Early pathological changes initiating cognitive disorders: Which nucleus is most prone to the iron deposition?

doi:10.21203/rs.3.rs-1607551/v1

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Early pathological changes initiating cognitive disorders: Which nucleus is most prone to the iron deposition?

https://doi.org/10.21203/rs.3.rs-1607551/v1

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Background: Iron deposition plays a vital role in damaging neurons and causing different cognitive disorders. Today, using the quantitative susceptibility mapping (QSM) technique, iron deposits in different brain areas can be assessed and measured.

Objective: This research aims to identify iron deposition changes of brain nuclei in different stages of cognitive disorders using the QSM technique to introduce biomarkers for the early detection of these disorders.

Methods: In this study, 35 participants with normal cognition and 46 patients with cognitive disorders classified into four different groups based on the severity of the disorder were scanned by a 3T MRI scanner.

QSM processing determined 12 regions of interest (ROIs) by automatic nuclei segmentation and statistical analysis performed in these groups’ MRI images.

Results: In the early mild cognitive impairment )EMCI( group, The QSM value of the bilateral thalamus(P<0.05) and left amygdala (P=0.006) nuclei were higher than the control group.

Based on the results of the receiver operating characteristic curve (ROC) analysis, the left amygdala (P=0.005), left putamen (P=0.002), left thalamus(P=0.05), and right thalamus(P<0.05) have an appropriate sensitivity and specificity to identify the different stages of cognitive disorders.

Conclusion: The left amygdala and bilateral thalamic nuclei are the first areas exposed to iron deposition during cognitive impairment.

Mentioned nuclei, especially the left amygdala, have high efficiency and sensitivity for the early detection of cognitive disorders.

Quantitative susceptibility mapping

Alzheimer’s disease

Iron Deposition

Magnetic susceptibility

1. Increased QSM levels were observed in the left amygdala, right thalamus, and left thalamus nuclei.

2. Mentioned nuclei can be introduced as biomarkers for the early detection of cognitive disorders, which provide a time window for treatment.

3. High efficiency and sensitivity were observed in the left amygdala nucleus, which can be used as a reliable biomarker for the early detection of cognitive disorders.

Neurodegenerative Alzheimer's disease (AD), more common in the elderly, imposes a heavy economic, psychological, and social burden on individuals and society(1, 2) .

There are usually stages of cognitive impairment before the onset of AD, which include subjective memory concerns (SMC), early mild cognitive impairment (EMCI), and late mild cognitive impairment (LMCI), respectively(3).

Various changes in the brain lead to the development and progression of these disorders; among these, pathological changes usually occur earlier than morphological changes(4, 5).

Iron deposition and amyloid-beta plaques aggregations are among the most important pathological changes in the brain during AD(6, 7).

Iron is the most abundant paramagnetic substance in the body, playing a pivotal role in performing many biological activities such as cell division and gene expression.

It is generally stored in the brain as ferritin or hemosiderin-6 forms and causes positive changes in the magnetic susceptibility of the tissue(8).

Despite all the benefits of iron, excessive deposition in different brain areas can damage neurons and impair cognitive function(9).

In addition, areas of increased iron deposition are prone to the accumulation of amyloid-beta plaques, one of the most well-known marks of AD(10).

According to various studies, there is a clear link between iron deposition and the development of neurodegenerative disorders such as AD and Parkinson's disease, so early detection of these iron and amyloid-beta plaques deposits leads to the diagnosis of these diseases in the early stages(11).

One of the most popular imaging modalities for brain sediment detection is positron emission tomography (PET); however, high levels of ionizing radiation are factors limiting its safe clinical use(12).

There are various magnetic resonance imaging (MRI)-based techniques to identify these iron depositions areas, such as T2 * weighted imaging (T2 * WI), susceptibility-weighted imaging (SWI), transverse relaxation rate (R2 *), field-dependent relaxation rate increase (FDRI), and calculation of Susceptibility through multiple orientation sampling (COSMOS).

In addition to their benefits, each of these techniques also has several disadvantages; for example, most of the methods mentioned above suffer from Blooming artifact(13).

