Simple Quantitative Indices for the Differentiation of Advanced-Stage Alzheimer’s Disease and Other Limbic Tauopathies

Abstract

Background:

The differentiation of Alzheimer’s disease (AD) from age-related limbic tauopathies (LT), including argyrophilic grain disease (AGD) and senile dementia of the neurofibrillary tangle type (SD-NFT), is often challenging because specific clinical diagnostic criteria have not yet been established. Despite the utility of specific biomarkers evaluating amyloid and tau to detect the AD-related pathophysiological changes, the expense and associated invasiveness preclude their use as first-line diagnostic tools for all demented patients. Therefore, less invasive and costly biomarkers would be valuable in routine clinical practice for the differentiation of AD and LT.

Objective:

The purpose of this study is to develop a simple reproducible method on magnetic resonance imaging (MRI) that could be adopted in daily clinical practice for the differentiation of AD and other forms of LT.

Methods:

Our newly proposed three quantitative indices and well-known medial temporal atrophy (MTA) score were evaluated using MRI of pathologically-proven advanced-stage 21 AD, 10 AGD, and 2 SD-NFT patients.

Results:

Contrary to MTA score, hippocampal angle (HPA), inferior horn area (IHA), and ratio between HPA and IHA (i.e., IHPA index) demonstrated higher diagnostic performance and reproducibility, especially to differentiate advanced-stage AD patients with Braak neurofibrillary tangle stage V/VI from LT patients (the area under the receiver-operating-characteristic curve of 0.83, 089, and 0.91; intraclass correlation coefficients of 0.930, 0.998, and 0.995, respectively).

Conclusion:

Quantitative indices reflecting hippocampal deformation with ventricular enlargement are useful to differentiate advanced-stage AD from LT. This simple and convenient method could be useful in daily clinical practice.

Keywords

Alzheimer’s disease argyrophilic grain disease hippocampal angle limbic tauopathy magnetic resonance imaging senile dementia of the neurofibrillary tangle type ventricular enlargement

INTRODUCTION

With increases in the aging population and corresponding increase in age-related dementia, clinicians are encountering patients with various kinds of dementia more frequently. Other than Alzheimer’s disease (AD), which is the most common cause of neurodegenerative dementia in persons over 60 years, pathologies such as dementia with Lewy bodies (DLB), TAR DNA-binding protein-43 (TDP-43) proteinopathy, progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD) can be responsible for dementia in elderly patients. Medications such as cholinesterase inhibitors are effective for AD but not for all types of dementia [1]. Considering possible side effects, e.g., hypersensitivity against neuroleptics in DLB resulting in worsening of the clinical status, attempts to differentiate AD from other neurodegenerative dementias are important to decide the optimal treatment strategy.

Making a clinical diagnosis of age-related limbic tauopathies (LT) including argyrophilic grain disease (AGD) and senile dementia of the neurofibrillary tangle type (SD-NFT) is often challenging because specific diagnostic criteria have yet to be established [2, 3]. AGD and SD-NFT are common AD mimickers that show overlapping neuropathological features; that is, abundant tau deposits restricted mainly to the limbic and allocortical areas and little or even absent Aβ deposition [4 –6]. Due to the relatively restricted neuropathological changes, these tauopathies manifest mainly as very slowly progressive and amnestic mild cognitive impairment, similar to early-stage AD or other forms of limbic dementia [7]. In contrast to SD-NFT, behavioral and psychological symptoms, especially eating symptoms, are well-known clinical presentations in AGD [7, 8]. However, these symptoms are nonspecific and frequently observed in severely demented patients with AD [9]. Structural overlap is also observed in these limbic tauopathies and AD in that both of them present with hippocampal atrophy, which is a well-known imaging biomarker of AD on structural magnetic resonance imaging (MRI) [10, 11]. These clinical similarities make AGD and SD-NFT indistinguishable from AD using routine clinical diagnostic tests and MRI [12].

