New Results

A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

Jafar Zamani, Ali Sadr, Amir-Homayoun Javadi
doi: https://doi.org/10.1101/2020.08.18.256321

Abstract

Background Alzheimer’s disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC).

Methods A total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer’s disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain.

Results While HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite.

Conclusion Our results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

1 Introduction

Alzheimer’s disease (AD) is a devasting neurodegenerative disease, contributing to 60-70% of dementia cases 1. Currently there are around 50 million people with dementia worldwide. In 2015, the total global societal cost of dementia was estimated to be US$ 818 billion 2,3 Mainly due to increased life-expectancy, the total number of people with dementia is projected to reach 82 million (64% increase) in 2030, with between 49 and 57 million of these cases being AD 4 Whilst several drugs are available to mitigate the symptoms in some cases, no treatments are available that prevent progression from the relatively late stage at which the disease is diagnosed 5.

AD is characterised by two main pathological hallmarks: extracellular amyloid deposits, composed of insoluble amyloid beta (Aβ) protein, and intra-neuronal neurofibrillary tangles (NFTs), containing hyperphosphorylated tau protein 6. AD is also characterised by a significant loss of neurons and synapses, resulting in brain shrinkage and atrophy 7,8. Structural changes have been shown to be one of the earliest biomarkers that can be used in the diagnosis of AD 9–11. Much effort has been devoted to find patterns of changes in the structure of different brain areas that can be reliably used for diagnosis of AD 12

Earlier investigations relied mostly on manual segmentation of brain areas requiring a great deal of expertise and time 13–16. Therefore, the majority of the focus has been devoted to changes in the hippocampus due to its distinct structure 17 It has been shown that a loss in hippocampal volume can be an indication of AD 18,19 Further investigations have looked at subfields of the hippocampus, showing a nonuniform rate of neuroplasticity due to their specialisation 20–22. For example, it has been shown that NFT begin in the medial temporal region and exhibit a characteristic distribution pattern across subfields, starting in the CA1 and later spreading to subiculum, CA2, CA3 and CA4/Dentate Gyrus 23–27.

With the development of semi- and fully-automated segmentation methods, however, it has now become easier and faster to segment not only the hippocampal area, but also other brain areas 28–32. HIPpocampus subfield Segmentation (HIPS) 33, volBrain 34, Computational Anatomy Toolbox (CAT) 35,36, BrainSuite 37,38 and FreeSurfer 39 are some of the commonly used semi- and fully-automated methods. These methods, however, are yet under development 40,41. For example, CA1 segmentation in the FreeSurfer v5.3 was partially included in the subiculum 18 potentially explaining why the CA1 field was reported to be insensitive to AD pathology in some 42,43 but not all 44,45. Similar findings have recently raised questions and concerns regarding the accuracy and consistency of these methods 46–49 Therefore, it is important to investigate the accuracy of these methods further 50.

Benefiting from the computational power of automated methods, analysis of a large number of brain images has become more feasible. Large datasets of brain scans such as Minimal Interval Resonance Imaging in Alzheimer’s Disease (MIRIAD), a public database of Alzheimer’s magnetic resonance imaging (MRI) 51, offer a great opportunity to have a more comprehensive approach to the underlying mechanism and progression of AD 52–56. It also facilitates multisite studies to form a more accurate understanding of the disease 57,58.

Mini mental state examination (MMSE) is one of the commonly accepted measurements of cognitive ability, in particular in clinical settings 59–61. This measure has been widely used in classification of AD. For example, MIRIAD classifies participants with score between 12 and 26/30 as AD and those higher than 26/30 as healthy control. There is huge body of literature showing correlation between MMSE score and brain atrophy 62–64

The aim of this study was to investigate the reliability of four automated segmentation methods of volBrain, CAT and BrainSuite for segmentation of the whole brain, and HIPS for segmentation of subfields of hippocampus, which belongs to the same analysis tool as volBrain. We used images belonging to MIRIAD. Correlation of the volume of each brain area with MMSE scores are also investigated. To investigate the reliability of the three methods volBrain, CAT and BrainSuite, the correlation of their common brain areas is also reported.

2 Material and Methods

2.1 Subjects

We used 44 images (22 AD and 22 HC) taken from the Minimal Interval Resonance Imaging in Alzheimer’s Disease (MIRIAD) dataset 51. Table 1 shows a summary of the descriptives of the participants.

View this table:
Table 1.

Comparison between Alzheimer’s disease (AD) and healthy controls (HC).

2.2 Magnetic Resonance Imaging (MRI)

Data was extracted from MIRIAD database. All subjects underwent MRI scanning on a 1.5 T Signa scanner (GE Medical Systems, Milwaukee, WI, USA). T1-weighted volumetric images were obtained using an inversion recovery prepared fast spoiled gradient echo sequence with acquisition parameters time to repetition = 15 ms, time to echo = 5.4 ms, flip angle = 15°, TI = 650 ms, a 24-cm field of view and a 256 × 256 matrix, to provide 124 contiguous 1.5-mm thick slices in the coronal plane (voxels 0.9735 × 0.9735 × 1.5 mm3) 51.

2.3 Methods

HIPS and volBrain

The volumes of Cerebrospinal fluid (CSF), white matter (WM), grey matter (GM), brain hemispheres, cerebellum and brainstem were obtained using volBrain pipeline 34. This method is based on an advanced pipeline providing automatic segmentation of different brain structures from T1 weighted MRI, Figure 1. The preprocessing is based on the following procedure: (1) a denoising step with an adaptive non-local mean filter, (2) an affine registration in the Montreal Neurological Institute (MNI) space, (3) a correction of the image inhomogeneities, and (4) an intensity normalisation. (5) Afterwards, MRI images are segmented in the MNI space using non-local patch-based multi-atlas method. Images were corrected for intensity inhomogeneity using the N4 algorithm 65, and the images were segmented into brain/non-brain using a semi-automated technique (MIDAS). The Non-Local Means filter 66 was applied to each pixel of the image by computing a weighted average of surrounding pixels using a robust similarity measure that takes into account the neighbouring pixels surrounding the pixel being compared. This segmentation method is based on the idea of non-local patch-based label fusion technique, where patches of the brain image to be segmented are compared with those of the training library, looking for similar patterns within a defined search volume to assign the proper label 67,68. HIPS and volBrain are used for segmentation of the hippocampus subfields and the rest of the brain, respectively 33.

Figure 1.
Figure 1.

Processing pipeline for volBrain and HIPS adapted from Manjón and Coupé (2016) 34 under the terms of the Creative Commons Attribution License (CC BY).

