyloid beta and tau proteins through both phantom and ex vivo experiments. The negative susceptibility map for beta-amyloid and tau, contrasting with the positive susceptibility of iron, offers a histology-like image contrast (86). Nevertheless, the co-localization of iron and beta-amyloid may introduce contrasting effects on QSM maps. Furthermore, the limited ability of clinical MRI to achieve the ultra-high resolution utilized in this study diminishes the specificity of susceptibility maps when employed as a tool for macromolecular quantification in clinical settings. Cogswell et al. (87), in their study on a relatively larger sample size, found increased susceptibilities in deep and inferior gray nuclei-pallidum, putamen, substantia nigra, and subthalamic nucleus. Higher susceptibility was significantly associated with higher amyloid PET standardized uptake value ratio (SUVr) in the pallidum and putamen and higher tau PET in the basal ganglia, which had the largest effect size in the pallidum. The cortical regions did not consistently show disease related changes in QSM. The iron levels associated with cortical amyloid plaque and neurodegeneration were of insufficient quantity to be detected in vivo with QSM, or there may be local factors, including amyloid deposition, counteracting the anticipated susceptibility changes from local iron deposition. The greater effect sizes in the deep and inferior nuclei were likely based on the greater dynamic range in these regions. Figure 4 shows comparison of QSM and histological stainings of amyloid-β, tau, and iron in the frontal cortex of a control and AD patient.

Figure 4 Comparison of QSM and histological stainings of amyloid-β, tau, and iron in the frontal cortex of a control (A) and AD patient (B). (A) Control: QSM shows hyper-intense paramagnetic susceptibility at gray-white matter boundaries that match the iron staining (red arrows). Cortical gray matter shows low staining for amyloid-β, tau, and iron and has a susceptibility near zero. (B) AD patient: QSM shows hyper-intense paramagnetic susceptibility at the gray-white matter boundaries that match the iron staining (red arrows). One cortical gray matter region shows moderate amyloid-β staining (yellow triangle) and low tau staining, which has diamagnetic susceptibility. The figure was reused from (86) with permission. AD, Alzheimer’s disease; GM, grey matter; QSM, quantitative susceptibility mapping; WM, white matter.

MTR

Magnetization transfer (MT) refers to the transfer of spin magnetization from macromolecular protons to water protons. In MT imaging, a saturation pulse is placed at a frequency offset Df) away from the water peak to saturate macromolecular protons, which exchange with water protons via chemical exchange or MT, significantly reducing the detectable signal. A simple way to quantify the MT effect is to measure MTR, which has been used for indirect assessment of macromolecules with restricted motion and extremely short T₂s.

MTR has been employed for noninvasive evaluation of many neurological diseases including AD. Hanyu and colleagues initially examined the hippocampus in dementia patients using MTR (88). They found that MTR measurements could offer greater specificity than visual analysis in identifying structural impairment of the hippocampus in individuals with AD. The mean MTR levels in the hippocampus and temporal lobes have been found to be reduced in AD brains compared to those in control brains (88-90).

Apart from the mean MTR values, an MTR histogram analysis has been used to better characterize the changes in WM. Measures like ‘peak position,’ the MTR value with the highest frequency, and the ‘relative peak height,’ the relative voxel count at the peak position, have been defined. Relative peak height indicates parenchymal damage and is unaffected by brain atrophy. Results from MTR histogram analyses have even suggested that mean MTR changes are not restricted to the temporal lobes but can be found more globally throughout the brain (91-93). In their work, Ridha et al. assessed the value of measuring both MTR and brain volumes in differentiating patients with AD from control subjects. They found that using both parameters (brain volume and mean MTR) in combination provided significantly higher sensitivity in differentiating AD patients from controls than either parameter alone (93).

Ropele et al. (94) studied the longitudinal MTR changes in patients with AD. They found that AD patients had significantly and consistently lower global MTR values through the one-year follow-up period. Deep brain nuclei like the thalamus, putamen, and caudate nucleus showed significant reductions in MTR at 12 months follow-up compared to baseline. Furthermore, these findings were complemented by correlations with the mini-mental state examination (MMSE), suggesting the role of deep GM structures in the pathogenesis of AD (95-98).