Although several attempts have been made for their differentiation using biomarkers such as cerebrospinal fluid (CSF) amyloid-β, amyloid positron emission tomography (PET), and advanced analytical methods such as voxel-based morphometry (VBM), it is not realistic to apply these advanced techniques for all patients suspected of having AD [13 –15]. This prompted us to explore objective MRI findings using a simple reproducible method which can be adopted in daily clinical practice. The purpose of this study is to investigate simple quantitative indices on MRI for the differentiation of AD and other limbic tauopathies.

MATERIALS AND METHODS

Subjects

The study population was selected by searching the available medical records between January 2013 and December 2020 at Fukushimura Hospital. Patient backgrounds were standardized by applying the following inclusion criteria: 1) neuropathological diagnoses according to the published criteria of AD, AGD, and SD-NFT [3 , 17], 2) dementia status according to the major neurocognitive disorders of the DSM-V criteria, and 3) acquisition of MRI examinations including three-dimensional (3D) T1-weighted image (T1WI). An exclusion criterion was the presence of other comorbid neurodegenerative pathologies such as PSP and CBD [18, 19]. AD patients with argyrophilic grain were also excluded. On the other hand, slight accumulation of Lewy body and TDP-43 pathologies, which did not fulfill the diagnostic criteria of DLB and frontotemporal lobar degeneration with TDP-43 (FTLD-TDP), was regarded as a minimal senile neuropathologic change and left in the study cohort [20 –22]. Additionally, patients with obstructive hydrocephalus or large destructive lesions, such as cerebrovascular diseases and neoplasms, in the limbic lobes were excluded. This study was approved by the institutional review board of Fukushimura Hospital, and postmortem consent was obtained from the bereaved of all patients to use the data for academic purposes. The privacy of patients was completely protected. This retrospective single-center study was carried out in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

MRI protocol

All patients underwent MRI examination on a 1.5-T imager (MAGNETOM Aera®; Siemens Healthcare, Erlangen, Germany) with a 20-channel Head/Neck coil. A 3DT1WI was obtained on magnetization-prepared rapid gradient-echo (MPRAGE) for 25 patients and on fast low angle shot (FLASH) for eight patients. Scan parameters of MPRAGE were as follows: repetition time (TR)/ echo time (TE), 1700 ms/4.2 ms; inversion time, 800 ms; flip angle (FA), 19°; field of view (FOV), 230 mm; matrix, 256×256; and 1.25 mm-thick gapless sections. Scan parameters of FLASH were: TR/TE, 27 ms/6.9 ms; FA, 19°; FOV, 180 mm; matrix, 320×224; and 0.65 mm-thick gapless sections. Additionally, the multiplanar reconstruction technique was performed to make a 1.25 mm-thick oblique coronal plane perpendicular to the hippocampus body.

Image analysis

Using ITK-SNAP software (Version 3.8.0, http://www.itksnap.org/), two quantitative indices were measured on the oblique coronal reconstructed 3DT1WI. To enhance reproducibility, the slice section at the midpoint between a posterior edge of the left amygdala and left uncal apex was chosen as the reference measurement position (Fig. 1a). First, an angle between two lines parallel to the long axes of the bilateral hippocampal heads was measured using a “line and ruler mode” (Hippocampal angle: HPA) (Fig. 1c). After that, a “paintbrush mode (adaptive brush tool with the following settings: size, 40; granularity, 10; and smoothness, 100)” was used to segment the bilateral inferior horn of the lateral ventricle (Inferior horn area: IHA) (Fig. 1d). Then, an area of the bilateral inferior horn was calculated by multiplying pixel counts by pixel area. Finally, the ratio between the IHA and HPA (i.e., IHA/HPA) was calculated, and this index referred to as the “IHPA index”.