CAT

Computational Anatomy Toolbox (CAT) is a powerful package for brain T1-MRI data segmentation, Figure 2. It is a voxel base estimation method 69. The CAT preprocessing steps are as follows: (1) spatial registration to a template, (2) tissue segmentation into grey, white matter and CSF, and (3) bias correction of intensity non-uniformities. (4) Finally, segments are extracted by scaling the amount of volume changes based on spatial registration, so that the total volume of grey matter in the modulated image remains the same as the original image. For correction of the orientation and size of the brain, non-linear registration methods are applied to the image 70. Projection-based thickness (PBT) method is used for calculation of the cortical thickness and central surface 70,71. Spatial-adaptive Non-Local Means (SANLM) and classical Markov Random Field (MRF) were used for image Denoising 72. Adaptive Maximum a Posterior (AMAP) method was used for segmentation 69

Figure 2.
Figure 2.

Processing pipeline for CAT

BrainSuite

BrainSuite is an open source software tool that enables largely automated cortical surface extraction from MRI of the brain, Figure 3. BrainSuite includes automatic cortical surface extraction, bias field correlation, cerebrum labelling, and surface generation features. Also, this toolbox is used in tractography and connectivity matrix calculation in diffusion imaging data 37.

Figure 3.
Figure 3.

Processing pipeline for BrainSuite

2.4 Statistical Analysis

Independent-sample t-tests are run to compare the volume of different brain areas between the AD and HC groups for volBrain, CAT and BrainSuite for the whole brain, and HIPS for the hippocampus subfields. Bivariate-correlation analyses are also run to investigate the relationship between volume and MMSE scores for all four segmentation methods. Correlational analyses are run between the common brain areas in volBrain, CAT and BrainSuite to investigate the relationship between the three methods. Bonferroni correction is applied to account for multiple comparison.

3 Results

Using three automatic segmentation methods CAT, volBrain and BrainSuite, we segmented the whole brain, and using HIPS we segmented the hippocampus. Using independent-sample t-tests we compared the volumetric data for AD and HC for each segment. Figures 3–6 show sample output images for one AD patient and one HC participant. Furthermore, we investigated the correlation of volumetric data with MMSE scores in both groups.

Figure 4.
Figure 4.

Subcortical structures in an AD patient and a HC participant using CAT segmentation method. The histograms show the volume of each brain area

Figure 5.
Figure 5.

Cerebellum MRI-T1 brain segmentation in an AD patient and a HC participant using volBrain segmentation method.

Figure 6.
Figure 6.

Cerebellum MRI-T1 brain segmentation in an AD patient and a HC participant using BrainSuite segmentation method.

Figure 7.
Figure 7.

Left and right hippocampus subfield segmentation in an AD patient and a HC participant using HIPS segmentation method.

CAT segmentation method returned data for 63 distinct brain areas. This method highlighted many brain areas that are significantly different between the two groups, Table 2. In particular fusiform gyrus, parahippocampal gyrus, hippocampus, entorhinal cortex, amygdala, temporal gyri, thalamus, nucleus accumbens, insula, caudate and precuneus were significantly different. Importantly, the size of all these brain areas showed a strong correlation with MMSE scores. For further details see supplementary figures 1-3.

View this table:
Table 2.

Summary of the independent-sample t-tests comparing volumetric data between the participants with Alzheimer’s disease and healthy controls and the correlation of the data with MMSE scores using CAT method.

volBrain segmentation method returned data for eight distinct brain areas. In particular the amygdala, hippocampus, nucleus accumbens, thalamus and caudate were significantly different between the two groups, Table 3. Again, the size of all these brain areas showed a strong correlation with MMSE scores. For further details see supplementary figures 4-6.

View this table:
Table 3.

Summary of the independent-sample t-tests comparing volumetric data between the participants with Alzheimer’s disease and healthy controls and the correlation of the data with MMSE scores using volBrain method.

BrainSuite segmentation method returned data for 50 distinct brain areas. In contrast to CAT and volBrain, this method highlighted only six brain areas that are significantly different between the two groups, Table 4. These brain areas included temporal gyri, third ventricle, supramarginal gyrus and angular gyrus. Similar to previous segmentation methods, all these brain areas showed strong correlation with MMSE scores. For further details see supplementary figures 7-9.

View this table:
Table 4.

Summary of the independent-sample t-tests comparing volumetric data between the participants with Alzheimer’s disease and healthy controls and the correlation of the data with MMSE scores using BrainSuite segmentation method.

HIPS segmentation method returned data for the whole hippocampus and five of its subfields: CA1, CA2-CA3, CA4/Dentate Gyrus, Subiculum and strata radiatum/lacunosum/moleculare (SR-SL-SM). All these areas showed a significant difference between the two groups, Table 5. The size of hippocampus and all its subfields showed strong correlation with MMSE scores. For further details see supplementary figures 10-12.

View this table:
Table 5.

Summary of the independent-sample t-tests comparing volumetric data between the participants with Alzheimer’s disease and healthy controls and the correlation of the data with MMSE scores using HIPS segmentation method.

To investigate the relationship between the three whole-brain segmentation methods CAT, volBrain and BrainSuite, we ran correlational analysis, Table 6. Seven brain areas were common between these methods: nucleus accumbens, amygdala, caudate, globus pallidus, hippocampus, putamen and thalamus. CAT and volBrain showed strong correlation for nucleus accumbens, amygdala, caudate, hippocampus and thalamus. Two brain areas globus pallidus and putamen were not significantly correlated. These brain areas did not show significant difference between the two groups either. BrainSuite, however, showed no significant correlation with either of the other two segmentation methods.

View this table:
Table 6.

Correlation of the size of common brain areas reported by the three segmentation methods

4 Discussion

We used HIPS automated method to segment the subfields of hippocampus, and CAT, volBrain and BrainSuite automated methods to segment the whole brain using T1 weighted MRI data. Our results showed that all subfields of hippocampus in the Alzheimer’s Disease (AD) group were significantly smaller than those of the healthy control (HC) group. The atrophy of all subcomponents of hippocampus were correlated with the MMSE measure. Quite a large portion of cortical and subcortical areas in the brain were also smaller in the AD group as compared to the control group, as evident from CAT and volBrain segmentation results. The shrinkage in these brain areas mostly showed a strong correlation with MMSE measure. BrainSuite failed to discriminate between the two groups. While CAT and volBrain shows a strong correlation, BrainSuite did not show any significant correlation with CAT and volBrain.

