Next Article in Journal
Clinical Peri-Implant Parameters and Marginal Bone Loss for Early Mandibular Implant Overdentures: A Follow-Up of 60 Months
Previous Article in Journal
Testicular Cancer Treatments and Sexuality: A Narrative Review
Previous Article in Special Issue
Qualitative Verbal Fluency Components as Prognostic Factors for Developing Alzheimer’s Dementia and Mild Cognitive Impairment: Results from the Population-Based HELIAD Cohort
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Medicina
Volume 60
Issue 4
10.3390/medicina60040587
medicina-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Brodmann Areas, V1 Atlas and Cognitive Impairment: Assessing Cortical Thickness for Cognitive Impairment Diagnostics

by
Maksims Trišins
1,
Nauris Zdanovskis
1,2,3,*,
Ardis Platkājis
1,2,
Kristīne Šneidere
3,4,
Andrejs Kostiks
5,
Guntis Karelis
5,6 and
Ainārs Stepens
3
1
Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia
2
Department of Radiology, Riga East University Hospital, LV-1038 Riga, Latvia
3
Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
4
Department of Health Psychology and Pedagogy, Riga Stradins University, LV-1007 Riga, Latvia
5
Department of Neurology and Neurosurgery, Riga East University Hospital, LV-1038 Riga, Latvia
6
Department of Infectology, Riga Stradins University, LV-1007 Riga, Latvia
*
Author to whom correspondence should be addressed.
Medicina 2024, 60(4), 587; https://doi.org/10.3390/medicina60040587
Submission received: 29 February 2024 / Revised: 26 March 2024 / Accepted: 29 March 2024 / Published: 31 March 2024
(This article belongs to the Special Issue Preclinical Markers Preluding the Onset of Cognitive Impairment and Dementia)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background and Objectives: Magnetic resonance imaging is vital for diagnosing cognitive decline. Brodmann areas (BA), distinct regions of the cerebral cortex categorized by cytoarchitectural variances, provide insights into cognitive function. This study aims to compare cortical thickness measurements across brain areas identified by BA mapping. We assessed these measurements among patients with and without cognitive impairment, and across groups categorized by cognitive performance levels using the Montreal Cognitive Assessment (MoCA) test. Materials and Methods: In this cross-sectional study, we included 64 patients who were divided in two ways: in two groups with (CI) or without (NCI) impaired cognitive function and in three groups with normal (NC), moderate (MPG) and low (LPG) cognitive performance according to MoCA scores. Scans with a 3T MRI scanner were carried out, and cortical thickness data was acquired using Freesurfer 7.2.0 software. Results: By analyzing differences between the NCI and CI groups cortical thickness of BA3a in left hemisphere (U = 241.000, p = 0.016), BA4a in right hemisphere (U = 269.000, p = 0.048) and BA28 in left hemisphere (U = 584.000, p = 0.005) showed significant differences. In the LPG, MPG and NC cortical thickness in BA3a in left hemisphere (H (2) = 6.268, p = 0.044), in V2 in right hemisphere (H (2) = 6.339, p = 0.042), in BA28 in left hemisphere (H (2) = 23.195, p < 0.001) and in BA28 in right hemisphere (H (2) = 10.015, p = 0.007) showed significant differences. Conclusions: Our study found that cortical thickness in specific Brodmann Areas—BA3a and BA28 in the left hemisphere, and BA4a in the right—differ significantly between NCI and CI groups. Significant differences were also observed in BA3a (left), V2 (right), and BA28 (both hemispheres) across LPG, MPG, NC groups. Despite a small sample size, these findings suggest cortical thickness measurements can serve as effective biomarkers for cognitive impairment diagnosis, warranting further validation with a larger cohort.

Graphical Abstract

1. Introduction

Cognitive impairment refers to a decline in cognitive function that affects various cognitive domains and can impact daily functioning. Severe cognitive impairment is often considered equivalent to dementia, which is characterized by chronic cognitive decline in multiple domains leading to a significant decline in daily activities [1]. Dementia is not solely a normal part of aging but rather a condition that significantly affects individuals’ quality of life and independence. It is essential to distinguish between mild cognitive impairment, where cognitive function is impaired to a greater extent than is expected to be due to age, but the patient does not meet the criteria for dementia [2].
To diagnose cognitive impairment, especially in its early stages, a combination of clinical assessments and biomarkers plays a crucial role. Clinical examinations, including cognitive tests such as Montreal Cognitive Assessment (MoCA) and assessments of daily functioning, are typically used as the primary method for diagnosing cognitive impairment [3]. However, biomarkers can significantly aid in the early diagnosis of cognitive impairment, particularly in individuals without evident clinical signs of neurocognitive decline [4].
In the study by Battaglia et al. (2024), the focus was on the neural correlates and molecular mechanisms underlying memory and learning. The research emphasizes the critical role of memory and learning in acquiring, storing, and retrieving information essential for cognitive processes. This understanding is particularly relevant in the context of cognitive decline, where disruptions in memory and learning processes are often early indicators. By exploring the neural underpinnings and molecular pathways involved in memory and learning, this study contributes to a deeper understanding of how these cognitive functions are orchestrated at the neural level and how their impairment can lead to cognitive decline and neurodegenerative diseases such as Alzheimer’s [5].
MRI is a valuable tool in diagnosing cognitive impairment, particularly in conditions such as mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Brodmann areas are distinct regions of the cerebral cortex identified based on cytoarchitectural variance. Mapping these areas using MRI can offer crucial insights into cognitive function and dysfunction [6].
The study by Battaglia et al. (2024) focuses on utilizing a multiscale integrated approach to investigate functional connectivity in fear learning. The research emphasizes the significance of functional brain connectivity assessments, particularly through techniques such as functional magnetic resonance imaging (fMRI). By employing fMRI, the study aims to gain valuable insights into how different brain regions interact and communicate during fear learning processes. Functional magnetic resonance imaging plays a crucial role in elucidating the neural mechanisms involved in fear learning, providing a deeper understanding of the functional connectivity patterns within the brain during these cognitive processes and could possibly aid in early diagnosis of cognitive decline [7].
Research has shown that MRI activation maps can be overlaid on automated anatomical labeling templates to estimate activation in Brodmann areas, aiding in visualizing cognitive processes and dysfunction [8]. Specifically, studies have indicated selective atrophy in Brodmann area 35 (BA35), approximating the trans-entorhinal region, in individuals with preclinical AD compared to controls, highlighting the utility of MRI in detecting early structural changes linked to cognitive decline [9]. Furthermore, alterations in cortical thickness can be indicative of cognitive decline, with specific regions such as the entorhinal cortex and precuneus playing crucial roles in cognitive impairment [10,11].
Alterations in cortical thickness can serve as indicators of cognitive decline due to the close relationship between brain structure and cognitive function. Changes in cortical thickness have been associated with various cognitive processes and disorders. For instance, studies have shown that alterations in cortical thickness in regions such as Brodmann Area 44, involved in language processing, and the primary motor cortex (BA4Ap) have shown changes in cortical thickness that may affect cognitive abilities [12,13]. Moreover, cortical thickness alterations in areas such as the entorhinal cortex (BA28) have been linked to memory deficits and cognitive decline, particularly in conditions such as Alzheimer’s disease [14]. Changes in cortical thickness in language-related regions, including Broca’s area (BA44) and peri-sylvian language networks, have been associated with language impairment and cognitive dysfunction [15,16]. Furthermore, structural connectivity deficits in regions such as the inferior frontal gyrus (BA44) have been observed in individuals with speech disorders, highlighting the role of cortical thickness in language function and cognitive processes [17].
This work aims to compare cortical thickness measurements in different areas of the brain divided using Brodmann area map parcellation in two ways: between the two groups—patients without cognitive impairment (NCI) and patients with impaired cognitive function (CI) according to MoCA, and also between the three groups (normal cognition (NC), moderate cognitive performance group (MPG) and low cognitive performance group (LPG)), which were divided based on the results of the MoCA test.