In brain regions most susceptible to damage in AD, U-fibers in the superficial WM appear to be the most vulnerable type of neurons. In their study, Fornari et al. (99) demonstrated a widespread reduction in MTR values in superficial WM with predominant involvement of the left hemisphere, as in previous studies (34,45). They also demonstrated two distinct patterns of demyelination in surface WM based on the age of AD onset, underlining the potential of MTR imaging as an in vivo biomarker superior to currently used techniques. Figure 5 shows widespread decrease in MTR values in different regions of the brain.

Figure 5 The results of the 2-sample t test for mean MTR values show a global decrease in MTR values in AD patients when compared to elderly controls. Each ROI has been tested separately and the results are displayed in a 3D rendering. They are thresholded at P<0.05 corrected for multiple comparisons. The color bar represents P_corrected values. The figure was reused from (99), with permission. AD, Alzheimer’s disease; MTR, magnetization transfer ratio; ROI, region of interest.

MTR, however, is affected by various factors like RF excitation amplitude, frequency offset, duty cycle, acquisition parameters, and tissue properties. It is therefore more desirable to measure the voxel-level MT-related intrinsic tissue properties, e.g., the forward magnetization transfer rate (k_for) map from tissue water to macromolecular protons, independent of the data acquisition details. Lower k_for values have been reported in multiple brain grey and WM regions (100,101). These studies, however, analyzed predetermined regions of interest for increased signal-to-noise ratio (SNR). Recently, Duan et al. (102) demonstrated voxel-wise k_for abnormalities in AD, enabling precise detection of abnormal k_for values in spatial locations associated with AD, without predefined hypotheses.

Another intrinsic MT parameter that is studied is the macromolecular fraction (MMF). In biological tissues, macromolecules such as proteins and lipids have restricted molecular motion compared to free water molecules. The MT process involves the exchange of magnetization between these two compartments. The MMF is a quantitative measure of the total magnetization fraction associated with macromolecules. MMF has been studied in tissues like meniscus (103), cartilage (104,105), brain (106,107), tendon (108,109), etc. This MMF or myelin proton fraction is not affected by the spatial orientation of myelinated fibers (affects diffusion imaging indexes) (110-113), the concentration of iron and other paramagnetic ions (affects T1, T2, T2* quantification), and magnetic field strength (114,115). Kiefer et al. (116), in their study on qMT in the hippocampus, were able to distinguish MCI and AD brains using MMF and T2r (T2 of the restricted pool), whereas MTR could only distinguish control and AD brains. Studies have also shown reduced MMF in the right parahippocampal cingulum (117) and left thalamus (118) in asymptomatic participants with familial and genetic risk factors of AD.

MWF

While myelin is defined as the lipid layers that wrap concentrically around axons, myelin water (MW) is defined as the water trapped within these layers. In qMRI, MW is a target as an indirect measure of myelin content in the brain. However, like myelin, MW content is traditionally very difficult to image or quantify due to several factors. MW is localized within the very narrow gap (<4 nm) between myelin lipid layers, causing the water molecules to interact with neighboring lipids (119,120). Subsequently, it has unique signal characteristics as a hybrid between the very short T₂ relaxation times of macromolecules/non-aqueous myelin (<1 ms) from its tightly bound protons and the relatively long T₂ of intra/extracellular water (40–200 ms) and its more free protons. Furthermore, roughly ~70% of hydrogen atoms in WM are found in the intra/extracellular space, rendering the MW the nondominant signal source in this region (120). MW itself has a very short T₂ time between 10–20 ms, much shorter than that of intra/extracellular water and which decays rapidly on conventional MRI sequences. Simultaneously, the T₁ of MW is (<400 ms) and affected by the cross relaxation induced by the surrounding lipids (121). The resonance frequency of MW is also highly dependent on the specific orientation between WM fiber tracts and the B₀ field, namely when they are perpendicular, MW displays a large positive frequency shift, while intra/extracellular water displays a large negative shift.