With the advancement of computational methods, fine-grain analysis of the brain areas is more feasible 73–75. Earlier methods relied heavily on manual segmentation of the brain areas, which was extremely time demanding and also required a great level of expertise. Therefore, the majority of the analysis was limited to brain areas with more distinct structure, such as the hippocampus. Many semi- and fully-automated segmentation methods have been developed. While these methods have been used more commonly in recent years, the reliability and accuracy of these methods was yet to be fully studied. We used four pipelines of HIPS 33, volBrain 34, CAT 35,36 and BrainSuite 37 In this study we evaluated their reliability by looking at their ability to discriminate between AD and HC groups, whether a correlation existed between them, their correlation with MMSE scores, and comparing their results with past literature. Our results showed strong reliability of HIPS, volBrain and CAT. These methods have been successfully applied to brain images from those with AD 76–79

BrainSuite, however, underperformed greatly. For example, it failed to accurately segment the hippocampus, thalamus and amygdala to show a significant difference between the two groups. While this automatic segmentation method has been used frequently in past research 80, its application has been mostly limited to the processing of brains with no atrophy 81,82, as well as detection of gross segments such as tumours 83. This highlights the importance of validation studies such as ours to gain a greater understanding of the applications and limitations of different methods 84–87

The volume of the hippocampus is considered as an important biomarker for AD and has been included in recently proposed research diagnostic criteria 88,89. It has been shown that the hippocampal atrophy estimated on anatomical T1 weighted MRI can help in classifying the different stages of AD 90–92 Confirming past literature, our results showed that the hippocampus volume significantly differed between AD and the control group.

Histological studies have shown that lesions are not uniformly distributed within the hippocampus 91,93. Neuronal loss results in a reduction of the thickness of the layers richer in neuronal bodies, while the loss of synapses results in the reduction of the layers poorer in neuronal bodies 94–97 and these changes are stage-dependent 98,99 Our results, however, failed to differentiate the contribution of these subfields in AD; they all showed significant reduction in size, compared to the control group. This effect could be because our AD group consisted of those with later stages of AD. The contribution of different subfields of the hippocampus is more visible in those with mild cognitive impairment 100–102 Therefore, in future studies it would be informative to include participants with different stages of AD to investigate the contribution of different subfields of the hippocampus in AD.

While the contribution of atrophy in the hippocampus has been widely studied, the role of atrophy in the rest of the brain in AD is less clear 40. An important contributing factor is that the boundaries of the hippocampus are easier for human operators or automated algorithms to recognise than other brain areas such as the amygdala, entorhinal cortex or thalamus 40. Due to methodological advances, however, it is now possible to measure atrophy across the entire cortex with good precision 103. Our results from CAT and volBrain methods showed strongly significant differences between many brain areas such as the amygdala, thalamus, nucleus accumbens, insula and caudate. These findings are in-line with past literature showing similar differences in these brain areas 40,104–106.

There is a growing body of literature showing a correlation between cognitive decline and brain atrophy 62,107,108. For example, it has been shown that basal forebrain changes are correlated with cognitive decline in MCI and AD patients, as measured with recall task and MMSE 109,110, as well as healthy participants that later progressed to AD 111. Atrophy of other brain areas such as lateral and medial parietal cortex, as well as lateral temporal cortex have also been shown to have a correlation with cognitive decline 112 Our results showed a strong correlation between brain atrophy and cognitive decline as measured by MMSE. All brain areas that were significantly different between the AD and the control group showed a significant correlation with MMSE, except for the caudate (CAT p = 0.001155, volBrain p = 0.005091, Bonferroni corrected statistic not significant). While the effect of shrinkage of the caudate in AD is not very clear 113, there is some evidence that caudate volume has a correlation with MMSE measures, although not as strongly as other brain areas such as the thalamus 63,114 An important consideration is that atrophy in the left caudate has a stronger role in AD, as compared to the right caudate 113,115. Our analysis combined both the left and right caudate, which may have led to this inconsistency between our results and previous literature.

Although AD commonly presents as an amnestic syndrome, there is significant heterogeneity across individuals 116, which is accompanied by different atrophy patterns 64,117–119 For example, while those with more language difficulties might exhibit greater atrophy in temporal or parietal regions 120,121, those with more visual difficulties might have greater atrophy in posterior cortical regions 122,123. Availability of the automated systems offers many opportunities, such as the ability to analyse a large number of brain images with reasonable time and expertise. This is in particular very appealing, considering the increased number of large datasets such as MIRIAD. Automated systems can go through the collection and aggregate data from a wide range of participants, healthy and patients to gain a greater understanding of AD. This is important considering the heterogeneity of the disease and its progression 124.

Another application of automated systems is in clinical settings. By the time of diagnosis, rapid ongoing atrophy is already far advanced 125,126. Early diagnosis of AD can help with deceleration of the progression of the disease 21,127,128. This is particularly important as there are modifiable factors that can help with brain health129–131. Therefore, a massive effort has been devoted to the development of diagnostic methods to enable researchers and clinicians to detect AD and cases with potential progression to AD, as early as possible 132,133. For the development of preventive strategies, it is important to predict future brain atrophy, as this may aid in identifying which individuals with normal cognition are more susceptible of progressing to later stages of AD 134–136. Automated systems provide additional information to clinicians, enabling them to have a greater understanding of the progression of the atrophy 12,137 Some of these methods have already received approval from different licensing bodies such as CE (European conformity) and FDA (food and drug administration, USA) approval 138. These methods, however, come with some limitations such as speed of processing, expensive licences, or requirement of other specialised software. This study is another step to evaluate freely available analytical tools to achieve an ideal analysis pipeline, suitable for researchers and clinicians.

Availability of the reliable automated segmentation methods enables researchers and clinicians to have a greater understanding of the underlying mechanisms and the progression of the AD. This will allow them to attempt to prevent or decelerate the progression of the disease more effectively. Future research can look at the rate of atrophy to predict the progression of disease 139–142 This rate can be helpful to have a more informed understanding whether an individual with MCI will later progress to AD or not 105,143,144 The output of automated segmentation methods can also be used in training of intelligent classification methods such as those using artificial neural networks and support vector machines, which has shown promising results 145–154

The purpose of this article was not to identify the superiority of any particular automatic segmentation method over another, but to solely highlight possible limitations and applications of four commonly used segmentation methods. We proposed that CAT, volBrain and HIPS are methods that can robustly operate on brain images with significant atrophy and can be used in research and clinical settings. BrainSuite, however, should be used with caution for brain images with atrophy.

Authors’ Contribution

JZ and AHJ analysed the data. JZ and AHJ wrote the manuscript. JZ, AHJ and AS revised the manuscript.