2. Materials and Methods

A total of 64 participants were enrolled in the cross-sectional study and then were divided into groups in two ways according to the Montreal Cognitive Assessment (MoCA) score:
  • In two groups (with a score ≥ 26 and with a score <25);
  • In three groups (with a score ≥ 26, with a score ≥ 15 and ≤25 and with a score ≤ 14).
Division in two groups. Patients without cognitive impairment (NCI) according to MoCA (with a score ≥ 26) and patients with impaired cognitive function (CI) according to MoCA (with a score < 25) [18].
Research participant demographic data, gender, and MoCA scores between the two groups can be seen in Table 1.
A Chi-square test on gender was performed, determining that there was a statistically significant difference between the groups (X2 = 4.512, p = 0.034). A Mann–Whitney U test did not find age differences in groups (U = 363.000, p = 0.584). MoCA scores were significantly different between the two groups (U = 799.000, p < 0.001).
Division in three groups. To better assess differences between severe cognitive impairment and mild/moderate cognitive impairment patients were divided as follows:
  • Normal cognition group (NC)—participants with MoCA scores ≥ 26;
  • Moderate cognitive performance group (MPG)—participants with MoCA ≥ 15 and ≤25;
  • Low cognitive performance group (LPG)—participants with MoCA ≤ 14.
There were 17 participants in the NC group (mean age 70.941, SD 7.554, youngest 51 year, oldest 83 years. Mean MoCA score 27.529 (SD 1.231, lowest score 26, highest score 30).
MPG group contained 33 patients (mean age 72.758, SD 7.293, youngest 57 years, oldest 85 years). Mean MoCA score 22.121 (SD 2.792, lowest score 15, highest score 25).
LPG group contained 14 patients (mean age 74.286, SD 10.373, youngest 62, oldest 96). Mean MoCA score 8.786 (SD 3.786, lowest score 4, highest score 14). Research participant demographic data, gender, and MoCA scores between the three groups can be seen in Table 2.
A Chi-square test on gender was performed, determining that there were no statistically significant differences between the groups (X2 = 4.631, p = 0.099). Similarly, Kruskall–Wallis tests did not find age differences in groups (H (2) = 0.340, p = 0.844). MoCA scores were significantly different between the three groups (H (2) = 52.795, p < 0.001).

2.1. Selection of Participants

In our study, individuals were referred to a board-certified neurologist specializing in cognitive impairment for evaluation due to either subjective cognitive complaints or suspected cognitive decline. All of the selected patients had right hand dominance. Exclusion criteria encompassed clinically significant neurological conditions such as tumors, major strokes, intracerebral lobar hemorrhages, malformations, Parkinson’s disease, and multiple sclerosis, as well as substance, alcohol abuse, major depression, schizophrenia, and other psychiatric conditions. Additionally, patients with documented significant vascular diseases were not considered for inclusion. Magnetic resonance imaging (MRI) did not reveal any other noteworthy pathological findings in the study participants.

2.2. MRI Acquisition Protocol

MRI imaging was conducted using a 3.0 tesla MRI scanner within the setting of a university hospital. 3D T1 Ax (flip angle 11, TE min full, TI 400, FOV 25.6, layer thickness 1 mm) images were used for post-processing. Additional sequences were used to rule out other clinically significant pathologies, including, T2, 3D FLAIR, DWI, ADC, SWI sequences.

2.3. Cortical Parcellation

Cortical reconstruction was performed by using Freesurfer 7.2.0 image analysis software. It is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/ accessed on 16 February 2024). The technical details of these procedures are described in prior publications [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34].
We used the Brodmann Area Maps (BA Maps) and Hinds V1 Atlas labelling protocol to extract cortical thickness results [35,36,37,38,39,40].

2.4. Statistical Analysis

JASP 0.18.3 was used for the statistical analysis (Eric-Jan Wagenmakers, Amsterdam, The Netherlands) [41]. Statistical analysis included descriptive statistics, a Chi-square test, a Mann–Whitney U test, a Kruskal–Wallis test, and Dunn’s post hoc analysis of study results.
Descriptive statistics were used to estimate general variables and differences between groups. A Chi-square test was used to determine the association between categorical variables. A Mann–Whitney U test was used to evaluate statistically significant differences between the patients without cognitive impairment and patients with impaired cognitive function according to MoCA. A Kruskall–Wallis test was used to evaluate statistically significant differences between the NC, MPG and LPG groups, and if there were statistically significant differences, Dunn’s post hoc test was utilized with additional Bonferroni and Holm corrections.

3. Results

3.1. Statistical Differences between Two Groups (NCI and CI)

By analyzing differences between NCI and CI groups using Mann-Whitney U test, statistically significant results were found in cortex thickness of primary somatosensory area (BA3a) in left hemisphere, primary anterior motor area (BA4a) in the right hemisphere and entorhinal cortex (BA28) in the left hemisphere (p < 0.05). Other regions did not show statistically significant differences between the NCI and CI groups, although some regions had a p-value close to statistically significant: primary somatosensory cortex (BA1) in the right hemisphere (p = 0.057), secondary visual cortex (V2) in the right hemisphere (p = 0.060) and entorhinal cortex (BA28) in the right hemisphere (p = 0.062). When presenting the p value, multiple comparison corrections were not made. Therefore, the results serve as exploratory data to be validated by a larger cohort and further multiple comparison corrections. The results are presented in Table 3.