Conventional MRI generally focuses on tissue with long T₂ water components such as the WM, GM, intra/extracellular water (40–200 ms), and CSF (>350 ms). This leaves out a critical biomarker that has the potential to be used in the broad evaluation of the brain as a whole.

As such, a variety of techniques have been developed to evaluate MW, specifically the MWF. MWF is typically defined as the ratio of the area of the MW T₂ distribution to the area of the entire T2 distribution. Many developed techniques have found that a reduced MWF is associated with myelin reduction and amyloid pathology in preclinical AD.

The Carr-Purcell-Meiboom-Gill (CPMG) T₂, or spin echo method utilizes a 90 ^o excitation pulse followed by a series of 180° refocusing pulses to acquire a T₂ decay curve (122,123). The acquisitions can be accelerated by collecting multiple images per echo time using spiral acquisitions (122,124). To extract the shorter MW T₂ time from the total T₂ signal distribution of all brain tissue, non-negative least squares analysis (NNLS) was traditionally used and more recently, decomposing the decay curve into log-Gaussian components, namely CSF, intra/extracellular water, and MW (125,126). Decomposing into different components is generally considered more robust, but it may lead to misleading MWF results when the number of T2 components is underestimated or overestimated. Similar T₂ based MW techniques generally correlate well with myelin histology, even with region of interest (ROI) reproducibility, but suffer from poor voxel level reproducibility due to the inaccurate estimation (120,127). Multicomponent T₂ analysis and multicomponent-Driven Equilibrium Single Pulse Observation of T₁ and T₂ (mcDESPOT) model the brain tissue with two or more water components (typically MW and intra/extracellular water) and isolate them via postprocessing based on differences between the components’ T₂ /T₁. This is based on a spoiled gradient sequence and balanced free precession sequence. Visualization of short transverse relaxation time components (ViSTa) is based on acquiring multiple inversion recovery images with a wide range of T₁ for robust suppression of CSF and intra/extracellular water signals.

Studies have revealed a significant association between myelin loss, with its subsequent reduced MWF and AD state (128,129). Figure 6 shows 3D T1 weighted images, MWF, grey matter volume (GMV), white matter volume (WMV) comparisons between a 72-year-old healthy female control and 73-year-old female AD patient. A decrease in the WMV and a gross signal drop in the MWF maps was observed in the AD patient. The largest area under curve (AUC) for differentiating between cognitively normal (CN) and AD groups was 0.79 in corpus callosum for MWF, 0,883 in hippocampus for gray matter volume (GMV) and 0.86 in hippocampus for WMV (128). This decreased MWF also follows cognitive impairment associated with aging, further implicating it as a useful biomarker for AD. Furthermore, reduced MWF is also related to positive apolipoprotein E4 (APOE4) status, a major genetic risk factor for AD, of which mutations are responsible for inhibiting amyloid β clearance and facilitating tau pathology (130).

Figure 6 Representative maps of the 3D T1W, GMV, WMV and MWF with and without color code. A gross signal drop in the apparent MWF signal was observed in the AD group compared to the CN group in the white matter. This figure was adapted from an open access article (128), under the terms of the Creative Commons Attribution License 4.0 (CCBY). AD, Alzheimer’s disease; CN, control; GMV, grey matter volume; MWF, myelin water fraction; T1W, T1-weighted; WMV, white matter volume.