In addition, T2 * WI is dependent on the scan parameters; the SWI method depends on the patient's head position and the magnetic susceptibility of the tissues around the target area(14).

Also, the values obtained from the R2 * technique depend on the iron and water content of the tissue; to perform the FDRI technique, we need two magnetic fields with two different strengths, and finally, in the COSMOS technique, the patient's head should be scanned in different directions(15–20).

These limitations make the widespread use of these methods complicated in the clinical field.

One of the essential features of body tissues is their magnetic susceptibility, which is an inherent characteristic of body tissues in response to the application of external magnetic fields and expresses tissue constituents.

The presence of diamagnetic substances such as calcium and white matter in the brain reduces magnetic susceptibility, and the presence of paramagnetic substances such as iron increases the magnetic susceptibility of the tissue(21).

Recently, a technique based on changes in magnetic susceptibility has been introduced called quantitative susceptibility mapping (QSM), which can be applied to many routine MRI sequences(16, 22).

This technique involves post-processing algorithms applied to the phase and magnitude images obtained with different MRI sequences and creates a map in part-per-million (ppm) that shows the areas increased or decreased in terms of changes in magnetic susceptibility(23–25).

So far, numerous researches have been done in using this technique to evaluate neurodegenerative diseases(11, 26, 27).

This research project aims to identify the first brain nuclei pathological changes in the spectrum of cognitive disorders, which leads to the introduction of a suitable biomarker for the early detection of cognitive impairment.

2-1. Database

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).

The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment MCI and early AD.

The ADNI3 project was launched in 2016 to determine the relationships between clinical, cognitive, imaging, genetic, and biochemical biomarkers across the spectrum of AD.

Scientists of this institution are working on 59 research centers in the United States and Canada. This project aims to identify the pathological changes in the brain that trigger these disorders.

2-2. Participants

Based on the available data, 35 cognitively normal and 46 participants with cognitive impairment disorders were included in this study.

The cognitive disorders group is classified into four groups based on cognitive test scores and assessments performed by ADNI specialists.
Inclusion criteria for subject selection include:

1. People should be in one of the five groups CN, SMC, EMCI, LMCI, and AD.

2. Multi-echo GRE and T1W scans of these people should be available.

3. Phase and magnitude images should be available separately.

4. Information about cognitive information such as MMSE scores should be available.

Participants' demographic characteristics information is given in Table 1.

(Table1)

2-3. MRI Acquisition

All patient’s MRI scans were performed using the Siemens Prisma 3.0T MRI scanner equipped with a Head-Neck coil.

3D Accelerated_Sagittal_MPRAGE sequence were acquired with the following parameters: TR (ms)=2300, TE/TI (ms)=2.98/ 900, Slice Thickness (mm)=1, Flip Angle=9, Pixel Bandwidth=240 and Acquisition Matrix and Reconstruction Matrix=240.

GRE multi-echo sequence was obtained with 3 different time of echoes (3TE) with the following parameters: TR (ms)=650, TE1-TE2-TE3 (ms)=6.09-13-20, Slice Thickness (mm)=4, Flip Angle=20, Pixel Bandwidth=260, Matrix size=256×256, Voxel size x (mm)= 0.859375, Voxel size y (mm)=0.859375 and Number of slices=44.

2-4. Image Processing

2-4-1. Quantitative Susceptibility Mapping

QSM reconstruction has four steps: generating tissue mask, phase unwrapping, background field removal, and field-to-susceptibility inversion (Figure1).
Each of these steps is performed with different algorithms and toolboxes(23).
After making QSM images, it is time to segment and determine the region of interest (ROIs) to evaluate the QSM values of each area in terms of ppm.
The basis of this reconstruction is done on the brain tissue, so first, we need to separate the area of the brain tissue from the skull bone in the magnitude image.
This brain tissue extraction was performed using the Brain Extraction Tool from FMRIB Software Library (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET)(28).

In the next step, to prevent the occurrence of the aliasing artifact in the phase images and total QSM reconstruction, we performed the phase unwrapping using a Laplacian-based phase unwrapping tool from STI Suite MATLAB toolbox (https://www.eecs.berkeley.edu/~chunlei.liu/software.html)(29).