3.2. Statistical Differences between Three Groups (LPG, MPG and NC)

We performed Kruskal-Wallis test to determine whether there are statistically significant differences between LPG, MPG and NC.
We did not find statistically significant differences in:
  • Somatosensory area (BA1)—left hemisphere (H (2) = 1.762, p = 0.414), right hemisphere (H (2) = 4.718, p = 0.095);
  • Somatosensory area (BA2)—left hemisphere (H (2) = 2.594, p = 0.273), right hemisphere (H (2) = 1.662, p = 0.436);
  • Somatosensory area (BA3b)—left hemisphere (H (2) = 1.342, p = 0.511), right hemisphere (H (2) = 0.071, p = 0.965);
  • Primary anterior motor area (B4a)—left hemisphere (H (2) = 1.721, p = 0.423), right hemisphere (H (2) = 4.075, p = 0.130);
  • Primary posterior motor area (B4p)—left hemisphere (H (2) = 1.544, p = 0.462), right hemisphere (H (2) = 4.207, p = 0.122);
  • Pre-motor area (B6)—left hemisphere (H (2) = 2.626, p = 0.269), right hemisphere (H (2) = 2.665, p = 0.264);
  • Broca’s area pars opercularis (BA44)—left hemisphere (H (2) = 0.644, p = 0.725), right hemisphere (H (2) = 3.412, p = 0.182);
  • Broca’s area pars triangularis (BA45)—left hemisphere (H (2) = 0.359, p = 0.836), right hemisphere (H (2) = 3.505, p = 0.173);
  • Primary visual area (V1)—left hemisphere (H (2) = 3.438, p = 0.179), right hemisphere (H (2) = 4.164, p = 0.125);
  • Middle temporal area (MT)—left hemisphere (H (2) = 0.152, p = 0.927), right hemisphere (H (2) = 0.818, p = 0.664);
  • Perirhinal cortex (BA35)—left hemisphere (H (2) = 4.850, p = 0.088), right hemisphere (H (2) = 3.995, p = 0.136);
  • Secondary visual area (V2)—left hemisphere (H (2) = 4.261, p = 0.119);
  • Primary somatosensory area (B3a)—right hemisphere (H (2) = 0.404, p = 0.817).

3.2.1. Primary Somatosensory Area Left Hemisphere—BA3a

The Kruskal–Wallis tests revealed a significant difference between groups evaluating cortical thickness in primary somatosensory area (BA3a) in the left hemisphere (H (2) = 6.268, p = 0.044) (see Figure 1).
By performing Dunn’s post hoc test, statistically significant differences were found between the MPG—NC group (p = 0.012, after Bonferroni and Holm correction statistical significance was maintained) (see Table 4).

3.2.2. Secondary Visual Area Right Hemisphere—V2

The Kruskal–Wallis tests revealed a significant difference between groups evaluating cortical thickness in secondary visual area (V2) in the right hemisphere (H (2) = 6.339, p = 0.042) (see Figure 2).
By performing Dunn’s post hoc test, statistically significant differences were found between the MPG—NC group (p = 0.020, but after Bonferroni and Holm correction there were no statistically significant difference) (see Table 5).

3.2.3. Entorhinal Cortex Left Hemisphere—BA28

The Kruskal–Wallis tests revealed a significant difference between groups evaluating cortical thickness in entorhinal cortex (BA28) in the left hemisphere (H (2) = 23.195, p < 0.001) (see Figure 3).
By performing Dunn’s post hoc test, statistically significant differences were found between the LPG—MPG group (p < 0.001, after Bonferroni and Holm correction statistical significance was maintained) and the LPG—NC group (p < 0.001, after Bonferroni and Holm correction statistical significance was maintained) (see Table 6).

3.2.4. Entorhinal Cortex Right Hemisphere—BA28

The Kruskal–Wallis tests revealed a significant difference between groups evaluating cortical thickness in entorhinal cortex (BA28) in the right hemisphere (H (2) = 10.015, p = 0.007) (see Figure 4).
By performing Dunn’s post hoc test, statistically significant differences were found between the LPG—MPG group (p = 0.011, after Bonferroni and Holm correction statistical significance was maintained) and the LPG—NC group (p = 0.002, after Bonferroni and Holm correction statistical significance was maintained) (see Table 7).