Myelin PD

Myelin is a concentrically laminated membranous structure consisting of alternating protein and lipid layers, and is a major component of the WM of the brain. It is present in the form of a sheath surrounding the axons, insulating them from electrically charged ions and molecules. Myelin helps increase nerve conduction velocity. Loss of myelin is the hallmark of numerous inflammatory and neurodegenerative disorders, including multiple sclerosis (MS), AD, and other neurological diseases (131). Direct assessment of the integrity of myelin may be important. However, the non-aqueous myelin protons have extremely short T₂s (less than 1 ms), and cannot be directly imaged with conventional clinical MRI sequences which have TEs of several milliseconds or longer (132-137). As a result, conventional MRI sequences only provide an indirect assessment of myelin. Myelin-specific information, such as its T1 and T2* relaxation times and PD, is unknown. ultrashort echo time (UTE) sequences with minimum nominal TEs as short as 8 µs, which are more than 100 times shorter than the TEs of conventional MRI sequences, make it possible to directly detect signal from myelin using clinical whole-body scanners (132-142).

While UTE-type sequences with TEs <0.1 ms allow direct myelin imaging, the major challenge to this approach is selectivity because water components with longer T2 show far higher signals than myelin (143). Adiabatic inversion recovery (IR) pulses allow uniform inversion and suppression of the longitudinal magnetizations of various water components, making it possible to image myelin selectively with the IR-based UTE (IR-TUE) sequences. The short repetition time adiabatic IR UTE (STAIR-UTE) sequence allows more efficient suppression of various water components in the brain with a broad range of T1s and T2s. Figure 7 shows 3D STAIR-UTE myelin PD maps for a healthy 36-year-old male volunteer (Figure 7A-7D) and a 72-year-old female AD patient (Figure 7E-7H). Normal WM (NWM) in the healthy volunteer has a myelin PD ~9 mol/L, while normal GM (NGM) has a myelin PD ~5 mol/L, largely consistent with the literature. The AD patient shows extensive myelin loss with a myelin PD of ~5–7 mol/L for NAWM.

Figure 7 Selected myelin PD maps of a 36-year-old healthy male volunteer (A-D) and a 72-year-old female AD patient (E-H) generated from 3D STAIR-UTE imaging. The AD patient (WM myelin PD ~5–7 mol/L) shows extensive myelin loss across the whole brain compared to the healthy volunteer (WM myelin PD ~8–10 mol/L). AD, Alzheimer’s disease; PD, parkinson disease; STAIR, short repetition time adiabatic inversion recovery; UTE, ultrashort echo time; WM, white matter.

Artificial intelligence (AI) in AD

AI has played a crucial role in addressing neurological diseases, particularly through the analysis of imaging data. Traditional machine learning (ML) models, including radiomics features, have demonstrated effectiveness in evaluating pathologies (144). Deep learning (DL), a newer approach within ML, employs convolutional neural networks (CNNs) for in-depth analysis of biomedical images.

DL-based networks consist of neurons that perform mathematical operations on input data, aiming to establish relationships between input (e.g., images, clinical data) and output (e.g., disease presence, survival status) data (145). Public datasets such as Alzheimer’s Disease Neuroimaging Initiative (ADNI) (146) and Open Access Series of Imaging Studies (OASIS) (147) provide valuable medical image sets for AI applications in AD.

AI has been applied in various aspects of MRI imaging, including image reconstruction, denoising, segmentation, synthesis, disease prediction, and aiding in disease diagnosis and prognosis.

Diagnostic/prognostic analysis

One of the most popular tasks in articles that used AI for AD assessment is classification and AD diagnoses, such as the binary classification of AD vs. MCI (148), AD vs. normal (149-151), or MCI vs. normal, or multi-class classification such as AD vs. MCI vs. normal (152-154). In this light, Gupta et al. (155) utilized the ADNI dataset to classify patients into MCI and AD using a support vector machine (SVM) classifier. In another article, Kam et al. (156) made a CNN-based model that detects MCI at an early stage using resting-state functional MRI scans. Cui et al. (157) also showed that DL models are capable of classifying patients into AD, MCI, and healthy by analyzing the hippocampus and its adjacent areas in T1-weighted MRI scans.