In order to eliminate the unwanted consequences of the border areas, the background phase removal was done using the V-SHARP tool from the STI suite MATLAB toolbox.

Finally, QSM reconstruction or the susceptibility map was computed by the streaking artifacts reduction (STAR) algorithm(30, 31).

For the accuracy of the between-group analysis, a reference was made to the brain mask during QSM reconstruction(32).

The SEPIA toolbox was used to perform the above steps ( https://github.com/kschan0214/sepia)(33).

2-4-2. Automatic Segmentation

To create the region of interest (ROIs) mask, we performed automatic segmentation using the FMRIB Software Library, a model-based segmentation/registration tool.(https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST)(34).

At this stage, the location of 12 nuclei was determined, including left-thalamus, left-caudate, left-putamen, left-pallidum, left-Hippocampus, left-amygdala, right-Thalamus, right-caudate, right-putamen, right-pallidum, right-hippocampus, and right-amygdala.

2-5. Quantitative Susceptibility Analysis

In this step, we measured the magnetic susceptibility values of each nucleus using 3D Slicer software (http://www.slicer.org)(35).

For each participant, the segmented mask and QSM image were defined as input for the software; and based on the FSL guide (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST/UserGuide), we specified the nuclei name of each segment.

Finally, we saved the mean magnetic susceptibility value for further statistical analysis from the calculated statistical parameters for each nucleus.

2-6. Statistical analysis

Statistical analyzes were performed with the help of IBM Statistic SPSS (Statistical package for social science) software V26.

Comparing the age and MMSE scores variables among the groups was done using one-way analysis of variance (ANOVA).

The chi-squared test was used for the same purpose about the qualitative variable of gender.

After examining the normal distribution of data with the Kolmogorov-Smirnov test, ANOVA with post hoc test (Tukey-Kramer test) was used to assess the significant difference between the mean magnetic susceptibility of brain nuclei in 5 groups for normality values.

Kruskal–Wallis, and Mann–Whitney–Wilcoxon tests were also performed for non-normality values with the same purpose.

Finally, ROC curve analysis was performed on the brain nuclei of participants in all five groups.

P ≤ 0.05 was considered statistically significant in all statistical tests.

(Figure1)

3-1. Participants characteristics

Based on the results, the age variable did not differ significantly between the study groups, while there was a significant difference between MMSE scores among the target groups.

MMSE scores were significantly different between AD groups and SMC (P <0.001), AD and EMCI (P <0.001), AD and control (P <0.001), SMCI and LMCI (P =0.025), and LMCI compared to control (P =0.002).

Also, a significant difference was observed between the number of men and women in the study groups (P=0.04); the analyzed demographic information can be seen in Table1.

3-2. Evaluation of regional susceptibility

Based on the results of the analysis, QSM values in the EMCI group significantly increased in the left thalamus (P =0.021), left amygdala (P =0.006), and right thalamus (P =0.049) compared to the CN group (Figure2, Table2, Table3, Table4).

(Figure2)

(Table2)

(Table3)

(Table4)

3-3. ROC curve analysis

None of the nuclei had the sensitivity and specificity necessary to diagnose SMC patients from the CN group (Table5).

In the EMCI group, Area Under the Curve (AUC) values in the nuclei of the left thalamus (P=0.017), left putamen (P=0.002), right thalamus (P=0.006), left amygdala (P=0.005), and right putamen (P=0.005) were greater than 0.7(Table6).

The highest sensitivity is related to the left amygdala and left putamen nuclei, with 81% in this group.

In the LMCI group, only the left amygdala nucleus with an AUC value greater than 0.7 (P = 0.047) is effective in diagnosing LMCI patients; the sensitivity of this variable is 66%, and its specificity is 75%(Table7).

Furthermore, in the AD group, the highest AUC values were obtained in the left and right thalamic nuclei, which are 0.7 (P=0.050) and 0.76 (P=0.011), respectively.

These two nuclei also have the highest sensitivity in identifying AD patients based on QSM values (Table8) (Figure3).

(Figure3)

(Table 5)

(Table 6)

(Table7)

(Table8)

This research project was implemented to achieve several goals.

