Structural Analysis of Brain Hub Region Volume and Cortical Thickness in Patients with Mild Cognitive Impairment and Dementia

Nauris Zdanovskis; Ardis Platkājis; Andrejs Kostiks; Guntis Karelis

doi:10.3390/medicina56100497

,

and

¹

Department of Radiology, Riga East University Hospital, Hipokrāta iela 2, LV-1038 Riga, Latvia

²

Department of Radiology, Riga Stradins University, Dzirciema iela 16, LV-1007 Riga, Latvia

³

Department of Neurosurgery and Neurology, Riga East University Hospital, Hipokrāta iela 2, LV-1038 Riga, Latvia

^*

Author to whom correspondence should be addressed.

Medicina2020, 56(10), 497;https://doi.org/10.3390/medicina56100497

This article belongs to the Special Issue Advantages of Structural and Functional Imaging of the Human Brain

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Abstract

Background and Objectives: A complex network of axonal pathways interlinks the human brain cortex. Brain networks are not distributed evenly, and brain regions making more connections with other parts are defined as brain hubs. Our objective was to analyze brain hub region volume and cortical thickness and determine the association with cognitive assessment scores in patients with mild cognitive impairment (MCI) and dementia. Materials and Methods: In this cross-sectional study, we included 11 patients (5 mild cognitive impairment; 6 dementia). All patients underwent neurological examination, and Montreal Cognitive Assessment (MoCA) test scores were recorded. Scans with a 3T MRI scanner were done, and cortical thickness and volumetric data were acquired using Freesurfer 7.1.0 software. Results: By analyzing differences between the MCI and dementia groups, MCI patients had higher hippocampal volumes (p < 0.05) and left entorhinal cortex thickness (p < 0.05). There was a significant positive correlation between MoCA test scores and left hippocampus volume (r = 0.767, p < 0.01), right hippocampus volume (r = 0.785, p < 0.01), right precuneus cortical thickness (r = 0.648, p < 0.05), left entorhinal cortex thickness (r = 0.767, p < 0.01), and right entorhinal cortex thickness (r = 0.612, p < 0.05). Conclusions: In our study, hippocampal volume and entorhinal cortex showed significant differences in the MCI and dementia patient groups. Additionally, we found a statistically significant positive correlation between MoCA scores, hippocampal volume, entorhinal cortex thickness, and right precuneus. Although other brain hub regions did not show statistically significant differences, there should be additional research to evaluate the brain hub region association with MCI and dementia.

Keywords:

brain networks; brain hubs; mild cognitive impairment; dementia; neuroimaging; cortical thickness; white matter volume

1. Introduction

The human brain cortex is interlinked by a complex network of axonal pathways that range from smaller local circuits and broader long-range fiber pathways [,]. There are several structural features of cortical networks that can be utilized as a quantitative variable through graph theory, i.e., nodal degree, strength, eccentricity, path length, clustering coefficient, transitivity, centrality, etc. [,,] Brain networks are not distributed evenly. Brain hubs are the parts of the brain that are making many connections with other parts of the brain. [,] Brain network hub functionality is essential for neuronal communication and integration [,]. These hubs are located in the bilateral putamen, thalamus, superior parietal, superior frontal, precuneus, hippocampus, insula, right pallidum, and left lingual gyrus [].

Several studies found a brain network hub connectivity disruption association with mild cognitive impairment and Alzheimer’s disease [,,].

We analyzed brain network hub volume and cortical thickness and determined whether there are distinct differences in these regions for patients with mild cognitive impairment (MCI) and dementia. Additionally, we analyzed entorhinal cortex cortical thickness that is considered as a reliable Alzheimer’s disease brain biomarker [,].

2. Materials and Methods

Participants were admitted to the neurological outpatient clinic due to suspected cognitive impairment. Patients underwent neurological examination, and the Montreal Cognitive Assessment (MoCA) test was performed, and scores were recorded. Patients were divided into two groups—mild cognitive impairment and dementia. The average and median age and MoCA scores are shown in Table 1.

Table 1. Demographic data and Montreal Cognitive Assessment (MoCA) scores in study patients.

Exclusion criteria for patients were other clinically significant neurological diseases, and drug or alcohol abuse. Patients did not have any other significant MR abnormalities (e.g., tumors, malformations, large vessel stroke).

MRI was performed on a single-site scanner to avoid scanner differences. All patients were scanned on a 3T scanner. We used the GE MP-RAGE sequence protocol with 1 mm³ resolution and 1 mm slice thickness with the appropriate gray–white matter contrast that was evaluated for every patient. All scans were converted from DICOM format to NIFTI format to perform further analysis.

Cortical reconstruction and volumetric segmentation were performed by using Freesurfer 7.1.0 image analysis software. It is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/). The technical details of these procedures are described in prior publications [,,,,,,,,,,,,,,,].