4. Discussion

Till today scientists all around the world are trying to find early markers of dementia and cognitive impairment. Cortical thickness measurements have emerged as a valuable biomarker for cognitive impairment in various neurological conditions. In our study we tried to find connections between cortical thickness of different brain regions according to Brodmann area map and cognitive impairment. In discussion we compare our results with other research papers and provide possible explanations for our findings, as well as future directions.
BA4a is a primary anterior motor area responsible for motor planning and execution. Changes in cortical thickness in BA4a may influence motor control and coordination, which can impact cognitive functions related to motor skills and coordination [42,43]. Our study showed statistically significant results in NCI and CI groups in BA4a in the right hemisphere. One study suggests that cortical thickness alterations in BA4a and other motor areas may be associated with motor deficits and cognitive impairment in individuals with Parkinson’s disease [44].
BA3a is a primary somatosensory area involved in processing sensory information. Alterations in cortical thickness in BA3a may affect sensory perception, potentially impacting cognitive functions related to sensory processing and discrimination [45,46]. Our study showed statistically significant results between LPG, MPG, and NC groups and in the NCI and CI groups in BA3a in the left hemisphere. The results of one study suggest that alterations in cortical thickness in BA3b, a primary somatosensory area, may impact texture discrimination and perception, potentially contributing to cognitive impairment in individuals with these deficits [47].
V2 is the secondary visual cortex essential for visual processing and perception. It is involved in higher-order visual processing tasks such as color perception, motion detection, and object recognition. The connection between V2 and cognitive functioning is significant as V2 contributes to the integration and interpretation of visual stimuli, which are essential for various cognitive processes. It receives the information from V1 and process the information to other parts of the cortex [48]. Alterations in cortical thickness in V2 may affect visual perception and recognition, potentially impacting cognitive functions related to visual information processing and object recognition [49]. Our study showed statistically significant results between LPG, MPG, and NC groups in V2 in the right hemisphere. Additionally, it showed nearly significant results in the NCI and CI groups. Previously association between cortical thickness alterations in V2 region and cognitive impairment was observed individuals with Huntington’s disease [50].
The most promising region in a role of a biomarker proved to be entorhinal cortex (BA28) in both hemispheres, since it showed statistically significant results between LPG, MPG, and NC groups on both sides. Additionally, entorhinal cortex in the left hemisphere showed statistically significant results in the NCI and CI groups and in the right hemisphere showed nearly significant result between two groups. The entorhinal cortex (BA28) in both the left and right hemispheres plays a crucial role in spatial memory and episodic memory functions [51]. It is involved in navigation, formation, and consolidation of spatial and declarative memory [52]. The left entorhinal cortex is associated with object recognition and familiarity-based judgments [53]. Changes in the right entorhinal cortex have been associated with memory deficits and cognitive decline, highlighting its importance in cognitive function [54]. Additionally, the entorhinal cortex is correlated with episodic memory and is impacted by Alzheimer’s disease-related pathology [55]. The results reinforce studies that linked entorhinal cortex thickness to cognitive impairment [56,57,58].
Similar studies collectively emphasize the importance of investigating cognitive functions, brain volumes, metacognitive abilities, and white matter integrity in individuals with cognitive impairment, particularly in the context of mathematical knowledge, financial capacity, and related cognitive functions in conditions such as amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease.
The study by Giannouli and Tsolaki (2023) focused on investigating brain volumes and metacognitive deficits in individuals with amnestic Mild Cognitive Impairment (aMCI) compared to healthy controls. They found positive correlations between knowledge of strategies (avoidance strategies) and amygdala on both sides and white matter volume [59]. In a study by Stoeckel et al. (2013), the researchers explored the relationship between the MRI volume of the medial frontal cortex and financial capacity in individuals with mild Alzheimer’s disease. The study indicated that the volume of the medial frontal cortex could predict financial capacity in patients with mild Alzheimer’s disease [60]. Giannouli and Tsolaki (2019) found a strong correlation between financial capacity in aMCI patients and the volumes of the right amygdala and left angular gyrus [61]. Gerstenecker et al. (2017) conducted a study focusing on the association between white matter degradation and reduced financial capacity in individuals with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The research highlighted that in AD, increased mean, and axial diffusivities in specific brain regions such as the anterior cingulate, callosum, and frontal areas were linked to poorer financial capacity [62].
By analyzing our data, we did not find significant differences in the NCI and CI groups, and similarly in the LPG, MPG, and NC groups apart from the structures mentioned above. This study was exploratory rather than confirmatory and performed on a small cohort. When presenting the p value, multiple comparison corrections were not made. Therefore, the results serve as exploratory data to be validated by a larger cohort and further multiple comparison corrections.
One of the primary limitations of this study is the relatively small sample size of 64 patients. A larger sample would provide more robust and generalizable findings. Another possible limitation is the cross-sectional design of the study. Longitudinal studies would offer more insights into the progression and predictive value of cortical thickness changes over time. Also, depression is known to impact cognitive functions and could potentially confound the relationship between cortical thickness measurements and cognitive impairment. The study does not account for the potential effects of depression cognitive-neuropsychological, and emotional characteristics of the participants on cognitive performance and cortical thickness. In the end, while some differences in cortical thickness reached statistical significance, the clinical significance of these findings in terms of actual cognitive impairment and functional outcomes remains to be determined.
Future studies should integrate multimodal imaging techniques such as functional MRI (fMRI) and diffusion tensor imaging (DTI) to examine the functional and structural connectivity changes related to cortical thickness alterations. Additionally, it would be beneficial to compare cortical thickness measurements from BA mapping with other cognitive assessment tools to assess the reliability and consistency of the results. Furthermore, research should focus on investigating the specific roles of the identified Brodmann areas (BA3a, BA4a, BA28, V2) in cognitive processes and include comprehensive cognitive-neuropsychological and emotional assessments to understand their influence on cortical thickness measurements and cognitive performance.
Utilizing cortical thickness measurements as biomarkers can aid in early diagnosis and intervention for cognitive impairment, improving the accuracy of diagnosis and allowing for personalized treatment plans. Additionally, identifying specific Brodmann areas linked to cognitive impairment can guide the development of educational and counseling programs to enhance cognitive function and quality of life for those experiencing cognitive decline.

5. Conclusions

In our study, cortical thickness of primary somatosensory area (BA3a) in left hemisphere, primary anterior motor area (BA4a) in the right hemisphere and entorhinal cortex (BA28) in the left hemisphere showed significant differences in the NCI and CI groups. Additionally, primary somatosensory area (BA3a) in the left hemisphere, secondary visual area (V2) in the right hemisphere and entorhinal cortex (BA28) in both hemispheres showed significant differences in the LPG, MPG, and NC groups. Post-hoc tests showed significant differences in MPG—NC groups in primary somatosensory area (BA3a) in the left hemisphere and in LPG—MPG together with LPG—NC groups in entorhinal cortex (BA28) of both hemispheres.
Despite the limitations we believe our study provides the foundation for a new way of using cortical thickness measurements as quantitative biomarkers in diagnosis of cognitive impairment and dementia.
Further analysis with a larger patient cohort is needed to evaluate and validate the diagnostic certainty and prognostic value of cortical thickness measurement in different areas of the brain using Brodmann area map parcellation.

Author Contributions

Conceptualization, M.T., N.Z., K.Š., A.S. and A.K.; methodology, N.Z., M.T. and A.S.; software, M.T. and N.Z.; validation, N.Z. and A.K.; formal analysis, N.Z., K.Š. and A.S.; investigation, N.Z., K.Š. and A.S.; resources, N.Z., A.S., A.P. and G.K.; data curation, M.T., N.Z., A.K., G.K. and A.P.; writing—original draft preparation, M.T. and N.Z.; writing—review and editing, M.T., N.Z., A.S. and K.Š.; visualization, M.T. and N.Z.; supervision, N.Z., A.S., K.Š., A.P. and G.K.; project administration, N.Z. and A.S.; funding acquisition, M.T., N.Z. and A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Riga East University Hospital (No. AP-144/10, 3 October 2019) and the Ethics Committee at Riga East University Hospital Ethics Board (No. 08-A/19, 3 October 2019). The patients/participants provided their written informed consent to participate in this study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