Fig. 1

Measurement of the hippocampal angle (HPA) and area of the bilateral inferior horn (IHA). a) A magnified axial image at the level of the uncus. A crosshair tool on ITK-SNAP software was used to identify a slice section at the midpoint (small arrow) between a posterior edge of the left amygdala and left uncal apex (arrowheads), which was selected as a reference measurement position. b) An oblique coronal image at a reference measurement position (i.e., midpoint between a posterior edge of the left amygdala and left uncal apex). c) Measurement of the HPA. Two lines parallel to the long axes of the bilateral hippocampal heads were drawn (arrows). The angle between two crossing lines was measured as the HPA. d) Measurement of IHA. An “adaptive brush tool” of ITK-SNAP software enabled operators to easily segment the inferior horn with only one or two clicks. Two white-filled areas indicated the IHA.

In addition to the above-mentioned quantitative indices, the semiquantitative Scheltens’ medial temporal atrophy (MTA) score was evaluated to assess the degree of atrophy of the hippocampus [23]. The five-point MTA score ranged from 0 = no atrophy to 4 = severe atrophy for the rated brain regions. These were initially assessed separately in each hemisphere and then averaged (e.g., in a case of the right side score was 3 and the left side 4, the overall score was calculated as 3.5). The raters were a neuroradiologist and neurologist with 16 years (rater 1, K.S.) and 12 years (rater 2, Y.U.) of neuroradiologic experience with MR imaging of dementia. For the analyses, the two raters were blinded to all the patients’ information and independently evaluated all MR images.

Statistical analysis

Statistical analyses were performed using IBM SPSS statistics 24 (IBM SPSS Inc, Chicago, IL, USA). The one-way analysis of variance for normally distributed data (age, disease duration, HPA, and IHA), Kruskal-Wallis test for non-normally distributed data (mini-mental state examination score: MMSE, IHPA index, and MTA score), and Fisher’s exact test for categorical data (gender and ratio of Lewy body and TDP-43 pathology) were performed for comparisons among patient groups. When a significant level was found in multiple comparisons, the unpaired t-test, Mann-Whitney U test or pairwise test was also performed. Resulting p values were corrected according to the Bonferroni method and considered as statistically significant if < 0.05. Additionally, the relationship between HPA and IHA was assessed by Pearson’s product-moment correlation coefficient. To evaluate the interrater reproducibility, the intraclass correlation coefficient and weighted Cohen’s kappa were calculated. Using the HPA, IHA, and IHPA index, a receiver operating characteristic (ROC) curve analysis was performed to determine which quantitative indices discriminated the different groups. Youden’s index was applied to determine the cut-off values.

RESULTS

Clinical features

Patients’ characteristics are summarized in Table 1. There were no significant differences in age, gender, disease duration, or MMSE score between the AD and LT patients. AD group consisted of 21 patients with 14 severe pathological changes corresponding to Braak NFT stage V/VI stage with frequent neuritic plaques according to the Consortium to Establish a Registry for Alzheimer Disease (CERAD) and 7 with moderate pathological changes corresponding to Braak NFT stage IV stage with moderate or frequent CERAD neuritic plaques [24]. According to the ABC score, 13 and 8 patients were classified into high and intermediate levels of AD neuropathologic changes, respectively [16]. LT group consisted of 12 patients with 10 with AGD and two with SD-NFT. All AGD patients suffered from advanced AGD, i.e., AGD Saito stage III [17]. In both SD-NFT patients, argyrophilic grains of AGD Saito stage I were detected as a comorbid pathology. There was no difference in the co-existence rate of TDP-43 and Lewy-type pathology between the two groups.