In addition, the identification of progression in MCI and AD patients has always been of interest in the literature (158-160). In a study by Spasov et al. (158), researchers trained a neural network model on 785 patients and aimed to predict whether a patient with MCI would proceed to AD or not in the future. Their model achieved AUC of 0.925 which can be considered decent for clinical application. In another study, Fisher et al. (159) built and utilized an unsupervised ML model to correctly predict AD progression in 1909 patients. Another research study (160) focused on the development of a recurrent neural network in order to forecast MCI progression into AD, reaching AUCs of 0.83–0.86 and utilizing multi-modal ADNI data.

Image reconstruction & denoising

Traditional reconstruction methods for radiological scans require a considerable amount of time and computation. DL-based models have performed much faster and have simplified complex computation patterns, leading to more accurate and readily available images. For example, DL achieves more efficient and speedier PET (161) and PET/MR (162) reconstructed images of AD patients, which would help when the physician aims for lower acquisition time or the patient cannot cooperate well.

DL has also helped with the denoising task of MRI images. The application of AI models to reconstructed MR images would lead to higher-quality images that contain more informative data and are less blurry or have fewer artifacts. A few studies have utilized AI-based denoising systems in AD (163-165). According to Kanemaru et al. (163), the AI denoising system efficiently reduces the noise, keeping the necessary contrast. The study of Lee et al. (164) reached the same results using PET images. Last but not least, Ravi et al. (165) developed a DL-based denoising system applied to MRI images, achieving high quality images.

Image synthesis

DL models, especially generative adversarial networks (GANs), have emerged as a tool to create synthetic medical images. These models mimic a real-world sample of an image and transform it into a completely synthesized image that cannot be easily distinguished from a true patient image. This helps in the training process of other AI models that need very high numbers of data samples. For example, several research articles have focused on the use of GANs for the aim of MRI image synthesis and applying a classifier to categorize data into AD and normal cases (166). Another instance is the study of Hamghalam et al. (167), which utilized GANs to generate new MR images and then fed the images to a DL-based segmentation model. Their final model aimed to segment white and GM, which is also a crucial task for AD.

Segmentation

Image segmentation can be referred to as dividing the image pixels into two or more subgroups. For example, one may try to distinguish WM from GM and/or CSF, which we can refer to as tissue segmentation. Another instance is the separation of the hippocampus from other brain areas, which can be regarded as anatomical segmentation. The latter plays a key role in the analysis of AD as much of the underlying pathology of AD remains in the morphometry analysis of the hippocampal area. Segmentation masks can be achieved using either traditional ML models or novel DL networks such as U-net, which has emerged as one of the fundamental medical imaging segmentation models (168). In this regard, several studies have investigated the use of AI for the segmentation of the hippocampus in MRI data (169,170).

Challenges of AI

There are, however, some challenges imposed with the applicability of AI in AD research. For example, we need larger multi-institutional imaging datasets coupled with demographical, clinical, and genomics features in order to construct a holistic model. Unfortunately, this goal remains hard to reach as several institutions disallow the public share of patients’ data. In addition, current research in AI has focused on the development of new models rather than the translation of

currently-developed models to an external dataset. Furthermore, a literature review also shows the lack of globally accepted reporting guidelines to standardize the methodologies of articles, except for some checklists such as Checklist for Artificial Intelligence in Medical Imaging (CLAIM) 32 which is still not widely used in the literature. Addressing these challenges would definitely help establish a better foundation for AI research in AD, leading to more generalizable, explainable, and trustworthy models that will be readily applied to daily clinical practice.

Conclusions

The integration of myelin damage in the understanding of AD pathophysiology has significant implications for both research and clinical practice. Traditionally viewed as a disorder primarily affecting GM, mounting evidence suggests that myelin damage is not only a central feature of AD but may also precede the development of hallmark pathologies such as amyloid-beta (Aβ) plaques and tau tangles. This paradigm shift underscores the importance of WM integrity and myelin preservation in the context of AD.