The datasets presented in this article are not readily available since they could have potentially identifiable data. Requests to access the datasets should be directed to nauris.zdanovskis@rsu.lv.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Murray, A.M. Cognitive Impairment in the Aging Dialysis and Chronic Kidney Disease Populations: An Occult Burden. Adv. Chronic Kidney Dis. 2008, 15, 123–132. [Google Scholar] [CrossRef]
  2. Petersen, R.C.; Smith, G.E.; Waring, S.C.; Ivnik, R.J.; Kokmen, E.; Tangelos, E.G. Aging, Memory, and Mild Cognitive Impairment. Int. Psychogeriatr. 1997, 9, 65–69. [Google Scholar] [CrossRef]
  3. Petersen, R.C. Mild Cognitive Impairment as a Diagnostic Entity. J. Intern. Med. 2004, 256, 183–194. [Google Scholar] [CrossRef]
  4. Forlenza, O.V.; Diniz, B.S.; Gattaz, W.F. Diagnosis and Biomarkers of Predementia in Alzheimer’s Disease. BMC Med. 2010, 8, 89. [Google Scholar] [CrossRef] [PubMed]
  5. Battaglia, S.; Avenanti, A.; Vécsei, L.; Tanaka, M. Neural Correlates and Molecular Mechanisms of Memory and Learning. Int. J. Mol. Sci. 2024, 25, 2724. [Google Scholar] [CrossRef] [PubMed]
  6. Yushkevich, P.A.; Pluta, J.B.; Wang, H.; Xie, L.; Ding, S.-L.; Gertje, E.C.; Mancuso, L.; Kliot, D.; Das, S.R.; Wolk, D.A. Automated Volumetry and Regional Thickness Analysis of Hippocampal Subfields and Medial Temporal Cortical Structures in Mild Cognitive Impairment: Automatic Morphometry of MTL Subfields in MCI. Hum. Brain Mapp. 2015, 36, 258–287. [Google Scholar] [CrossRef]
  7. Battaglia, S.; Di Fazio, C.; Mazzà, M.; Tamietto, M.; Avenanti, A. Targeting Human Glucocorticoid Receptors in Fear Learning: A Multiscale Integrated Approach to Study Functional Connectivity. Int. J. Mol. Sci. 2024, 25, 864. [Google Scholar] [CrossRef] [PubMed]
  8. Bennett, C.; González, M.; Tapia, G.; Riveros, R.; Torres, F.; Loyola, N.; Veloz, A.; Chabert, S. Cortical Mapping in Glioma Surgery: Correlation of fMRI and Direct Electrical Stimulation with Human Connectome Project Parcellations. Neurosurg. Focus 2022, 53, E2. [Google Scholar] [CrossRef]
  9. Xie, L.; Wisse, L.E.M.; Pluta, J.; De Flores, R.; Piskin, V.; Manjón, J.V.; Wang, H.; Das, S.R.; Ding, S.; Wolk, D.A.; et al. Automated Segmentation of Medial Temporal Lobe Subregions on in Vivo T1-weighted MRI in Early Stages of Alzheimer’s Disease. Hum. Brain Mapp. 2019, 40, 3431–3451. [Google Scholar] [CrossRef]
  10. AddNeuroMed Consortium; Velayudhan, L.; Proitsi, P.; Westman, E.; Muehlboeck, J.-S.; Mecocci, P.; Vellas, B.; Tsolaki, M.; Kłoszewska, I.; Soininen, H.; et al. Entorhinal Cortex Thickness Predicts Cognitive Decline in Alzheimer’s Disease. JAD 2013, 33, 755–766. [Google Scholar] [CrossRef]
  11. Dong, Y.; Li, Y.; Liu, K.; Han, X.; Liu, R.; Ren, Y.; Cong, L.; Zhang, Q.; Hou, T.; Song, L.; et al. Anosmia, Mild Cognitive Impairment, and Biomarkers of Brain Aging in Older Adults. Alzheimer’s Dement. 2023, 19, 589–601. [Google Scholar] [CrossRef] [PubMed]
  12. Hagoort, P. On Broca, Brain, and Binding: A New Framework. Trends Cogn. Sci. 2005, 9, 416–423. [Google Scholar] [CrossRef] [PubMed]
  13. Carlén, M. What Constitutes the Prefrontal Cortex? Science 2017, 358, 478–482. [Google Scholar] [CrossRef] [PubMed]
  14. Nishitani, N.; Schürmann, M.; Amunts, K.; Hari, R. Broca’s Region: From Action to Language. Physiology 2005, 20, 60–69. [Google Scholar] [CrossRef] [PubMed]
  15. Catani, M.; Jones, D.K.; Ffytche, D.H. Perisylvian Language Networks of the Human Brain. Ann. Neurol. 2005, 57, 8–16. [Google Scholar] [CrossRef] [PubMed]
  16. Uylings, H.B.M.; Rajkowska, G.; Sanz-Arigita, E.; Amunts, K.; Zilles, K. Consequences of Large Interindividual Variability for Human Brain Atlases: Converging Macroscopical Imaging and Microscopical Neuroanatomy. Anat. Embryol. 2005, 210, 423–431. [Google Scholar] [CrossRef]
  17. Nogami, C.; Kobayashi, R.; Yokoi, K.; Ohba, M.; Hashimoto, R.; Sakamoto, K.; Inoue, K.; Otani, K.; Hirayama, K. Syntactic Impairment Associated with Hypoperfusion in the Left Middle and Inferior Frontal Gyri after Right Cerebellar Hemorrhage. Intern. Med. 2023, 62, 3405–3412. [Google Scholar] [CrossRef]
  18. Nasreddine, Z.S.; Phillips, N.A.; Bédirian, V.; Charbonneau, S.; Whitehead, V.; Collin, I.; Cummings, J.L.; Chertkow, H. The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool for Mild Cognitive Impairment. J. Am. Geriatr. Soc. 2005, 53, 695–699. [Google Scholar] [CrossRef]
  19. Dale, A.M.; Fischl, B.; Sereno, M.I. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage 1999, 9, 179–194. [Google Scholar] [CrossRef]
  20. Fischl, B.; Sereno, M.I.; Tootell, R.B.H.; Dale, A.M. High-Resolution Intersubject Averaging and a Coordinate System for the Cortical Surface. Hum. Brain Mapp. 1999, 8, 272–284. [Google Scholar] [CrossRef]
  21. Reuter, M.; Rosas, H.D.; Fischl, B. Highly Accurate Inverse Consistent Registration: A Robust Approach. NeuroImage 2010, 53, 1181–1196. [Google Scholar] [CrossRef] [PubMed]
  22. 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]
  23. Ségonne, 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]
  24. 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] [PubMed]