References

  1. 1.↵
    Scheltens, P. et al. Alzheimer’s disease. The Lancet 388, 505–517 (2016).
    OpenUrl
  2. 2.↵
    World Health Organization. Fact Sheet-Dementia. (2015).
  3. 3.↵
    Association, A. 2019 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia 15, 321–387 (2019).
    OpenUrlCrossRef
  4. 4.↵
    Nichols, E. et al. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology 18, 88–106 (2019).
    OpenUrl
  5. 5.↵
    Knapp, M. et al. Cost-effectiveness of donepezil and memantine in moderate to severe Alzheimer’s disease (the DOMINO-AD trial). International Journal of Geriatric Psychiatry 32, 1205–1216 (2017).
    OpenUrlCrossRefPubMed
  6. 6.↵
    Montine, T. J. et al. National institute on aging-Alzheimer’s association guidelines for the neuropathologic assessment of Alzheimer’s disease: A practical approach. Acta Neuropathologica 123, 1–11 (2012).
    OpenUrlCrossRefPubMedWeb of Science
  7. 7.↵
    Duyckaerts, C., Delatour, B. & Potier, M. C. Classification and basic pathology of Alzheimer disease. Acta Neuropathologica 118, 5–36 (2009).
    OpenUrlCrossRefPubMedWeb of Science
  8. 8.↵
    Edwards, F. A. A Unifying Hypothesis for Alzheimer’s Disease: From Plaques to Neurodegeneration. Trends in Neurosciences 42, 310–322 (2019).
    OpenUrlCrossRef
  9. 9.↵
    McConathy, J. & Sheline, Y. I. Imaging biomarkers associated with cognitive decline: A review. Biological Psychiatry 77, 685–692 (2015).
    OpenUrlCrossRefPubMed
  10. 10.
    Tabatabaei-Jafari, H., Shaw, M. E. & Cherbuin, N. Cerebral atrophy in mild cognitive impairment: A systematic review with meta-analysis. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring 1, 487–504 (2015).
    OpenUrl
  11. 11.↵
    Grundman, M. et al. Mild Cognitive Impairment Can Be Distinguished from Alzheimer Disease and Normal Aging for Clinical Trials. Archives of Neurology 61, 59–66 (2004).
    OpenUrlCrossRefPubMedWeb of Science
  12. 12.↵
    Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P. & Thompson, P. M. The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology 6, 67–77 (2010).
    OpenUrl
  13. 13.↵
    Mueller, S. G. et al. Hippocampal atrophy patterns in mild cognitive impairment and alzheimer’s disease. Human Brain Mapping 31, 1339–1347 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  14. 14.
    Kerchner, G. A. et al. Hippocampal CA1 apical neuropil atrophy in mild Alzheimer disease visualized with 7-T MRI. Neurology 75, 1381–1387 (2010).
    OpenUrlCrossRefPubMed
  15. 15.
    Yassa, M. A. et al. High-resolution structural and functional MRI of hippocampal CA3 and dentate gyrus in patients with amnestic Mild Cognitive Impairment. NeuroImage 51, 1242–1252 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  16. 16.↵
    Adachi, M. et al. Morphology of the inner structure of the hippocampal formation in Alzheimer disease. American Journal of Neuroradiology 24, 1575–1581 (2003).
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    McRae-McKee, K. et al. Combining hippocampal volume metrics to better understand Alzheimer’s disease progression in at-risk individuals. Scientific Reports 9, 1–9 (2019).
    OpenUrl
  18. 18.↵
    de Flores, R., la Joie, R. & Chételat, G. Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience 309, 29–50 (2015).
    OpenUrlCrossRefPubMed
  19. 19.↵
    Nobis, L. et al. Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank. NeuroImage: Clinical 23, 101904 (2019).
    OpenUrl
  20. 20.↵
    Mueller, S. G. & Weiner, M. W. Selective effect of age, Apo e4, and Alzheimer’s disease on hippocampal subfields. Hippocampus 19, 558–564 (2009).
    OpenUrlCrossRefPubMedWeb of Science
  21. 21.↵
    Nestor, P. J., Scheltens, P. & Hodges, J. R. Advances in the early detection of Alzheimer’s disease. Nature Medicine 10, S34–S41 (2004).
    OpenUrlCrossRefPubMedWeb of Science
  22. 22.↵
    Wisse, L. E. M. et al. Hippocampal subfield volumes at 7T in early Alzheimer’s disease and normal aging. Neurobiology of Aging 35, 2039–2045 (2014).
    OpenUrlCrossRefPubMed
  23. 23.↵
    Braak, H. & Braak, E. Alzheimer’s disease affects limbic nuclei of the thalamus. Acta Neuropathologica 81, 261–268 (1991).
    OpenUrlCrossRefPubMedWeb of Science
  24. 24.
    Bobinski, M. et al. Relationships between Regional Neuronal Loss and Neurofibrillary Changes in the Hippocampal Formation and Duration and Severity of Alzheimer Disease. Journal of Neuropathology and Experimental Neurology 56, 414–420 (1997).
    OpenUrlCrossRefPubMed
  25. 25.
    Schönheit, B., Zarski, R. & Ohm, T. G. Spatial and temporal relationships between plaques and tangles in Alzheimer-pathology. Neurobiology of Aging 25, 697–711 (2004).
    OpenUrlCrossRefPubMedWeb of Science
  26. 26.
    Li, F., Ball, S., Katz, B. & Smith, A. Case report of syncope during a transcranial direct current stimulation experiment in a healthy adult participant. Brain Stimulation 0, 1201–1202 (2018).
    OpenUrl
  27. 27.↵
    la Joie, R. et al. Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer’s disease and semantic dementia. NeuroImage: Clinical 3, 155–162 (2013).
    OpenUrl
  28. 28.↵
    Litjens, G. et al. A survey on deep learning in medical image analysis. Medical Image Analysis 42, 60–88 (2017).
    OpenUrlCrossRefPubMed
  29. 29.
    Han, X. et al. Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. NeuroImage 32, 180–194 (2006).
    OpenUrlCrossRefPubMedWeb of Science
  30. 30.
    Ambarki, K., Wåhlin, A., Birgander, R., Eklund, A. & Malm, J. MR imaging of brain volumes: Evaluation of a fully automatic software. American Journal of Neuroradiology 32, 408–412 (2011).
    OpenUrlAbstract/FREE Full Text
  31. 31.
    Kerchner, G. A. et al. Shared vulnerability of two synaptically-connected medial temporal lobe areas to age and cognitive decline: A seven tesla magnetic resonance imaging study. Journal of Neuroscience 33, 16666–16672 (2013).
    OpenUrlAbstract/FREE Full Text
  32. 32.↵