As described in the review, multiple relaxation components have been described in the brain using T1, T2*, T1ρ, and MT analysis. Although most T1ρ bicomponent research has focused on musculoskeletal tissues such as tendons and cartilage (171-174), demonstrating promising outcomes, its extension to brain analysis highlights the potential for valuable insights into neural relaxation mechanisms. Menon et al. (175) demonstrated bi-exponential T1ρ relaxation in brain GM and WM, superior to the traditional single component fitting, providing greater information on bound and free water components. Table 2 summarises the quantitative imaging biomarkers and their behaviour in AD.

PET is another neuroimaging tool that has been used to quantitatively study the molecular processes in the brain (176,177). PET radioligands bind targets such as amyloid beta (178-180), tau (181) and translocator protein 18 kDA (TSPO) (182). The quantitative assessment of amyloid involves measuring the SUVr in the cortical regions associated with significant amyloid burden. Cortical GM regions, i.e., anterior and posterior cingulate, medial orbital frontal, temporal, and parietal lobes and the precuneus are routinely used for analysis (183). A reference region, which is believed not to accumulate amyloid, is required for SUVr calculations. Regions such as the cerebellar cortex, whole cerebellum, pons, and subcortical WM have been used as reference regions for these calculations (184). Qualitative visual assessments using amyloid PET scans have shown a sensitivity of about 84.6% and a specificity of 38.1% for distinguishing AD patients from healthy controls (185). Improved scan parameters and additional biomarkers have enhanced test accuracy. Although more time-consuming, the quantitative assessment of the global cortex SUVr is considered more accurate, with a sensitivity of 92.3% and a specificity of 90.5% (185). However, amyloid PET positivity is seen in healthy older adults, with amyloid positivity rates ranging from 10–12% in those 60 to 70 years old to more than 50% in persons aged 80–90 years (186). A positive amyloid scan can also be seen in other medical conditions like Lewy body dementia (187). Amyloid PET scans may, in certain instances, underestimate the brain amyloid plaque burden, especially in the setting of low CSF Aβ42 levels (188). In addition, diffusion tensor imaging (DTI) has also been used extensively to study the neurodegeneration in AD and recently used to study early AD changes (189). DTI was not included in our paper because of multiple reviews on DTI in AD in published literature, and also because we wanted to focus on techniques that directly visualize and quantify the myelin content, iron deposition and localized neurodegeneration.

In discussing the challenges surrounding quantitative imaging and integration of AI, several key obstacles emerge. Foremost among these is the time-consuming nature of quantitative imaging techniques, which presents a significant barrier to their widespread adoption in clinical settings. Furthermore, the lack of standardized sequences for different MRI platforms complicates the comparison and reproducibility of results across studies. Current research in AI has focused on developing new models rather than translating currently-developed models to an external dataset. Furthermore, a literature review also shows the lack of globally accepted reporting guidelines to standardize the methodologies of articles, except for some checklists such as CLAIM (190), which is still not widely used in the literature. Addressing this issue is crucial to establishing robust and reliable quantitative imaging protocols.

Additionally, the necessity for large sample sizes in quantitative imaging studies poses logistical and resource challenges, requiring considerable effort to recruit and retain participants. Larger multi-institutional imaging datasets coupled with demographical, clinical, and genomics features are needed to construct holistic AI models. Unfortunately, this goal remains hard to reach as several institutions disallow the public share of patients’ data. Moreover, the analysis of quantitative imaging data demands advanced algorithms and computational expertise, underscoring the importance of skilled technicians and researchers in this field. Lastly, as quantitative imaging becomes increasingly utilized in the diagnosis and monitoring of AD, there is a pressing need for further research to differentiate between different pathologies that may overlap with AD. This highlights the complexity inherent in utilizing quantitative imaging as a diagnostic tool and underscores the importance of ongoing investigation to fully realize its potential in clinical practice.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-1602/rc

Funding: This work was supported by the National Institutes of Health (No. RF1AG075717) and Veterans Affairs Clinical Science Research and Development (No. I01CX002211).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1602/coif). J.D. serves as an unpaid editorial board member of Quantitative Imaging in Medicine and Surgery. The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Written informed consent was obtained from each participant as approved by the institutional review board (IRB) of the University of California, San Diego.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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