  25. Fischl, B.; Liu, A.; Dale, A.M. Automated Manifold Surgery: Constructing Geometrically Accurate and Topologically Correct Models of the Human Cerebral Cortex. IEEE Trans. Med. Imaging 2001, 20, 70–80. [Google Scholar] [CrossRef] [PubMed]
  26. Fischl, B.; Salat, D.H.; 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]
  27. 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]
  28. 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, S69–S84. [Google Scholar] [CrossRef]
  29. 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]
  30. Han, X.; Jovicich, J.; Salat, D.; van der Kouwe, A.; Quinn, B.; Czanner, S.; Busa, E.; Pacheco, J.; Albert, M.; Killiany, R.; et al. Reliability of MRI-Derived Measurements of Human Cerebral Cortical Thickness: The Effects of Field Strength, Scanner Upgrade and Manufacturer. NeuroImage 2006, 32, 180–194. [Google Scholar] [CrossRef]
  31. Jovicich, J.; Czanner, S.; Greve, D.; Haley, E.; van der Kouwe, A.; Gollub, R.; Kennedy, D.; Schmitt, F.; Brown, G.; Macfall, J.; 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] [CrossRef] [PubMed]
  32. Reuter, M.; Fischl, B. Avoiding Asymmetry-Induced Bias in Longitudinal Image Processing. NeuroImage 2011, 57, 19–21. [Google Scholar] [CrossRef] [PubMed]
  33. 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] [CrossRef] [PubMed]
  34. Fischl, B.; Sereno, M.I.; Dale, A.M. Cortical Surface-Based Analysis: II: Inflation, Flattening, and a Surface-Based Coordinate System. NeuroImage 1999, 9, 195–207. [Google Scholar] [CrossRef] [PubMed]
  35. Fischl, B.; Rajendran, N.; Busa, E.; Augustinack, J.; Hinds, O.; Yeo, B.T.T.; Mohlberg, H.; Amunts, K.; Zilles, K. Cortical Folding Patterns and Predicting Cytoarchitecture. Cereb. Cortex 2008, 18, 1973–1980. [Google Scholar] [CrossRef] [PubMed]
  36. Hinds, O.P.; Rajendran, N.; Polimeni, J.R.; Augustinack, J.C.; Wiggins, G.; Wald, L.L.; Rosas, H.D.; Potthast, A.; Schwartz, E.L.; Fischl, B. Accurate Prediction of V1 Location from Cortical Folds in a Surface Coordinate System. Neuroimage 2008, 39, 1585–1599. [Google Scholar] [CrossRef]
  37. Zilles, K.; Schleicher, A.; Palomero-Gallagher, N.; Amunts, K. Quantitative Analysis of Cyto- and Receptor Architecture of the Human Brain. In Brain Mapping: The Methods; Elsevier: Amsterdam, The Netherlands, 2002; pp. 573–602. ISBN 978-0-12-693019-1. [Google Scholar]
  38. Amunts, K.; Schleicher, A.; Zilles, K. Cytoarchitecture of the Cerebral Cortex--More than Localization. Neuroimage 2007, 37, 1061–1065. [Google Scholar] [CrossRef]
  39. Toga, A.W.; Thompson, P.M.; Mori, S.; Amunts, K.; Zilles, K. Towards Multimodal Atlases of the Human Brain. Nat. Rev. Neurosci. 2006, 7, 952–966. [Google Scholar] [CrossRef]
  40. Amunts, K.; Armstrong, E.; Malikovic, A.; Hömke, L.; Mohlberg, H.; Schleicher, A.; Zilles, K. Gender-Specific Left–Right Asymmetries in Human Visual Cortex. J. Neurosci. 2007, 27, 1356–1364. [Google Scholar] [CrossRef]
  41. JASP Team. JASP (Version 0.18.3). 2024. Available online: Https://Jasp-Stats.Org/ (accessed on 16 February 2024).
  42. Hämäläinen, P.; Rosti-Otajärvi, E. Cognitive Impairment in MS: Rehabilitation Approaches. Acta Neurol. Scand. 2016, 134, 8–13. [Google Scholar] [CrossRef]
  43. Van Ettinger-Veenstra, H. Cumulative Evidence for MS as a Neural Network Disconnection Syndrome Consistent with Cognitive Impairment Mechanisms and the Confounding Role of Fatigue and Depression-Outlook from the Fourth Nordic MS Symposium. Acta Neurol. Scand. 2016, 134, 4–7. [Google Scholar] [CrossRef] [PubMed]
  44. Sabatini, U.; Boulanouar, K.; Fabre, N.; Martin, F.; Carel, C.; Colonnese, C.; Bozzao, L.; Berry, I.; Montastruc, J.L.; Chollet, F.; et al. Cortical Motor Reorganization in Akinetic Patients with Parkinson’s Disease. Brain 2000, 123, 394–403. [Google Scholar] [CrossRef] [PubMed]
  45. Koepsell, T.D.; Monsell, S.E. Reversion from Mild Cognitive Impairment to Normal or Near-Normal Cognition: Risk Factors and Prognosis. Neurology 2012, 79, 1591–1598. [Google Scholar] [CrossRef] [PubMed]
  46. Vardy, J.L.; Dhillon, H.M.; Pond, G.R.; Rourke, S.B.; Bekele, T.; Renton, C.; Dodd, A.; Zhang, H.; Beale, P.; Clarke, S.; et al. Cognitive Function in Patients With Colorectal Cancer Who Do and Do Not Receive Chemotherapy: A Prospective, Longitudinal, Controlled Study. JCO 2015, 33, 4085–4092. [Google Scholar] [CrossRef] [PubMed]
  47. Simões-Franklin, C.; Whitaker, T.A.; Newell, F.N. Active and Passive Touch Differentially Activate Somatosensory Cortex in Texture Perception. Hum. Brain Mapp. 2011, 32, 1067–1080. [Google Scholar] [CrossRef]
  48. Ungerleider, S.K.A.L.G. Mechanisms of Visual Attention in the Human Cortex. Annu. Rev. Neurosci. 2000, 23, 315–341. [Google Scholar] [CrossRef] [PubMed]
  49. Hegdé, J.; Van Essen, D.C. Selectivity for Complex Shapes in Primate Visual Area V2. J. Neurosci. 2000, 20, RC61. [Google Scholar] [CrossRef] [PubMed]
  50. Coppen, E.M.; Van Der Grond, J.; Hart, E.P.; Lakke, E.A.J.F.; Roos, R.A.C. The Visual Cortex and Visual Cognition in Huntington’s Disease: An Overview of Current Literature. Behav. Brain Res. 2018, 351, 63–74. [Google Scholar] [CrossRef] [PubMed]
