Cranial Catastrophe Beyond Respiratory Symptoms:
COVID-19’s Hidden Neurological Damage Revealed by MRI DTI Tractography
Risa Dameria Surbakti1*, Sugiyanto2,
Rasyid3, Tri Asih Budiati4, Leny Latifah5,
Poltekes Kemenkes Semarang1,2,3,4,5,
RSUD Ulin Banjarmasin1, Ciputra Mitra Hospital Banjarmasin1
Email: heavenly_icha@yahoo.com
|
KEYWORDS |
ABSTRACT |
|
Neuroimaging, Tractography, Diffusion Tensor Imaging, Recovered Covid-19 Patients,
Cranial Nerves |
Emerging evidence suggests that coronavirus disease 2019
(Covid-19) can significantly affect cranial nerves, resulting in various
neurological complications. Predominant issues include anosmia, ageusia, and
severe headaches. This study involved 30 recovered Covid-19 patients who
underwent MRI Tractography with a Superconductor 1.5 Tesla machine and
Diffusion Tensor Imaging (DTI). We analyzed correlations between gender, age,
Covid-19 symptoms, pathological findings, diffusion metrics, motor and
cognitive functions, and other clinical characteristics. Probabilistic
constrained spherical deconvolution tractography and tract quantification
were performed following diffusion tensor parameters, utilizing fiber
tracking methods and fractional anisotropy (FA) metrics. Tractography
reconstructions of cranial nerves were successfully achieved in all patients.
Affected cranial nerves showed decreased FA and disrupted fibers, with lower
axonal density in clinically recovered patients. Patients with moderate and
severe symptoms had lower FA in the cranial nerves and slightly more brain
abnormalities. Motor and cognitive deficits were prevalent among recovered
patients. This study demonstrates that DTI provides essential qualitative and
quantitative insights into the pathophysiology underlying neurological
disorders in Covid-19 patients. These insights can be used to improve
clinical outcomes and quality of life for patients’ post-recovery. |
|
|
Corresponding Author: Risa Dameria Surbakti1*
Email: heavenly_icha@yahoo.com
INTRODUCTION
Since its emergence in late 2019,
Coronavirus Disease 2019 (Covid-19) has become the cause of death for
approximately 6,823,213 people worldwide, out of a total of 670,203,160 cases.
According to data released by the Center for Systems Science and Engineering
(CSSE) at Johns Hopkins University (JHU) on January 29, 2023, around
663,379,947 individuals have successfully recovered. Several other studies
conducted in various countries have identified complications in the brain and
nervous system following Covid-19 infection
The majority of Covid-19 positive cases who
have successfully recovered fall within the age group of 15-64, which is
considered the productive working-age category
Brain MRI protocols encompass various
sequences, including SWI (Susceptibility Weighted Imaging) for detecting iron
and microbleeding, ASL (Arterial Spin Labeling) to measure blood flow
The aims of this research are to evaluate
the long-term neurological effects of Covid-19 using advanced MRI techniques
and to understand the structural and functional changes in the brain and
nervous system among recovering patients. This study will also investigate the
correlation between these changes and the severity of Covid-19, along with the
presence of comorbidities.
METHOD
Researchers selected 30 samples at
Banjarmasin Hospital from July 2020 to April 2023. The patients were aged
between 18 and 65 years and had been infected with Covid-19, with or without
symptoms. The severity of Covid-19 symptoms was categorized into five levels.
Post-recovery MRI scans were conducted to identify brain pathological findings,
focusing on lesions and vascular abnormalities. Participants reported various
neurological symptoms post-Covid-19, including cognitive impairments and motor
disturbances. FA values were analyzed to assess white matter integrity in the
brain, indicating the extent of nerve fiber damage and anisotropy levels.
Spearman’s Rho Correlation Test and Mann-Whitney U Test were used in three
statistical tests to examine gender differences in Covid-19 symptom severity,
explore gender disparities in MRI brain pathological findings, and evaluate the
relationship between age and Covid-19 symptom severity
RESULT AND DISCUSSION
Based on age categories, there is variation
in the number of samples, where the age category with the highest number of
samples is 26-35 years old, with 10 samples (33.3% of the total). The age
categories with the second-highest number of samples are 36-45 years old and
46-55 years old, each with 7 samples (23.3% of the total). The age categories
with the lowest number of samples are 17-25 years old and 56-65 years old, each
with 3 samples (10% of the total). The data collected on the severity of Covid-19
symptoms in relation to pathological findings in post-recovery Brain MRI scans
reveals significant differences. Among the samples, those with mild Covid-19
symptoms exhibited the highest number of pathological findings (12 samples). In
contrary, samples without symptoms still showed a substantial number of
pathological findings (9 samples), while samples with moderate symptoms had 8
samples with such findings. Remarkably, samples with severe Covid-19 symptoms
exhibited a significantly lower incidence of pathological findings, with only 1
sample indicating such abnormalities. Samples experiencing neurological
disturbances after recovering from Covid-19 are categorized into three
indications: motoric, cognitive, and other neurological disorder indications.
Out of the total samples, 16% exhibited cognitive disturbances, 25% experienced
motoric disturbances, and 59% of the samples showed indications of other
neurological disorders.
Table 1.