We used the Desikan–Killiany–Tourville (DKT) labeling protocol to extract cortical thickness results [].

Data were analyzed with statistical analysis software JASP Version 0.13. Descriptive statistics for volumetric data and cortical thickness data were determined in the MCI and dementia groups. The Mann–Whitney U test was used to analyze brain hub volume and cortical thickness differences in the MCI and dementia groups. Spearman’s correlation was calculated, and statistical significance was determined in both groups by correlating MoCA scores and hub volume and cortical thickness data.

Ethics committee (3 October 2019) and institutional review board (21 October 2019) approvals were obtained (ethical approval number: AP-144/19). Written informed consent for participation in a study and use of anonymous data was obtained for every patient.

3. Results

3.1. Mean Values of Volumes and Cortical Thickness

Mean values of volumes and cortical thickness with standard deviation and standard error were calculated and are shown in Table 2.

Table 2. Mean values with standard deviation and standard error comparing patients with mild cognitive impairment (MCI) and dementia.

3.2. The Mann–Whitney U Test

By analyzing differences between MCI and dementia groups, statistically significant results were found in both hemisphere hippocampal volumes and left entorhinal cortex thickness (p < 0.05). Other regions did not show statistically significant differences between the MCI and dementia groups. Multiple comparison correction was not performed when reporting p values. Thus, results serve as exploratory data that must be validated with a larger cohort and further multiple comparison correction. The results are presented in Table 3.

Table 3. The Mann–Whitney U test by comparing the MCI and dementia patient groups.

The median value for left hippocampal volume (Table 4) in the MCI group was 3742.1 mm³, and in the dementia group 2938.0 mm³, and the distribution in these two groups differed significantly (Mann–Whitney U = 27, p = 0.03).

Table 4. Median values in the left hippocampus, right hippocampus, and left entorhinal cortex.

The median value for right hippocampal volume (Table 4) in the MCI group was 4004.0 mm³, and in the dementia group 2995.4 mm³, and the distribution in these two groups differed significantly (Mann–Whitney U = 28, p = 0.02).

The median value for left entorhinal cortex thickness (Table 4) in the MCI group was 2.896 mm, and in the dementia group 2.226 mm, and the distribution in these two groups differed significantly (Mann–Whitney U = 28, p = 0.02).

3.3. Spearman’s Correlations

The Spearman’s correlations (Table 5) were conducted to determine whether there are associations of the MoCA score and volume or cortical thickness in hub regions. The two-tailed test of significance indicated that there was a significant positive correlation between MoCA score and left hippocampus volume (r = 0.767, p < 0.01), right hippocampus volume (r = 0.785, p < 0.01), right precuneus cortical thickness (r = 0.648, p < 0.05), left entorhinal cortex thickness (r = 0.767, p < 0.01), and right entorhinal cortex thickness (r = 0.612, p < 0.05).

Table 5. Spearman’s correlation coefficients and p values by correlating MoCA score with volume and cortical thickness.

4. Discussion

In this study, we compared brain hub region structural data and entorhinal cortex thickness in patients with MCI and dementia. We found that out of all brain hub regions, only hippocampal volume had statistically significant differences in both groups. Additionally, we analyzed entorhinal cortex thickness in the MCI and dementia groups and found statistically significant differences in the left entorhinal cortex.

So, to analyze the brain hub region association with MCI and dementia, we performed MoCA score, volume, and cortical thickness associations analysis. We found a significant positive correlation in both hemispheres’ hippocampal volume, right precuneus cortical thickness, and both hemispheres’ entorhinal cortex thickness with MoCA scores.

Many studies have found smaller hippocampal volumes in patients with dementia than MCI or healthy controls [,].

Precuneus atrophy is associated with Alzheimer’s dementia [] and dementia in Parkinson’s disease [].

Further, the entorhinal cortex is proposed as a biomarker for early detection of Alzheimer’s disease. [,].

Regarding putamen structural changes, there are mixed results. One study found that putamen had strongly reduced volumes in Alzheimer’s disease [], but also in another study, putamen structural values did not diverge from normal cognition patients across the entire lifespan [].

Other brain hub regions have been analyzed separately and associated with cognitive decline, MCI, or dementia [,].

By analyzing our data, we did not find significant differences in the MCI and dementia groups apart from the structures mentioned above. This study was exploratory rather than confirmatory and performed on a small cohort. We did not perform multiple comparison correction when reporting p values. We are planning to validate our results and perform multiple comparison corrections with a larger cohort.

5. Conclusions

In our study, hippocampal volume and entorhinal cortex showed significant differences in the MCI and dementia patient groups. Additionally, we found a statistically significant positive correlation between MoCA scores, hippocampal volume, entorhinal cortex thickness, and right precuneus.

Although other brain hub regions did not show statistically significant differences, there should be additional research to evaluate the brain hub region association with MCI and dementia.