Initially, identifying the brain nucleus that undergoes the first changes in magnetic susceptibility in the early stages of cognitive disorders to introduce biomarkers for early detection of these stages.

Finally, the efficiency, sensitivity, and specificity of the QSM technique in diagnosing each group of patients were evaluated using the ROC curve.

4.1. Evaluation of regional susceptibility

Based on the results of previous research projects, the formation of primary amyloid-beta plaques is closely related to iron accumulation and the increase in iron deposits with the production of more amyloid-beta peptides (4, 36, 37).

Microscopic changes often lead to changes in the intrinsic properties and magnetic susceptibility of the tissue.
Since the increase in QSM values is strongly proportional to the increase in iron deposition, biomarkers can be introduced to identify these disorders early(38) (39).

In the present study, a notable increase in QSM values of the left amygdala, right thalamus, and left thalamus of the EMCI group was observed compared to the control group, indicating the onset of pathological changes leading to neuronal activity damage at the beginning of cognitive impairment.

This increase in QSM values is also present in the nuclei of the left amygdala, right thalamus, and left thalamus of patients with AD, but there is no statistically significant difference.

The occurrence of the plateau effect may explain the reason for this effect in AD group(40).

These findings are in line with the results of previous studies in different study groups (41-43).

4-2. ROC curve analysis

This section aims to assess the efficiency, sensitivity, and specificity of the QSM technique as a new and non-invasive method in diagnosing patients with a range of cognitive disorders.

Initially, the performance of this technique in distinguishing the SMC group was measured using the ROC curve.

Despite the relatively good sensitivity and specificity of some brain nuclei, due to the low level of AUC, none of the nuclei is suitable for differentiating SMC patients from healthy individuals.

In the EMCI group, the left putamen and left amygdala nuclei are efficient and sensitive for diagnosing EMCI patients; in 81% of cases, people with this disease can be correctly diagnosed by referring to the QSM values of these brain nuclei(42, 44).

In the LMCI group, only the left amygdala nucleus with appropriate efficiency, sensitivity, and specificity can diagnose these patients, which is in line with the results of Kim's research(42).

Finally, the use of this method in the AD group was evaluated using the ROC curve.

In this group, the right thalamus and the left thalamus nuclei have the appropriate efficiency, sensitivity, and specificity to differentiate AD patients from healthy individuals, which aligns with Kim and Li's research results (42, 44).

The most critical limitations were the lack of access to data from clinical research centers and sufficient data in databases.

It is undoubtedly preferable to do this research on a high sample size.

Differences in gender distribution were other limitations of this study.

The left amygdala nuclei, right thalamus, and left thalamus are the first areas exposed to iron deposition at the onset of cognitive impairment.

Due to the high efficiency, sensitivity, and specificity of QSM values of these nuclei, especially the left amygdala, they can be used as biomarkers for the early detection of cognitive disorders.

In general, the QSM technique to diagnose and monitor a range of cognitive impairments is ideal.

Abbreviation table
AD	Alzheimer's disease	ADNI	Alzheimer’s disease neuroimaging initiative
ANOVA	one-way analysis of variance	AUC	Area Under the Curve
COSMOS	Calculation Of Susceptibility through Multiple Orientation Sampling	EMCI	early mild cognitive impairment
FDRI	Field-Dependent Relaxation rate Increase	LMCI	late mild cognitive impairment
MMSE	Mini-Mental State Exam	MRI	Magnetic Resonance Imaging
PET	Positron Emission Tomography	ppm	part-per-million
QSM	Quantitative Susceptibility Mapping	R2*	Transverse Relaxation Rate
ROC	receiver operating characteristic curve	ROI	region of interest
SMC	subjective memory concerns	SWI	Susceptibility-Weighted Imaging
*T2WI**	T2 * Weighted Imaging

Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

*Ethics approval and consent to participate:

Our project has been approved with the following code of ethics in Mashhad University of Medical Sciences, Iran:

IR.MUMS.MEDICAL.REC.1400.510

The online version of the approval is available at the following address and is open to the public:

https://ethics.research.ac.ir/IR.MUMS.MEDICAL.REC.1400.510

*Consent for publication:

Not applicable

*Availability of data and material:

Patient data were collected from the ADNI database and used in the study.
Raw data can be downloaded from the ADNI database for free.
https://adni.loni.usc.edu/

*Competing interests:

There are no relevant financial or non-financial competing interests to report.