Table 1

Patients’ characteristics

	AD		LT	p
	AD Braak NFT IV	AD Braak NFT V/VI
Number	7	14	12	N.A.
Age at MRI (y)	86.1±7.2	85.1±6.4	87.2±6.4	0.72^*
Male/Female	2/5	1/13	4/8	0.19^†
Duration (y)	7.0±5.2	10.8±5.0	5.9±2.0	0.05^*
MMSE	14.0±6.1	6.4±5.9	12.9±7.3	0.06^‡
Braak NFT	IV 7	V 2, VI 12	I 2, II 9, III1	N.A.
CERAD 0/A/B/C	0/0/2/5	0/0/0/14	4/3/4/1	N.A.
Thal 0/1/2/3/4/5	0/0/0/0/6/1	0/0/0/1/4/9	3/5/2/1/1/0	N.A.
LtP	3/7 (43%)	8/14 (57%)	3/10 (30%)^§	0.38^†
TDP-43P	2/7 (29%)	8/14 (57%)	4/7 (57%)^§	0.61^†
AGD I/II/III	0/0/0	0/0/0	2/0/10	N.A.

Data are shown as absolute numbers or the mean±standard deviation. AD, Alzheimer’s disease; AGD, argyrophilic grain disease; CERAD, Consortium to Establish a Registry for Alzheimer Disease; LtP, Lewy-type pathology; LT, limbic tauopathies; MMSE, Mini-Mental State Examination; MRI, magnetic resonance imaging; NA, not applicable; NFT, neurofibrillary tangle; TDP-43 pathology, TAR DNA-binding protein-43; y, years. ^*one-way analysis of variance; ^†Fisher’s exact test; ^‡Kruskal-Wallis test; ^§Lewy-type and TDP-43 pathologies were not fully evaluated in some LT patients.

MRI features

Representative coronal 3DT1WI of pathologically-proven AD and LT patients are presented in Fig. 2. Results of HPA, IHA, IHPA index, and MTA score in AD and LT patients are shown in Table 2. Compared with LT patients, all AD group and AD with Braak NFT stage V/VI subgroups revealed lower HPA (141.9±22.1 versus 114.4 ±18.6, and 111.7±19.7; p = 0.002, and p = 0.002, respectively). IHA and IHPA indices in all AD group (468.4±290.0 and 4.5±3.2) and AD with Braak NFT V/VI stage subgroup (538.5±321.7 and 5.2±3.5) were higher than those of LT patients (209.3±75.4 and 1.6±0.8; p = 0.04, p = 0.01, p = 0.004, and p = 0.001, respectively). On the other hand, no statistical difference was observed in MTA score between AD and LT patients (p = 0.52). In contrast to LT patients (p = 0.22), a significant correlation was found between HPA and IHA not only in all AD patient group but also in AD with Braak NFT stage IV and with Braak NFT stage V/VI subgroups (p = 0.001, 0.016, and 0.017, respectively). The intraclass correlation coefficients of HPA, IHA, and IHPA index, and weighted Cohen’s kappa of MTA score for interrater reliability were 0.930, 0.998, 0.995 and 0.683, respectively.

Fig. 2

Representative coronal 3DT1WI of pathologically proven AD and LT patients. a) An 89-year-old pathologically proven AD (Braak NFT stage VI stage) patient (HPA = 90.0, IHA = 420.2 and IHPA index = 4.67). b) A 98-year-old pathologically proven AD (Braak NFT stage V stage) patient (HPA = 92.3, IHA = 500.3 and IHPA index = 5.42). c) A 96-year-old pathologically proven AD (Braak NFT stage IV stage) patient (HPA = 115.3, IHA = 272.2 and IHPA index = 2.37). d) An 86-year-old pathologically proven AGD patient (HPA = 145.7, IHA = 145.4 and IHPA index = 0.99). e) A 97-year-old pathologically proven AGD patient (HPA = 165.5, IHA = 194.8 and IHPA index = 1.18). f) An 81-year-old pathologically proven SD-NFT patient (HPA = 127.2, IHA = 120.6 and IHPA index = 0.95). Compared with LT patients (d-f), AD patients (a-c) showed a long vertical elliptical shaped deformation of the hippocampus and marked dilation of the inferior horn (arrows). Notably, even in those with severe hippocampal atrophy, hippocampal deformation and ventricular dilation were relatively mild in AGD and SD-NFT patients (e, f). AD, Alzheimer’s disease; AGD, argyrophilic grain disease; HPA, hippocampal angle; IHA, inferior horn area; SD-NFT, senile dementia of the neurofibrillary tangle type.