Author Contributions

Conceptualization, N.Z., A.P., A.K., and G.K.; methodology, N.Z., A.P., A.K., and G.K.; software, N.Z.; validation, N.Z., A.P., A.K., and G.K.; formal analysis, N.Z. and A.K.; investigation, N.Z. and A.K.; resources, N.Z., A.P., A.K., and G.K.; writing—original draft preparation, N.Z.; writing—review and editing, N.Z., A.P., A.K., and G.K.; visualization, N.Z.; supervision, A.P. and G.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

Sporns, C.; Honey, J.; Kötter, R. Identification and classification of hubs in brain networks. PLoS ONE 2007, 2, e1049. [Google Scholar] [CrossRef]
Hagmann, P.; Cammoun, L.; Gigandet, X.; Meuli, R.; Honey, C.J.; Van Wedeen, J.; Sporns, O. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008, 6, e159. [Google Scholar] [CrossRef]
Hilgetag, C.C.; Kötter, R.; Stephan, K.E.; Sporns, O. Computational Neuroanatomy; Humana Press: Totowa, NJ, USA, 2002. [Google Scholar]
Yu, Q.; Du, Y.; Chen, J.; Sui, J.; Adali, T.; Pearlson, G.D.; Calhoun, V.D. Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs; IEEE: Piscataway, NJ, USA, 2018; Volume 106, pp. 886–906. [Google Scholar]
Farahani, F.V.; Karwowski, W.; Lighthall, N.R. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A SYSTEMATIC Review; Frontiers Media SA: Lausanne, Switzerland, 2019; Volume 13, p. 585. [Google Scholar]
van den Heuvel, O.; Sporns, O. Network Hubs in the Human Brain; Elsevier Current Trends: Amsterdam, The Netherlands, 2013; Volume 17, pp. 683–696. [Google Scholar]
Oldham, S.; Fornito, A. The Development of Brain Network Hubs; Elsevier Ltd.: Amsterdam, The Netherlands, 2019; Volume 39, p. 100607. [Google Scholar]
van den Heuvel, M.P.; Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 2011, 31, 15775–15786. [Google Scholar] [CrossRef]
Dai, Z.; Yan, C.; Li, K.; Wang, Z.; Wang, J.; Cao, M.; Lin, Q.; Shu, N.; Xia, M.; Bi, Y.; et al. Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer’s Disease. Cereb. Cortex 2014, 25, 3723–3742. [Google Scholar] [CrossRef]
Navas, D.; Papo, S.; Boccaletti, F.; Del-Pozo, R.; Bajo, F.; Maestú, J.H.; Martínez, P.; Gil, I.; Sendiña-Nadal, J.; Buldú, M. Functional hubs in mild cognitive impairment. Int. J. Bifurc. Chaos 2015, 25. [Google Scholar] [CrossRef]
Guillon, J.; Attal, Y.; Colliot, O.; La Corte, V.B.; Dubois, D.; Schwartz, M.; Chavez, F.; De Vico Fallani, F. Loss of brain inter-frequency hubs in Alzheimer’s disease. Sci. Rep. 2017, 7, 1–13. [Google Scholar] [CrossRef]
Zhou, M.; Zhang, F.; Zhao, L.; Qian, J.; Dong, C. Entorhinal cortex: A good biomarker of mild cognitive impairment and mild Alzheimer’s disease. Rev. Neurosci. 2016, 27, 185–195. [Google Scholar] [CrossRef]
Holbrook, N.; Tustison, F.; Marquez, J.; Roberts, M.; Yassa, A.; Gillen, D. Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer’s disease. medRxiv 2019, 19011825. [Google Scholar] [CrossRef]
Dale, B.; Fischl, M.; Sereno, I. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage 1999, 9, 179–194. [Google Scholar] [CrossRef]
Fischl, M.I.; Sereno, R.B.H.; Tootell, A.; Dale, M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 1999, 8, 272–284. [Google Scholar] [CrossRef]
Reuter, M.; Rosas, H.D.; Fischl, B. Highly Accurate Inverse Consistent Registration: A Robust Approach. NeuroImage 2010, 53, 1181–1196. [Google Scholar] [CrossRef]
Segonne, F.; Dale, A.M.; Busa, E.; Glessner, M.; Salat, D.; Hahn, H.K.; Fischl, B. A hybrid approach to the skull stripping problem in MRI. NeuroImage 2004, 22, 1060–1075. [Google Scholar] [CrossRef]
Segonne, F.; Pacheco, J.; Fischl, B. Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans Med. Imaging 2007, 26, 518–529. [Google Scholar] [CrossRef]
Desikan, R.S.; Ségonne, F.; Fischl, B.; Quinn, B.T.; Dickerson, B.C.; Blacker, D.; Buckner, R.L.; Dale, A.M.; Maguire, R.P.; Hyman, B.T.; et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 2006, 31, 968–980. [Google Scholar] [CrossRef]