*Funding:

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI).

*Authors' contributions:

Farzaneh Nikparast: Execution of research plan and article writing

Shabnam Niroumand: Monitoring the accuracy of statistical analyzes

Hossein Akbari-Lalimi: Collaborate on the proper execution of image processing

Dr.Hoda Zare: Author responsible and supervising the scientific accuracy of the content written in this article.

*Acknowledgements:

*Authors' information (optional):

Farzaneh Nikparast: MSc Of Medical Imaging, Email: [email protected]

Shabnam Niroumand: Assistant Professor Of Social Medicine,Email: [email protected]

Hossein Akbari-Lalimi: PhD Of Medical Physics,Email: [email protected]

Dr Hoda Zare: PhD Of Medical Physics, Email: [email protected]

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Table 1

the results of the participant's demographic analysis

Characteristic	CN (35)	SMC (10)	EMCI (17)	LMCI (9)	AD (10)	P-Value
Age (Mean SD)	73.34 8.07	77.7 3.65	76.76 6.28	77.44 5.15	78.7 6.83	P=0.11
MMSE Score (Mean SD)	29.0 1.43	28.80 1.13	27.35 1.96	24.6667 6.81	21.75 4.30	P < 0.001
Sex	23Female and 12 Male	7 Female and 3 Male	4 Female and 13 Male	4 Female and 5 Male	5 Female and 5 Male	P=0.04

CN, cognitively normal; SMC, subjective memory concerns; EMCI, early mild cognitive impairment; LMCI, late mild cognitive impairment; AD, Alzheimer's disease; MMSE, Mini-Mental State Examination.

P ≤ 0.05 was considered statistically significant and bold font indicates statistical significance

Table 2

Comparison of brain nuclei QSM values between study groups using one-way analysis of variance

One Way ANOVA
		group
		AD	SMCI	EMCI	LMCI	control	P-Value
Left Thalamus	Mean	.0031	-.0083	.0035	-.0020	-.0045	0.004*
Left Thalamus	Std. Deviation	.01086	.01034	.01116	.00875	.00829	0.004*
Left Caudate	Mean	.0152	.0236	.0249	.0154	.0377	0.037*
Left Caudate	Std. Deviation	.01871	.01765	.03994	.02255	.02373	0.037*
Left Putamen	Mean	.0390	.0234	.0411	.0279	.0220	0.066
Left Putamen	Std. Deviation	.03391	.01564	.02076	.02006	.02257	0.066
Right Thalamus	Mean	.0023	-.0095	.0013	-.0033	-.0074	0.008*
Right Thalamus	Std. Deviation	.01264	.01262	.00934	.01028	.00879	0.008*
Right Caudate	Mean	.0196	.0242	.0257	.0135	.0293	0.291
Right Caudate	Std. Deviation	.02175	.01804	.02953	.01981	.02154	0.291
Right Putamen	Mean	.0346	.0237	.0442	.0331	.0243	0.208
Right Putamen	Std. Deviation	.03388	.01983	.03104	.02128	.02194	0.208
Right Pallidum	Mean	.0374	.0423	.0528	.0587	.0564	0.132
Right Pallidum	Std. Deviation	.03428	.03129	.01857	.02710	.02128	0.132
Right Hippocampus	Mean	-.0333	-.0097	-.0148	-.0108	-.0082	0.224
Right Hippocampus	Std. Deviation	.04090	.02633	.03601	.03147	.02352	0.224

*p ≤ 0.05 was considered statistically significant and bold font indicates statistical significance

Table 3

Multiple comparisons of QSM values of ROI (dependent variables) in SMC, EMCI, LMCI, and AD compared to the control group using post hoc multiple comparison (Tukey-Kramer) test.