  51. Hasselmo, M.E.; Brandon, M.P. Linking Cellular Mechanisms to Behavior: Entorhinal Persistent Spiking and Membrane Potential Oscillations May Underlie Path Integration, Grid Cell Firing, and Episodic Memory. Neural Plast. 2008, 2008, 658323. [Google Scholar] [CrossRef]
  52. Lisowska, A.; Rekik, I.; AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; Bio-Clinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate, Eisai, Inc.; et al. Joint Pairing and Structured Mapping of Convolutional Brain Morphological Multiplexes for Early Dementia Diagnosis. Brain Connect. 2019, 9, 22–36. [Google Scholar] [CrossRef]
  53. James, A.; Von Oertzen, T.J.; Norbury, R.; Huppertz, H.-J.; Brandt, K.R. Left Entorhinal Cortex and Object Recognition. NeuroReport 2018, 29, 363–367. [Google Scholar] [CrossRef] [PubMed]
  54. Zhang, X.; Li, J.; Du, Y. Melodic Intonation Therapy on Non-Fluent Aphasia after Stroke: A Systematic Review and Analysis on Clinical Trials. Front. Neurosci. 2022, 15, 753356. [Google Scholar] [CrossRef]
  55. Paola, M.; Macaluso, E.; Carlesimo, G.A.; Tomaiuolo, F.; Worsley, K.J.; Fadda, L.; Caltagirone, C. Episodic Memory Impairment in Patients with Alzheimer’s Disease Is Correlated with Entorhinal Cortex Atrophy: A Voxel-Based Morphometry Study. J. Neurol. 2007, 254, 774–781. [Google Scholar] [CrossRef] [PubMed]
  56. Levar, N.; Francis, A.N.; Smith, M.J.; Ho, W.C.; Gilman, J.M. Verbal Memory Performance and Reduced Cortical Thickness of Brain Regions Along the Uncinate Fasciculus in Young Adult Cannabis Users. Cannabis Cannabinoid Res. 2018, 3, 56–65. [Google Scholar] [CrossRef] [PubMed]
  57. Tward, D.J.; Sicat, C.S.; Brown, T.; Bakker, A.; Gallagher, M.; Albert, M.; Miller, M. Alzheimer’s Disease Neuroimaging Initiative Entorhinal and Transentorhinal Atrophy in Mild Cognitive Impairment Using Longitudinal Diffeomorphometry. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 2017, 9, 41–50. [Google Scholar] [CrossRef]
  58. Loessner, L.; Matthes, C.; Haussmann, R.; Brandt, M.D.; Sauer, C.; Espin, M.; Noppes, F.; Werner, A.; Linn, J.; Hummel, T.; et al. Predictors of Subjective Cognitive Deficits in Patients with Mild Cognitive Impairment. Psychogeriatrics 2022, 22, 210–217. [Google Scholar] [CrossRef] [PubMed]
  59. Giannouli, V.; Tsolaki, M. Brain Volumes and Metacognitive Deficits in Knowledge of Self, Task and Strategies in Mathematics: A Preliminary Pilot One-Year Longitudinal Study in aMCI Patients Compared to Healthy Controls. Diagnostics 2023, 13, 680. [Google Scholar] [CrossRef]
  60. Stoeckel, L.E.; Stewart, C.C.; Griffith, H.R.; Triebel, K.; Okonkwo, O.C.; den Hollander, J.A.; Martin, R.C.; Belue, K.; Copeland, J.N.; Harrell, L.E.; et al. MRI Volume of the Medial Frontal Cortex Predicts Financial Capacity in Patients with Mild Alzheimer’s Disease. Brain Imaging Behav. 2013, 7, 282–292. [Google Scholar] [CrossRef]
  61. Giannouli, V.; Tsolaki, M. Are Left Angular Gyrus and Amygdala Volumes Important for Financial Capacity in Mild Cognitive Impairment? Hell. J. Nucl. Med. 2019, 22, 160–164. [Google Scholar]
  62. Gerstenecker, A.; Hoagey, D.A.; Marson, D.C.; Kennedy, K.M. White Matter Degradation Is Associated with Reduced Financial Capacity in Mild Cognitive Impairment and Alzheimer’s Disease. J. Alzheimer’s Dis. 2017, 60, 537–547. [Google Scholar] [CrossRef]
Medicina 60 00587 g001
Figure 1. Cortical thickness comparisons in primary somatosensory area (BA3a) in the left hemisphere between (from left to right) LPG, MPG, and NC with data distribution in each group.
Figure 1. Cortical thickness comparisons in primary somatosensory area (BA3a) in the left hemisphere between (from left to right) LPG, MPG, and NC with data distribution in each group.
Medicina 60 00587 g001
Medicina 60 00587 g002
Figure 2. Cortical thickness comparisons in secondary visual area (V2) in the right hemisphere between (from left to right) LPG, MPG, and NC with data distribution in each group.
Figure 2. Cortical thickness comparisons in secondary visual area (V2) in the right hemisphere between (from left to right) LPG, MPG, and NC with data distribution in each group.
Medicina 60 00587 g002
Medicina 60 00587 g003
Figure 3. Cortical thickness comparisons in entorhinal cortex (BA28) in the left hemisphere between (from left to right) LPG, MPG, and NC with data distribution in each group.
Figure 3. Cortical thickness comparisons in entorhinal cortex (BA28) in the left hemisphere between (from left to right) LPG, MPG, and NC with data distribution in each group.
Medicina 60 00587 g003
Medicina 60 00587 g004
Figure 4. Cortical thickness comparisons in entorhinal cortex (BA28) in the right hemisphere between (from left to right) LPG, MPG, and NC with data distribution in each group.
Figure 4. Cortical thickness comparisons in entorhinal cortex (BA28) in the right hemisphere between (from left to right) LPG, MPG, and NC with data distribution in each group.
Medicina 60 00587 g004
Table 1. Demographic data and Montreal Cognitive Assessment (MoCA) scores in study patients (divided in three groups).
Table 1. Demographic data and Montreal Cognitive Assessment (MoCA) scores in study patients (divided in three groups).