Table 2

Comparison of quantitative and semiquantitative indices

	AD			LT	p
	All AD patients	AD Braak NFT IV	AD Braak NFT V/VI
HPA	114.4±18.6^‡	119.6±17.9	111.7±19.7^‡‡	141.9±22.1	0.001^*
IHA	468.4±290.0^§	328.3±159.1	538.5±321.7^§§	209.3±75.4	0.009^*
IHPA index	4.5±3.2^\|\|	3.0±1.9	5.2±3.5^\|\|\|\|	1.6±0.8	0.001^#
MTA score	3.5±0.7	3.1±0.9	3.6±0.6	3.4±0.7	0.52^#

Data are shown as the mean±standard deviation. AD, Alzheimer’s disease; HPA, Hippocampal angle; IHA, Inferior horn area; LT, limbic tauopathies; MTA, medial temporal atrophy; NFT, neurofibrillary tangle. ^*one-way analysis of variance; ^#Kruskal-Wallis test; ^‡p = 0.002 versus LD by unpaired t test with Bonferroni correction; ^‡‡p = 0.002 versus LD by unpaired t test with Bonferroni correction; ^§p = 0.04 versus LD by unpaired t test with Bonferroni correction; ^§§p = 0.01 versus LD by unpaired t test with Bonferroni correction; ^|p = 0.004 versus LD by Mann-Whitney U test with Bonferroni correction; ^||p = 0.001 versus LD by Mann-Whitney U test with Bonferroni correction.

ROC analyses using HPA, IHA, and IHPA index to discriminate AD from LT patients were performed to evaluate the diagnostic accuracy of these quantitative indices (Fig. 3). HPA, IHA, and IHPA index exhibited areas under the curve (AUC) of 0.82, 0.84, and 0.86 for differentiating between AD all group (i.e., with Braak NFT stage IV and V/VI) and LT patients, respectively. For the differentiation between advanced-stage AD (i.e., with Braak NFT stage V/VI) and LT patients, these indices produced a higher AUC (0.83, 0.89, and 0.91, respectively). On the other hand, the AUC of HPA, IHA, and IHPA index were relatively lower for the differentiation between moderate-stage AD (i.e., with Braak NFT stage IV) from LT patients (0.77, 0.75, and 0.77, respectively). Cut-off values of these quantitative indices, and their sensitivities, specificities, and accuracies are shown in Table 3.

Fig. 3

ROC curves using HPA, IHA, and IHPA index for the differentiation of all AD (a-c), advanced-stage AD with Braak NFT stage V/VI (d-f), and moderate-stage AD with Braak NFT stage IV from LD (g-i).

Table 3

AUC, cut-off values, and diagnostic index in discriminating between AD and LD patients

		AUC	CV	SENS (%)	SPEC (%)	PPV (%)	NPV (%)	ACC (%)
HPA	All AD versus LT	0.82	125	91.7	76.2	94.1	68.8	81.8
	AD B V/VI versus LT	0.83	125	91.7	85.7	92.3	84.6	88.4
	AD B IV versus LT	0.77	132	75.0	71.4	62.5	81.8	73.7
IHA	All AD versus LT	0.84	266	76.2	83.3	88.9	66.7	78.8
	AD B V/VI versus LT	0.89	371	64.3	100	100	70.6	80.8
	AD B IV versus LT	0.75	266	71.4	83.3	71.4	83.3	78.9
IHPA index	All AD versus LT	0.86	1.73	90.5	83.3	90.5	83.3	87.9
	AD B V/VI versus LT	0.91	1.80	92.9	83.3	86.7	90.9	88.4
	AD B IV versus LT	0.77	1.74	85.7	83.3	75.0	90.9	84.2

ACC, accuracy; AD, Alzheimer’s disease; AUC, area under the curve; B, Braak neurofibrillary tangle stage; CV, cut-off value; HPA, Hippocampal angle; IHA, inferior horn area; LT, limbic tauopathies; NPV, negative predictive value; PPV, positive predictive value; SENS, sensitivity; SPEC, specificity.