Multiple Comparisons Tukey HSD
Brain nucleus	(I) group	(J) group: control	P-Value
Brain nucleus	(I) group	Mean Difference (I-J)	P-Value
Left thalamus	AD	0.00810	.141
	SMC	-.00330	.873
	EMCI	.00886^*	.021*
	LMCI	.00300	.919
Left caudate	AD	-.02405	.097
	SMC	-.01565	.478
	EMCI	-.01345	.437
	LMCI	-.02385	.127
Right thalamus	AD	.00961	.074
	SMC	-.00224	.972
	EMCI	.00859^*	.049*
	LMCI	.00399	.831

*p < 0.05 was considered statistically significant and bold font indicates statistical significance

Table 4

Mean rankings related to Kruskal-Wallis test for Comparison of brain nuclei QSM values between study groups.

P-Value	AD	LMCI	EMCI	SMC	CN
.486	34.40	40.78	37.47	35.50	46.23	Left Pallidum
.535	29.30	44.44	43.53	37.90	42.00	Left Hippocampus
.044*	42.90	48.11	51.88	45.20	32.14	Left Amygdala
.850	39.50	47.00	40.29	44.30	38.06	Right Amygdala

*p < 0.05 was considered statistically significant and bold font indicates statistical significance

Table 5

The Results of ROC curves analysis of QSM values were obtained from the twelve region-of-interest (ROIs) in the subjects with subjective memory concerns (SMC).

P-Value	AUE	Cut-off point	Sensitivity (%)	Specificity (%)	Nucleus
0.497	0.57	0.02	60	66	Left Putamen
0.148	0.65	0.03	50	94	Left Amygdala
1.000	0.5	0.03	50	66	Right Putamen
0.813	0.62	-0.018	80	41	Right Hippocampus
0.330	0.60	0.01	50	85	Right Amygdala

*p ≤ 0.05 was considered statistically significant and bold font indicates statistical significance

Table 6

The Results of ROC curves analysis of QSM values were obtained from the twelve region-of-interest (ROIs) in the subjects with early mild cognitive impairment (EMCI).

P-Value	AUE	Cut-off point	Sensitivity (%)	Specificity (%)	Nucleus
0.017	0.71	0.002	56	82	Left Thalamus
0.002	0.77	0.03	81	82	Left Putamen
0.896	0.51	0.001	50	66	Left Hippocampus
0.005	0.74	-0.006	81	72	Left Amygdala
0.006	0.74	-0.005	75	66	Right Thalamus
0.005	0.75	0.03	75	82	Right Putamen
0.726	0.53	0.07	25	100	Right Amygdala

*p ≤ 0.05 was considered statistically significant and bold font indicates statistical significance

Table 7

The Results of ROC curves analysis of QSM values were obtained from the twelve region-of-interest (ROIs) in the subjects with early mild cognitive impairment (LMCI).

P-Value	AUE	Cut-off point	Sensitivity (%)	Specificity (%)	Nucleus
0.284	0.61	-0.004	77	54	Left Thalamus
0.361	0.60	0.003	100	22	Left Putamen
0.925	0.51	0.01	55	72	Left Hippocampus
0.047	0.71	0.003	66	75	Left Amygdala
0.176	0.64	-0.006	77	60	Right Thalamus
0.378	0.59	0.04	44	81	Right Putamen
0.156	0.65	0.026	44s	91	Right Amygdala

*p ≤ 0.05 was considered statistically significant and bold font indicates statistical significance

Table 8

The Results of ROC curves analysis of QSM values were obtained from the twelve region-of-interest (ROIs) in the subjects with Alzheimer's disease (AD).

P-Value	AUE	Cut-off point	Sensitivity (%)	Specificity (%)	Nucleus
0.050	0.7	-0.004	90	64	Left Thalamus
0.104	0.67	0.076	10	94	Left Putamen
0.140	0.65	-0.003	70	72	Left Amygdala
0.011	0.76	-0.005	90	66	Right Thalamus
0.376	0.59	0.040	50	82	Right Putamen
0.768	0.53	0.0292	40	10	Right Amygdala

*p ≤ 0.05 was considered statistically significant and bold font indicates statistical significance

No competing interests reported.

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Early pathological changes initiating cognitive disorders: Which nucleus is most prone to the iron deposition?

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1. Introduction

2. Materials And Methods

3. Results

4. Discussion

5. Limitations

6. Conclusion

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