Fischl, A.; Liu, A.; Dale, A. Automated manifold surgery: Constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Med. Imaging 2001, 20, 70–80. [Google Scholar] [CrossRef]
Fischl, B.; Salat, D.; Busa, E.; Albert, M.; Dieterich, M.; Haselgrove, C.; van der Kouwe, A.; Killiany, R.; Kennedy, D.; Klaveness, S.; et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 2002, 33, 341–355. [Google Scholar] [CrossRef]
Fischl, B.; Dale, A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. USA 2000, 97, 11050–11055. [Google Scholar] [CrossRef]
Fischl, B.; Salat, D.H.; van der Kouwe, A.J.W.; Makris, N.; Ségonne, F.; Quinn, B.T.; Dale, A.M. Sequence-independent segmentation of magnetic resonance images. NeuroImage 2004, 23 (Suppl. 1), S69–S84. [Google Scholar] [CrossRef]
Fischl, B.; Sereno, M.I. DaleCortical Surface-Based Analysis: II: Inflation, Flattening, and a Surface-Based Coordinate System. NeuroImage 1999, 9, 195–207. [Google Scholar]
Fischl, B.; van der Kouwe, A.; Destrieux, C.; Halgren, E.; Ségonne, F.; Salat, D.H.; Busa, E.; Seidman, L.J.; Goldstein, J.; Kennedy, D.; et al. Automatically Parcellating the Human Cerebral Cortex. Cereb. Cortex 2004, 14, 11–22. [Google Scholar] [CrossRef]
Han, X.; Jovicich, J.; Salat, D.; van der Kouwe, A.; Quinn, B.; Czanner, S.; Busa, E.; Pacheco, J.; Albert, M.; Killiany, P.R.; et al. Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. NeuroImag 2006, 32, 180–194. [Google Scholar]
Jovicich, J.; Czanner, S.; Greve, E.; Haley, A.; van der Kouwe, R.; Gollub, D.; Kennedy, F.; Schmitt, G.; Brown, J.; MacFall, B.; et al. Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data. NeuroImage 2006, 30, 436–443. [Google Scholar] [PubMed]
Reuter, M.; Fischl, B. Avoiding Asymmetry-Induced Bias in Longitudinal Image Processing. NeuroImage 2011, 57, 1921. [Google Scholar]
Sled, J.G.; Zijdenbos, A.P.; Evans, A.C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 1998, 17, 87–97. [Google Scholar]
Den Heijer, T.; Van Der Lijn, F.; Koudstaal, P.J.; Hofman, A.; Van Der Lugt, A.; Krestin, G.P.; Niessen, W.J.; Breteler, M.M. A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline. Brain 2010, 133, 1163–1172. [Google Scholar]
Csernansky, J.G.; Wang, L.; Swank, J.; Miller, J.P.; Gado, M.; McKeel, D.; Miller, M.I.; Morris, J.C. Preclinical detection of Alzheimer’s disease: Hippocampal shape and volume predict dementia onset in the elderly. NeuroImage 2005, 25, 783–792. [Google Scholar]
Karas, P.; Scheltens, S.; Rombouts, R.; Van Schijndel, M.; Klein, B.; Jones, W.; Van Der Flier, H.; Vrenken, F.; Barkhof, F. Precuneus atrophy in early-onset Alzheimer’s disease: A morphometric structural MRI study. Neuroradiology 2007, 49, 967–976. [Google Scholar]
Zarei, M.; Ibarretxe-Bilbao, N.; Compta, Y.; Hough, C.; Junque, N.; Bargallo, E.; Tolosa, M.; Martí, J. Cortical thinning is associated with disease stages and dementia in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 2013, 84, 875–881. [Google Scholar]
De Jong, L.W.; Van Der Hiele, K.; Veer, I.M.; Houwing, J.J.; Westendorp, R.G.; Bollen, E.L.; De Bruin, P.W.; Middelkoop, H.A.; Van Buchem, M.A.; Van Der Grond, J. Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: An MRI study. Brain 2008, 131, 3277–3285. [Google Scholar]
Coupé, P.; Manjón, J.V.; Lanuza, E.; Catheline, C. Lifespan Changes of the Human Brain in Alzheimer’s Disease. Sci. Rep. 2019, 9, 1–12. [Google Scholar]
Blanc, S.J.; Colloby, N.; Philippi, X.; De Pétigny, B.; Jung, C.; Demuynck, C.; Phillipps, P.; Anthony, A.; Thomas, F.; Bing, J.; et al. Cortical thickness in dementia with lewy bodies and alzheimer’s disease: A comparison of prodromal and dementia stages. PLoS ONE 2015, 10, e0127396. [Google Scholar] [CrossRef] [PubMed]