DISCUSSION

This is the first study to investigate the diagnostic performance of quantitative indices on MRI to differentiate between pathologically-proven AD and LT including AGD and SD-NFT. The imaging analysis method using a simple tool of ITK-SNAP software and definitive anatomical location enabled a highly reproducible quantitative evaluation of structural changes in each patient group. Compared to the well-known semiquantitative MTA score, this quantitative analysis could discriminate AD from LT, especially for AD with severe pathological changes corresponding to Braak NFT stage V/VI stage. This result suggests that these simple quantitative indices could contribute to the imaging diagnosis of AD and LT.

The increasing number of demented persons is one of the most important social problems affecting the present rapidly aging society. Generally, AD, DLB and vascular dementia are thought to be common diseases of dementia. However, recent advances in neuropathological studies have shown an increased prevalence of age-related limbic tauopathies including AGD and SD-NFT in the elderly demented population [25, 26]. AGD is a 4-repeat (R) tauopathy that is characterized by argyrophilic grains in the limbic areas, especially in the amygdala, and oligodendroglial coiled bodies and ballooned neurons in the amygdala [27]. SD-NFT is a 3R/4R tauopathy that is characterized by deposition of numerous NFTs in the hippocampal region like AD but with absent or scarce senile plaques throughout the brain [3]. Recently, SD-NFT has been redefined as a part of primary age-related tauopathy (PART), which is a pathological concept that encompasses a wide clinical spectrum from normal cognitive function to dementia through mild cognitive impairment [28]. The prevalence of NFT in normal elderly individuals has raised the contention that SD-NFT is a form of pathological or “accelerated” aging [29]. However, considering the fact indicating the “symptomatic” form of PART (i.e., SD-NFT) presenting with demented condition, and much higher NFTs than early stages of AD and physiological aging, it seems to be beneficial for clinicians to recognize SD-NFT as an independent clinicopathological entity rather than the early stage of AD or physiological aging. Due to the similarity in their clinical features (i.e., limbic dementia) of LT and AD, no evident clinical feature is able to differentiate these tauopathies from early to moderate AD [7].

Considering the pathophysiology of AD, more specific biomarkers based on the A/T/N system need to be established to facilitate the diagnosis [30]. Especially, evaluation of amyloid (A) deposition representing amyloid-β 42 (Aβ₄₂) levels in the CSF or amyloid PET and tau (T) deposition representing hyperphosphorylated tau levels in the CSF or tau PET are useful to separate AD from non-AD dementias, which include AGD and PART [31, 32]. Neurodegeneration (N) that represents structural MRI is not specific. Previous studies using visual rating scales and VBM have demonstrated that hippocampal atrophy, a well-known imaging biomarker of AD, is also observed in pathologically-proven AGD, SD-NFT, and PART patients [10 , 34]. Although these advanced techniques showed high accuracy in detecting the AD-related pathophysiological changes, high cost and invasiveness preclude their use as first-line diagnostic tools for all patients suspected of having AD. Other biomarkers, if less invasive and applicable in routine clinical practice, would be valuable to distinguish AD and LT. Therefore, we have attempted to explore a new simple biomarker to differentiate LT including AGD and SD-NFT from advanced-stage AD.