    Yushkevich, P. A. et al. Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Human Brain Mapping 36, 258–287 (2015).
    OpenUrlCrossRefPubMedWeb of Science
  33. 33.↵
    Romero, J. E., Coupé, P. & Manjón, J. v. HIPS: A new hippocampus subfield segmentation method. NeuroImage 163, 286–295 (2017).
    OpenUrlCrossRef
  34. 34.↵
    Manjón, J. v. & Coupé, P. Volbrain: An online MRI brain volumetry system. Frontiers in Neuroinformatics 10, 1–14 (2016).
    OpenUrl
  35. 35.↵
    Tzourio-Mazoyer, N. et al. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain. NeuroImage 15, 273–289 (2002).
    OpenUrlCrossRefPubMedWeb of Science
  36. 36.↵
    Rolls, E. T., Huang, C.-C., Lin, C.-P., Feng, J. & Joliot, M. Automated anatomical labelling atlas 3. NeuroImage 206, 116189 (2020).
    OpenUrl
  37. 37.↵
    Shattuck, D. W. & Leahy, R. M. BrainSuite: An automated cortical surface identification tool. Medical Image Analysis 6, 129–142 (2002).
    OpenUrlCrossRefPubMedWeb of Science
  38. 38.↵
    Shattuck, D. W. et al. Semi-automated method for delineation of landmarks on models of the cerebral cortex. Journal of Neuroscience Methods 178, 385–392 (2009).
    OpenUrlCrossRefPubMed
  39. 39.↵
    Yushkevich, P. A. et al. Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI. NeuroImage 53, 1208–1224 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  40. 40.↵
    Pini, L. et al. Brain atrophy in Alzheimer’s Disease and aging. Ageing Research Reviews 30, 25–48 (2016).
    OpenUrl
  41. 41.↵
    Bigler, E. D. et al. FreeSurfer 5.3 versus 6.0: are volumes comparable? A Chronic Effects of Neurotrauma Consortium study. Brain Imaging and Behavior (2018) doi:10.1007/s11682-018-9994-x.
    OpenUrlCrossRef
  42. 42.↵
    Kook Lim, H. et al. Automated hippocampal subfields segmentation in late life depression. Journal of Affective Disorders 143, 253–256 (2012).
    OpenUrlPubMed
  43. 43.↵
    Hanseeuw, B. J. et al. Mild cognitive impairment: Differential atrophy in the hippocampal subfields. American Journal of Neuroradiology 32, 1658–1661 (2011).
    OpenUrlAbstract/FREE Full Text
  44. 44.↵
    Li, Y. di, Dong, H. B., Xie, G. M. & Zhang, L. J. Discriminative analysis of mild alzheimer’s disease and normal aging using volume of hippocampal subfields and hippocampal mean diffusivity: An in vivo magnetic resonance imaging study. American Journal of Alzheimer’s Disease and other Dementias 28, 627–633 (2013).
    OpenUrlCrossRefPubMed
  45. 45.↵
    Khan, U. A. et al. Molecular drivers and cortical spread of lateral entorhinal cortex dysfunction in preclinical Alzheimer’s disease. Nature Neuroscience 17, 304–311 (2014).
    OpenUrlCrossRefPubMed
  46. 46.↵
    Wisse, L. E. M., Biessels, G. J. & Geerlings, M. I. A Critical Appraisal of the Hippocampal Subfield Segmentation Package in FreeSurfer. Frontiers in Aging Neuroscience 6, 127–134 (2014).
    OpenUrl
  47. 47.
    Cover, K. S., van Schijndel, R. A., Bosco, P., Damangir, S. & Redolfi, A. Can measuring hippocampal atrophy with a fully automatic method be substantially less noisy than manual segmentation over both 1 and 3 years? Psychiatry Research - Neuroimaging 280, 39–47 (2018).
    OpenUrl
  48. 48.
    Marizzoni, M. et al. Longitudinal reproducibility of automatically segmented hippocampal subfields: A multisite European 3T study on healthy elderly. Human Brain Mapping 36, 3516–3527 (2015).
    OpenUrl
  49. 49.↵
    Bender, A. R. et al. Optimization and validation of automated hippocampal subfield segmentation across the lifespan. Human Brain Mapping 39, 916–931 (2018).
    OpenUrl
  50. 50.↵
    Pantazis, D. et al. Comparison of landmark-based and automatic methods for cortical surface registration. NeuroImage 49, 2479–2493 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  51. 51.↵
    Malone, I. B. et al. MIRIAD-Public release of a multiple time point Alzheimer’s MR imaging dataset. NeuroImage 70, 33–36 (2013).
    OpenUrl
  52. 52.↵
    Iglesias, J. E. et al. Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases. NeuroImage 141, 542–555 (2016).
    OpenUrl
  53. 53.
    Cash, D. M. et al. Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge. NeuroImage 123, 149–164 (2015).
    OpenUrlCrossRefPubMed
  54. 54.
    Platero, C., Lin, L. & Tobar, M. C. Longitudinal Neuroimaging Hippocampal Markers for Diagnosing Alzheimer’s Disease. Neuroinformatics 17, 43–61 (2019).
    OpenUrl
  55. 55.
    Giraldo, D. L., García-Arteaga, J. D., Cárdenas-Robledo, S. & Romero, E. Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer’s disease. Brain and Behavior 8, 1–11 (2018).
    OpenUrl
  56. 56.↵
    Ardekani, B. A., Convit, A. & Bachman, A. H. Analysis of the MIRIAD Data Shows Sex Differences in Hippocampal Atrophy Progression. Journal of Alzheimer’s Disease 50, 847–857 (2016).
    OpenUrl
  57. 57.↵
    Carrillo, M. C., Bain, L. J., Frisoni, G. B. & Weiner, M. W. Worldwide Alzheimer’s disease neuroimaging initiative. Alzheimer’s and Dementia 8, 337–342 (2012).
    OpenUrl
  58. 58.↵
    van Horn, J. D. & Toga, A. W. Multisite neuroimaging trials. Current Opinion in Neurology 22, 370–378 (2009).
    OpenUrlCrossRefPubMed
  59. 59.↵
    Schneider, L. S. & Goldberg, T. E. Composite cognitive and functional measures for early stage Alzheimer’s disease trials. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring 12, 1–9 (2020).
    OpenUrl
  60. 60.
    Dicks, E. et al. Modeling grey matter atrophy as a function of time, aging or cognitive decline show different anatomical patterns in Alzheimer’s disease. NeuroImage: Clinical 22, 101786 (2019).
    OpenUrl
  61. 61.↵
    Folstein, M. F., Folstein, S. E. & McHugh, P. R. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of psychiatric research 12, 189–198 (1975).
    OpenUrlCrossRefPubMedWeb of Science
  62. 62.↵
    Nelson, P. T. et al. Correlation of Alzheimer Disease Neuropathologic Changes With Cognitive Status: A Review of the Literature. Journal of Neuropathology & Experimental Neurology 71, 362–381 (2012).
    OpenUrl
  63. 63.↵