Gender (F:M)AgeMoCA
NCICINCICINCICI
Valid16:132:1517471747
Mean 70.94173.21327.52918.149
Std. Deviation 7.5548.2411.2316.890
Minimum 51.00057.00026.0004.000
Maximum 83.00096.00030.00025.000
Table 2. Demographic data and Montreal Cognitive Assessment (MoCA) scores in study patients (divided in three groups).
Table 2. Demographic data and Montreal Cognitive Assessment (MoCA) scores in study patients (divided in three groups).
Gender (F:M)Age MoCA
LPGMPGNCLPGMPGNCLPGMPGNC
Valid10:422:1116:1143317143317
Mean 74.28672.75870.9418.78622.12127.529
Std. Deviation 10.3737.2937.5543.7862.7921.231
Minimum 62.00057.00051.0004.00015.00026.000
Maximum 96.00085.00083.00014.00025.00030.000
X2 4.631
H (2) 0.340 52.795 ***
LPG—low cognitive performance group; MPG—moderate cognitive performance group; NC—normal cognition group. *** = p < 0.001.
Table 3. The Mann–Whitney U test by comparing the NCI and CI patient groups.
Table 3. The Mann–Whitney U test by comparing the NCI and CI patient groups.
Wdfp
BA1 in the left hemisphere340.000 0.373
BA1 in the right hemisphere274.000 0.057
BA2 in the left hemisphere370.000 0.659
BA2 in the right hemisphere333.000 0.316
BA3a in the left hemisphere241.000 0.016 *
BA3a in the right hemisphere377.500 0.744
BA3b in the left hemisphere373.000 0.693
BA3b in the right hemisphere408.500 0.897
BA4a in the left hemisphere314.000 0.196
BA4a in the right hemisphere269.000 0.048 *
BA4p in the left hemisphere349.000 0.451
BA4p in the right hemisphere280.500 0.072
BA6 in the left hemisphere309.500 0.174
BA6 in the right hemisphere299.000 0.128
BA44 in the left hemisphere348.000 0.438
BA44 in the right hemisphere303.500 0.147
BA45 in the left hemisphere360.500 0.558
BA45 in the right hemisphere287.000 0.089
V1 in the left hemisphere404.000 0.952
V1 in the right hemisphere397.500 0.982
V2 in the left hemisphere310.500 0.179
V2 in the right hemisphere275.500 0.060
MT in the left hemisphere415.000 0.820
MT in the right hemisphere359.500 0.548
BA35 in the left hemisphere351.500 0.470
BA35 in the right hemisphere483.000 0.209
BA28 in the left hemisphere584.000 0.005 *
BA28 in the right hemisphere523.000 0.062
* p < 0.05.
Table 4. Dunn’s post hoc comparison of the MoCA score and cortical thickness in primary somatosensory area (BA3a) in the left hemisphere between the LPG—MPG, LPG—NC, and MPG—NC groups.
Table 4. Dunn’s post hoc comparison of the MoCA score and cortical thickness in primary somatosensory area (BA3a) in the left hemisphere between the LPG—MPG, LPG—NC, and MPG—NC groups.
ComparisonzWiWjppbonfpholm
LPG—MPG−0.68033.03637.0760.4961.0000.496
LPG—NC1.46733.03623.1760.1420.4270.285
MPG—NC2.50137.07623.1760.0120.0370.037
Table 5. Dunn’s post hoc comparison of the MoCA score and cortical thickness in secondary visual area (V2) in the right hemisphere between the LPG—MPG, LPG—NC, and MPG—NC groups.
Table 5. Dunn’s post hoc comparison of the MoCA score and cortical thickness in secondary visual area (V2) in the right hemisphere between the LPG—MPG, LPG—NC, and MPG—NC groups.
ComparisonzWiWjppbonfpholm
LPG—MPG−1.66928.17938.0910.0950.2850.190
LPG—NC0.44228.17925.2060.6581.0000.658
MPG—NC2.31838.09125.2060.0200.0610.061
Table 6. Dunn’s post hoc comparison of the MoCA score and cortical thickness entorhinal cortex (BA28) in the left hemisphere between the LPG—MPG, LPG—NC, and MPG—NC groups.
Table 6. Dunn’s post hoc comparison of the MoCA score and cortical thickness entorhinal cortex (BA28) in the left hemisphere between the LPG—MPG, LPG—NC, and MPG—NC groups.
ComparisonzWiWjppbonfpholm
LPG—MPG−3.91512.25035.500<0.001<0.001<0.001
LPG—NC−4.62912.25043.353<0.001<0.001<0.001
MPG—NC−1.41335.50043.3530.1580.4730.158
Table 7. Dunn’s post hoc comparison of the MoCA score and cortical thickness entorhinal cortex (BA28) in the right hemisphere between the LPG—MPG, LPG—NC, and MPG—NC groups.
Table 7. Dunn’s post hoc comparison of the MoCA score and cortical thickness entorhinal cortex (BA28) in the right hemisphere between the LPG—MPG, LPG—NC, and MPG—NC groups.
ComparisonzWiWjppbonfpholm
LPG—MPG−2.54819.25034.3790.0110.0330.022
LPG—NC−3.05319.25039.7650.0020.0070.007
MPG—NC−0.96934.37939.7650.3330.9980.333
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Trišins, M.; Zdanovskis, N.; Platkājis, A.; Šneidere, K.; Kostiks, A.; Karelis, G.; Stepens, A. Brodmann Areas, V1 Atlas and Cognitive Impairment: Assessing Cortical Thickness for Cognitive Impairment Diagnostics. Medicina 2024, 60, 587. https://doi.org/10.3390/medicina60040587

AMA Style

Trišins M, Zdanovskis N, Platkājis A, Šneidere K, Kostiks A, Karelis G, Stepens A. Brodmann Areas, V1 Atlas and Cognitive Impairment: Assessing Cortical Thickness for Cognitive Impairment Diagnostics. Medicina. 2024; 60(4):587. https://doi.org/10.3390/medicina60040587

Chicago/Turabian Style

Trišins, Maksims, Nauris Zdanovskis, Ardis Platkājis, Kristīne Šneidere, Andrejs Kostiks, Guntis Karelis, and Ainārs Stepens. 2024. "Brodmann Areas, V1 Atlas and Cognitive Impairment: Assessing Cortical Thickness for Cognitive Impairment Diagnostics" Medicina 60, no. 4: 587. https://doi.org/10.3390/medicina60040587

Article Metrics

Back to TopTop