In this study, we developed three metrics calculated on MRI for differentiation of AD and LT, i.e., angle between two lines parallel to the bilateral hippocampal heads (HPA), area of the bilateral inferior horn of the lateral ventricle (IHA), and a novel index calculated by HPA and HPA (IHPA index). For simplification and greater applicability in daily clinical practice, the absolute area of the bilateral inferior horn was used without standardization by total intracranial volume. It has been reported that normalized measures are not always more useful than absolute ones [35]. Compared to the traditional Scheltens’ MTA score, these indices demonstrated high reproducibility and performance to differentiate AD from LT including AGD and SD-NFT. Some studies have suggested the utility of ventricular enlargement as a measure of AD progression [35, 36]. The cerebral ventricular enlargement associated with whole-brain atrophy progression in AD patients it makes evaluation of ventricular enlargement a plausible focus for the differentiation of AD from LT with mild cerebral atrophy relatively localized in the medial temporal lobes. Additionally, due to the atrophy of the hippocampal head, especially the CA1 subfield, the shape of the hippocampal head changes from long horizontal elliptical to long vertical elliptical [37]. Taking these and our study results that showed a correlation of HPA and IHA exclusively in AD patients, hippocampal deformation with ventricular enlargement may be more frequent in AD and explain smaller HPA, and larger IHA and IHPA index in AD patients.

One advantage of our study is that it developed a reproducible and simple method for the evaluation of structural changes in the medial temporal region. The sharp contrast between the signal intensity of CSF in the ventricles and surrounding tissue on 3DT1WI enabled a reproducible segmentation of the inferior horn of the lateral ventricles with a paintbrush tool. Similarly, definitive anatomical location enabled us to measure the reproducible hippocampal angle. Compared with VBM and volumetry, which are not widely used in clinical settings, our method seems to be sufficiently simple and suitable for use in daily clinical practice.

The relatively small number of patients, especially for pathologically proven SD-NFT, is a limitation of this study. Thus, it was difficult to directly compare AGD and SD-NFT. Considering the similarities of clinical and radiological profiles of AGD and SD-NFT, and clinical significance of differentiation of AD from them, we attempted to differentiate AGD and SD-NFT from AD as a first step. Exploration of the simple diagnostic clue could be essential for the appropriate clinical management of AD. Making differentiation between AGD and SD-NFT will be a next research challenge of a further study. Additionally, most of the patients were severely demented. Considering the correlation between the rate of ventricular volume change and NFT pathology, it is expected that advanced-stage AD patients would tend to show smaller HPA, and larger IHA and IHPA index based on hippocampal deformation with ventricular enlargement [38]. Thus, AD was divided into two subgroups according to the severity of Braak NFT stage to evaluate the diagnostic performance of new quantitative indices depending on the different disease stages in AD. Surely, although not statistically significant, AD with Braak NFT stage IV stage showed larger HPA, and smaller IHA and IHPA indices (i.e., minute deformation and ventricular enlargement) compared to those with Braak NFT stage IV/V. Therefore, clinicians should pay attention when applying these quantitative indices for the diagnosis of early-stage AD. Furthermore, the utility of these quantitative indices was not validated in patients with other kinds of neurodegenerative disorders such as Pick’s disease, FTLD with TDP-43, and pure limbic-predominant age-related TDP-43 encephalopathy (LATE), which can cause the severe medial temporal lobe atrophy. In spite of the criterion which excludes definite comorbid neurodegenerative pathologies, patients with some Lewy body and TDP-43 pathology were included in this study. Considering the importance of TDP-43 comorbidity in accelerating hippocampal atrophy in AD and PART, further study to better clarify the effect of TDP-43 pathology for the differentiation between AD and LT will be required [39, 40].

In conclusion, quantitative indices indicative of hippocampal deformation with ventricular enlargement are useful to differentiate advanced-stage AD from LT. The simplicity and convenience of this method make it suitable for routine clinical practice.

Footnotes

ACKNOWLEDGMENTS

We thank Mr. John Gelblum for his English proofreading.

Authors’ disclosures available online ().

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