    Ferrarini, L. et al. MMSE scores correlate with local ventricular enlargement in the spectrum from cognitively normal to Alzheimer disease. NeuroImage 39, 1832–1838 (2008).
    OpenUrlPubMed
  64. 64.↵
    Risacher, S. L. et al. Alzheimer disease brain atrophy subtypes are associated with cognition and rate of decline. Neurology 89, 2176–2186 (2017).
    OpenUrl
  65. 65.↵
    Tustison, N. J. et al. N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging 29, 1310–1320 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  66. 66.↵
    Manjón, J. v., Coupé, P., Martí-Bonmatí, L., Collins, D. L. & Robles, M. Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging 31, 192–203 (2010).
    OpenUrlCrossRefPubMed
  67. 67.↵
    Coupé, P. et al. Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54, 940–954 (2011).
    OpenUrlCrossRefPubMedWeb of Science
  68. 68.↵
    Romero, J. E., Manjón, J. v., Tohka, J., Coupé, P. & Robles, M. NABS: Non-local automatic brain hemisphere segmentation. Magnetic Resonance Imaging 33, 474–484 (2015).
    OpenUrl
  69. 69.↵
    Ashburner, J. & Friston, K. J. Unified segmentation. NeuroImage 26, 839–851 (2005).
    OpenUrlCrossRefPubMedWeb of Science
  70. 70.↵
    Gaser, C., & Dahnke, R. CAT-A Computational Anatomy Toolbox for the Analysis of Structural MRI Data. Human Brain Mapping (2016).
  71. 71.↵
    Dahnke, R. & Gaser, C. Brain structural trajectories over the adult lifespan. Human Brain Mapping (2017).
  72. 72.↵
    Manjón, J. v., Coupé, P., Martí-Bonmatí, L., Collins, D. L. & Robles, M. Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging 31, 192–203 (2010).
    OpenUrlCrossRefPubMed
  73. 73.↵
    Brewer, J. B., Magda, S., Airriess, C. & Smith, M. E. Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease. American Journal of Neuroradiology 30, 578–580 (2009).
    OpenUrlAbstract/FREE Full Text
  74. 74.
    Hata, K. et al. Automated Volumetry of Medial Temporal Lobe Subregions in Mild Cognitive Impairment and Alzheimer Disease. Alzheimer Disease and Associated Disorders 33, 206–211 (2019).
    OpenUrl
  75. 75.↵
    Fischl, B. et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).
    OpenUrlCrossRefPubMedWeb of Science
  76. 76.↵
    Haeger, A. et al. Effect of a multicomponent exercise intervention on brain metabolism: A randomized controlled trial on Alzheimer’s pathology (DementiaMOVE). Alzheimer’s & Dementia: Translational Research & Clinical Interventions 6, 1–9 (2020).
    OpenUrl
  77. 77.
    Coupé, P., Manjón, J. V., Lanuza, E. & Catheline, G. Lifespan Changes of the Human Brain In Alzheimer’s Disease. Scientific Reports 9, 1–12 (2019).
    OpenUrl
  78. 78.
    Planche, V. et al. Evolution of brain atrophy subtypes during aging predicts long-term cognitive decline and future Alzheimer’s clinical syndrome. Neurobiology of Aging 79, 22–29 (2019).
    OpenUrl
  79. 79.↵
    Goubran, M. et al. Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks. Human Brain Mapping 41, 291–308 (2020).
    OpenUrl
  80. 80.↵
    Wang, Z. I. et al. Automated MRI volumetric analysis in patients with rasmussen syndrome. American Journal of Neuroradiology 37, 2348–2355 (2016).
    OpenUrlAbstract/FREE Full Text
  81. 81.↵
    Skjøth-Rasmussen, J., Jespersen, B. & Brennum, J. The use of Brainsuite iCT for frame-based stereotactic procedures. Acta Neurochirurgica 157, 1437–1440 (2015).
    OpenUrl
  82. 82.↵
    Ou, Y. et al. Field of View Normalization in Multi-Site Brain MRI. Neuroinformatics 16, 431–444 (2018).
    OpenUrl
  83. 83.↵
    D’Andrea, G. et al. Intraoperative DTI and brain mapping for surgery of neoplasm of the motor cortex and the corticospinal tract: Our protocol and series in BrainSUITE. Neurosurgical Review 35, 401–412 (2012).
    OpenUrlPubMed
  84. 84.↵
    Mikhael, S. S. & Pernet, C. A controlled comparison of thickness, volume and surface areas from multiple cortical parcellation packages. BMC Bioinformatics 20, 1–12 (2019).
    OpenUrlCrossRef
  85. 85.
    Mikhael, S., Hoogendoorn, C., Valdes-Hernandez, M. & Pernet, C. A critical analysis of neuroanatomical software protocols reveals clinically relevant differences in parcellation schemes. NeuroImage 170, 348–364 (2018).
    OpenUrl
  86. 86.
    Carass, A. et al. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. NeuroImage 183, 150–172 (2018).
    OpenUrl
  87. 87.↵
    Schwarz, C. G. et al. A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer’s disease severity. NeuroImage: Clinical 11, 802–812 (2016).
    OpenUrl
  88. 88.↵
    Jack, C. R. et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s and Dementia 7, 257–262 (2011).
    OpenUrl
  89. 89.↵
    Albert, M. S. et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s and Dementia 7, 270–279 (2011).
    OpenUrlCrossRefWeb of Science
  90. 90.↵
    Anderson, V. M. et al. Gray matter atrophy rate as a marker of disease progression in AD. Neurobiology of Aging 33, 1194–1202 (2012).
    OpenUrlCrossRefPubMed
  91. 91.↵
    Boutet, C. et al. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5, 341–348 (2014).
    OpenUrl
  92. 92.↵
    Adler, D. H. et al. Characterizing the human hippocampus in aging and Alzheimer’s disease using a computational atlas derived from ex vivo MRI and histology. Proceedings of the National Academy of Sciences of the United States of America 115, 4252–4257 (2018).
    OpenUrlAbstract/FREE Full Text
  93. 93.↵
    Kwak, K., Niethammer, M., Giovanello, K. S., Styner, M. & Dayan, E. The contribution of hippocampal subfields to the progression of neurodegeneration. bioRxiv (2020) doi:10.1101/2020.05.06.081034.
    OpenUrlAbstract/FREE Full Text
  94. 94.↵
    Lace, G. et al. Hippocampal tau pathology is related to neuroanatomical connections: An ageing population-based study. Brain 132, 1324–1334 (2009).
    OpenUrlCrossRefPubMedWeb of Science
  95. 95.
    Delgado-González, J. C. et al. Neuronal volume of the hippocampal regions in ageing. Journal of Anatomy 1–10 (2020) doi:10.1111/joa.13189.
    OpenUrlCrossRef
  96. 96.
    Parker, T. D. et al. Differences in hippocampal subfield volume are seen in phenotypic variants of early onset Alzheimer’s disease. NeuroImage: Clinical 21, 101632 (2019).
    OpenUrl
  97. 97.↵
    Carlesimo, G. A. et al. Atrophy of presubiculum and subiculum is the earliest hippocampal anatomical marker of Alzheimer’s disease. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring 1, 24–32 (2015).
    OpenUrl
  98. 98.↵
    Rössler, M., Zarski, R., Bohl, J. & Ohm, T. G. Stage-dependent and sector-specific neuronal loss in hippocampus during alzheimer’s disease. Acta Neuropathologica 103, 363–369 (2002).
    OpenUrlCrossRefPubMed
  99. 99.↵
    Braak, E. & Braak, H. Alzheimer’s disease: Transiently developing dendritic changes in pyramidal cells of sector CA1 of the Ammon’s horn. ActaNeuropathologica 93, 323–325 (1997).
    OpenUrl
  100. 100.↵
    Su, L. et al. Hippocampal Stratum Radiatum, Lacunosum, and Moleculare Sparing in Mild Cognitive Impairment. Journal of Alzheimer’s Disease 61, 415–424 (2018).
    OpenUrl
  101. 101.
    Dounavi, M. E. et al. Volumetric alterations in the hippocampal subfields of subjects at increased risk of dementia. Neurobiology of Aging 91, 36–44 (2020).
    OpenUrl
  102. 102.↵
    Zhao, W. et al. Trajectories of the hippocampal subfields atrophy in the alzheimer’s disease: A structural imaging study. Frontiers in Neuroinformatics 13, 1–9 (2019).
    OpenUrl
  103. 103.↵
    Khlif, M. S. et al. A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants. NeuroImage: Clinical 21, (2019).
  104. 104.↵
    Tang, X., Holland, D., Dale, A. M., Younes, L. & Miller, M. I. Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer’s disease: Detecting, quantifying, and predicting. Human Brain Mapping 35, 3701–3725 (2014).
    OpenUrlCrossRefPubMed
  105. 105.↵
    Yi, H. A. et al. Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to Alzheimer’s disease. Journal of Neurology, Neurosurgery and Psychiatry 87, 425–432 (2016).
    OpenUrlAbstract/FREE Full Text
  106. 106.↵
    Tullo, S. et al. MR-based age-related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan. Human Brain Mapping 40, 5269–5288 (2019).
    OpenUrl
  107. 107.↵
    Mufson, E. J. et al. Mild cognitive impairment: Pathology and mechanisms. Acta Neuropathologica 123, 13–30 (2012).
    OpenUrlCrossRefPubMedWeb of Science
  108. 108.↵
    Dicks, E., van der Flier, W. M., Scheltens, P., Barkhof, F. & Tijms, B. M. Single-subject grey matter networks predict future cortical atrophy in preclinical Alzheimer’s disease. Neurobiology of Aging (2020) doi:10.1016/j.neurobiolaging.2020.05.008.
    OpenUrlCrossRef
  109. 109.↵
    Grothe, M. et al. Reduction of basal forebrain cholinergic system parallels cognitive impairment in patients at high risk of developing alzheimer’s disease. Cerebral Cortex 20, 1685–1695 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  110. 110.↵
    Muth, K. et al. Mild Cognitive Impairment in the Elderly is Associated with Volume Loss of the Cholinergic Basal Forebrain Region. Biological Psychiatry 67, 588–591 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  111. 111.↵
    Grothe, M., Heinsen, H. & Teipel, S. Longitudinal measures of cholinergic forebrain atrophy in the transition from healthy aging to Alzheimer’s disease. Neurobiology of Aging 34, 1210–1220 (2013).
    OpenUrlCrossRefPubMedWeb of Science
  112. 112.↵
    Ossenkoppele, R. et al. Associations between tau, Aβ, and cortical thickness with cognition in Alzheimer disease. Neurology 92, e601–e612 (2019).
    OpenUrl
  113. 113.↵
    Barber, R., McKeith, I., Ballard, C. & O’Brien, J. Volumetric MRI study of the caudate nucleus in patients with dementia with Lewy bodies, Alzheimer’s disease, and vascular dementia. Journal of Neurology Neurosurgery and Psychiatry 72, 406–407 (2002).
    OpenUrlAbstract/FREE Full Text
  114. 114.↵
    Yan, T. et al. Early-Stage Identification and Pathological Development of Alzheimer’s Disease Using Multimodal MRI. Journal of Alzheimer’s Disease 68, 1013–1027 (2019).
    OpenUrl
  115. 115.↵
    Botzung, A., Philippi, N., Noblet, V., Loureiro De Sousa, P. & Blanc, F. Pay attention to the basal ganglia: A volumetric study in early dementia with Lewy bodies. Alzheimer’s Research and Therapy 11, 1–9 (2019).
    OpenUrl
  116. 116.↵
    Sun, N., Mormino, E. C., Chen, J., Sabuncu, M. R. & Yeo, B. T. T. Multi-modal latent factor exploration of atrophy, cognitive and tau heterogeneity in Alzheimer’s disease. NeuroImage 201, (2019).
  117. 117.↵
    Young, A. L. et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nature Communications 9, 1–16 (2018).
    OpenUrl
  118. 118.
    Noh, Y. et al. Anatomical heterogeneity of Alzheimer disease Based on cortical thickness on MRIs. Neurology 83, 1936–1944 (2014).
    OpenUrlCrossRefPubMed
  119. 119.↵
    Ferreira, D. et al. Distinct subtypes of Alzheimer’s disease based on patterns of brain atrophy: Longitudinal trajectories and clinical applications. Scientific Reports 7, 1–13 (2017).
    OpenUrl
  120. 120.↵
    Gorno-Tempini, M. L. et al. Classification of primary progressive aphasia and its variants. Neurology 76, 1006–1014 (2011).
    OpenUrlCrossRefPubMed
  121. 121.↵
    Phillips, J. S. et al. Tau PET imaging predicts cognition in atypical variants of Alzheimer’s disease. Human Brain Mapping 39, 691–708 (2018).
    OpenUrlCrossRef
  122. 122.↵
    Ossenkoppele, R. et al. The behavioural/dysexecutive variant of Alzheimer’s disease: Clinical, neuroimaging and pathological features. Brain 138, 2732–2749 (2015).
    OpenUrlCrossRefPubMed
  123. 123.↵
    Ossenkoppele, R. et al. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease. Brain 139, 1551–1567 (2016).
    OpenUrlCrossRefPubMed
  124. 124.↵
    Arbabshirani, M. R., Plis, S., Sui, J. & Calhoun, V. D. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. NeuroImage 145, 137–165 (2017).
    OpenUrlCrossRefPubMed
  125. 125.↵
    Spires-Jones, T. L. & Hyman, B. T. The Intersection of Amyloid Beta and Tau at Synapses in Alzheimer’s Disease. Neuron 82, 756–771 (2014).
    OpenUrlCrossRefPubMedWeb of Science
  126. 126.↵
    McKhann, G. M. et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s and Dementia 7, 263–269 (2011).
    OpenUrlCrossRefWeb of Science
  127. 127.↵
    Sørensen, L. et al. Early detection of Alzheimer’s disease using MRI hippocampal texture. Human Brain Mapping 37, 1148–1161 (2016).
    OpenUrlCrossRef
  128. 128.↵
    McEvoy, L. K. & Brewer, J. B. Quantitative structural MRI for early detection of Alzheimers disease. Expert Review of Neurotherapeutics 10, 1675–1688 (2010).
    OpenUrl
  129. 129.↵
    Fotuhi, M., Do, D. & Jack, C. Modifiable factors that alter the size of the hippocampus with ageing. Nature Reviews Neurology 8, 189–202 (2012).
    OpenUrl
  130. 130.
    Erickson, K. I. et al. Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences 108, 3017–3022 (2011).
    OpenUrlAbstract/FREE Full Text
  131. 131.↵
    Varma, V. R., Chuang, Y. F., Harris, G. C., Tan, E. J. & Carlson, M. C. Low-intensity daily walking activity is associated with hippocampal volume in older adults. Hippocampus 25, 605–615 (2015).
    OpenUrlCrossRefPubMed
  132. 132.↵
    Liang, Y., Ryan, N. S., Schott, J. M. & Fox, N. C. Imaging the onset and progression of Alzheimer’s disease: Implications for prevention trials. Journal of Alzheimer’s Disease 33, (2013).
  133. 133.↵
    Coupé, P., Eskildsen, S. F., Manjón, J. v., Fonov, V. S. & Collins, D. L. Simultaneous segmentation and grading of anatomical structures for patient’s classification: Application to Alzheimer’s disease. NeuroImage 59, 3736–3747 (2012).
    OpenUrl
  134. 134.↵
    Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s and Dementia 7, 280–292 (2011).
    OpenUrlCrossRefWeb of Science
  135. 135.
    Veitch, D. P. et al. Understanding disease progression and improving Alzheimer’s disease clinical trials: Recent highlights from the Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s and Dementia 15, 106–152 (2019).
    OpenUrl
  136. 136.↵
    Vanhoenacker, A. S., Sneyers, B., de Keyzer, F., Heye, S. & Demaerel, P. Evaluation and clinical correlation of practical cut-offs for visual rating scales of atrophy: normal aging versus mild cognitive impairment and Alzheimer’s disease. Acta Neurologica Belgica 117, 661–669 (2017).
    OpenUrl
  137. 137.↵
    Shaw, T., York, A., Ziaei, M., Barth, M. & Bollmann, S. Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using Multi-Contrast MRI. NeuroImage 116798 (2020) doi:10.1016/j.neuroimage.2020.116798.
    OpenUrlCrossRef
  138. 138.↵
    Scarpazza, C. et al. Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders. Translational Psychiatry 10, (2020).
  139. 139.↵
    Henneman, W. J. P. et al. Hippocampal atrophy rates in Alzheimer disease: Added value over whole brain volume measures. Neurology 72, 999–1007 (2009).
    OpenUrlCrossRefPubMed
  140. 140.
    Leung, K. K. et al. Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer’s disease. NeuroImage 51, 1345–1359 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  141. 141.
    Frankó, E. & Joly, O. Evaluating Alzheimer’s Disease Progression Using Rate of Regional Hippocampal Atrophy. PLoS ONE 8, (2013).
  142. 142.↵
    Mak, E. et al. Longitudinal assessment of global and regional atrophy rates in Alzheimer’s disease and dementia with Lewy bodies. NeuroImage: Clinical 7, 456–462 (2015).
    OpenUrl
  143. 143.↵
    Eustache, P., Nemmi, F., Saint-Aubert, L., Pariente, J. & Péran, P. Multimodal Magnetic Resonance Imaging in Alzheimer’s Disease Patients at Prodromal Stage. Journal of Alzheimer’s Disease 50, 1035–1050 (2016).
    OpenUrl
  144. 144.↵
    Pagani, M. et al. Predicting the transition from normal aging to Alzheimer’s disease: A statistical mechanistic evaluation of FDG-PET data. NeuroImage 141, 282–290 (2016).
    OpenUrl
  145. 145.↵
    Qiu, S. et al. Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain 1–14 (2020) doi:10.1093/brain/awaa137.
    OpenUrlCrossRef
  146. 146.
    Zhao, W. et al. Automated Brain MRI Volumetry Differentiates Early Stages of Alzheimer’s Disease From Normal Aging. Journal of Geriatric Psychiatry and Neurology 32, 354–364 (2019).
    OpenUrl
  147. 147.
    Li, H., Habes, M., Wolk, D. A. & Fan, Y. A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data. Alzheimer’s and Dementia 15, 1059–1070 (2019).
    OpenUrl
  148. 148.
    Bachli, M. B. et al. Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach. NeuroImage 208, (2020).
  149. 149.
    Bouts, M. J. R. J. et al. Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification. Human Brain Mapping 40, 2711–2722 (2019).
    OpenUrl
  150. 150.
    Forouzannezhad, P. et al. A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging. Journal of Neuroscience Methods 333, 108544 (2020).
    OpenUrl
  151. 151.
    Khan, R. U., Tanveer, M. & Pachori, R. B. A novel method for the classification of Alzheimer’s disease from normal controls using magnetic resonance imaging. Expert Systems 1–22 (2020) doi:10.1111/exsy.12566.
    OpenUrlCrossRef
  152. 152.
    Oh, K., Chung, Y. C., Kim, K. W., Kim, W. S. & Oh, I. S. Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning. Scientific Reports 9, 1–16 (2019).
    OpenUrl
  153. 153.
    Dukart, J. et al. Meta-analysis based SVM classification enables accurate detection of Alzheimer’s disease across different clinical centers using FDG-PET and MRI. Psychiatry Research - Neuroimaging 212, 230–236 (2013).
    OpenUrl
  154. 154.↵
    Lecun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
    OpenUrlCrossRefPubMed
Back to top
Posted August 19, 2020.
Download PDF

Supplementary Material

Data/Code
Email
A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach
Jafar Zamani, Ali Sadr, Amir-Homayoun Javadi
bioRxiv 2020.08.18.256321; doi: https://doi.org/10.1101/2020.08.18.256321
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area