Classification of Covid -19 severity and
Brain MRI Expertise Post Covid-19
|
Age (year) |
Classification
of Covid-19 Symptom |
Post Covid-19 Expertise MRI Finding |
|||||||
|
With Pathology |
Without Pathology |
||||||||
|
No Symptom |
Mild |
Moderate |
Severe |
|
♀ |
♂ |
♀ |
♂ |
|
|
17-25 |
2 |
1 |
0 |
0 |
|
1 |
0 |
1 |
1 |
|
26-35 |
7 |
2 |
1 |
0 |
|
1 |
4 |
2 |
3 |
|
36-45 |
0 |
6 |
1 |
0 |
|
5 |
0 |
0 |
2 |
|
46-55 |
0 |
2 |
5 |
0 |
|
3 |
4 |
0 |
0 |
|
56-65 |
0 |
1 |
1 |
1 |
|
2 |
1 |
0 |
0 |
|
Total |
9 |
12 |
8 |
1 |
|
12 |
9 |
3 |
6 |
From the Tabel 1 based on the
severity of their symptoms, the age group of 26-35 years has the highest
percentage in the "Moderate" category at 23.33%. Meanwhile, in the
age group of 56-65 years, there is one sample (3.33%) experiencing severe
symptoms. The brain MRI revealed that 3 samples (14%) had pathological findings
on the right side, 2 samples (10%) had pathological findings on the left side,
and 16 samples (76%) had pathological findings on both sides (bilateral).
Table 2.
Neurology Disorder Post Covid-19 and Diffusion Tensor
Imaging on Cranial Nerves
|
MRI Samples |
Neurological Disorder Post Covid-19 |
||
|
Cognitive (a) |
Motoric (b) |
Others (c) |
|
|
No Disorder |
8 (26,67%) |
13 (43,33%) |
30 (100%) |
|
Single Disorder |
5 (16,67%) |
1 (3,33%) |
19 (63,33%) |
|
Multiple Disorder |
5 (16,67%) |
5 (16,67%) |
5 (16,67%) |
From the Table 2,
the statistical results of
the sample population that underwent Brain MRI scans with Post Covid-19,
Neurological Disorders was obtained. The diagnosis results before the MRI
examination revealed the following: Cognitive disorders were present in 8
samples (26.67%), Motoric disorders were observed in 13 samples (43.33%), Other
disorders were found in all samples (100%). There were samples that experienced
only one type of disorder, as follows: 19 samples (63.33%) had other disorders
without cognitive or motoric disturbances, 5 samples (16.67%) exhibited
cognitive disorders only, without motoric or other disorders, 1 sample (3.33%)
had motoric disorders alone, without cognitive or other disorders. There were
samples that experienced more than one type of disorder: 5 samples (16.67%) had
cognitive, motoric, and other disorders simultaneously.
Table 3.
Statistic Test Gender Differences in the Severity of
Covid-19 Symptoms
|
Statistic Test |
Severity of Covid-19 Symptoms |
|
Mann-Whitney U |
63.500 |
|
Wilcoxon W |
183.500 |
|
Z |
-2.136 |
|
Asymp.Sig. (2-tailed) |
.033 |
|
Exact Sig.
[2*(1-tailed Sig.)] |
.041b |
To explore relationships within the data,
three statistical tests were conducted. The first test, the Mann-Whitney U Test, examined gender differences in the severity of
Covid-19 symptoms. It revealed a significant relationship with a p-value of
0.033, indicating that gender influences the severity of Covid-19 symptoms in
this sample. This suggests that male and female participants experienced
different levels of symptom severity as it seen on Tabel 3.
Table 4.
Statistic Test Gender Disparities in MRI Brain
Pathological Findings Post-Covid-19
|
Statistic Test |
Pathological Findings Post-Covid-19 |
|
Mann-Whitney U |
90.000 |
|
Wilcoxon W |
210.000 |
|
Z |
-1.175 |
|
Asymp.Sig. (2-tailed) |
.240 |
|
Exact Sig.
[2*(1-tailed Sig.)] |
.367b |
a. Grouping Variable: Gender
b.
Not corrected for ties.
Another Mann-Whitney U Test was conducted
to explore gender disparities in MRI Brain pathological
findings post-Covid-19 as it can be seen on Table 4. However, this test
found no significant correlation, with a p-value of 0.240. This suggests that
gender does not significantly affect the pathological findings in MRI brain
results after recovery from Covid-19, indicating similar brain pathological changes
in both male and female participants. The third test, Spearman’s Rho
Correlation Test, evaluated the relationship between age and the severity of
Covid-19 symptoms. Table 5 showed a significant positive correlation with a
correlation coefficient of 0.730, indicating that older age correlates with a
higher likelihood of experiencing severe Covid-19 symptoms. This suggests that
as age increases, the severity of symptoms tends to be higher, highlighting the
impact of age on Covid-19 severity.
Table 5.
Statistic Test Relationship Between Age and the
Severity of Covid-19 Symptoms
|
|
|
|
Age |
Severity of Covid-19 Symptoms |
|
Spearman’s Rho |
Age |
Correlation Coefficient |
1.000 |
.704** |
|
|
|
Sig (2-tailed) |
. |
<.001 |
|
|
|
N |
30 |
30 |
|
|
Severity of Covid-19 Symptoms |
Correlation Coefficient |
.704** |
1.000 |
|
|
|
Sig (2-tailed) |
<.001 |
. |
|
|
|
N |
30 |
30 |
Four categories were identified based on
the range of FA values. In category 1, no samples were included, indicating
that no Covid-19 survivors had a high level of anisotropy with FA values above
0.8. This suggests that nerve fibers in Covid-19 survivors are generally not
consistently well-organized after viral infection. Category 2 also had no
samples included. The FA value range between 0.6 to 0.8
indicates a moderate level of anisotropy, where most nerve fibers remain
well-organized. However, in Covid-19 survivor samples, there may be some
variations in fiber density or orientation. Category 3 includes samples with FA
values below 0.6. This range indicates a low level of anisotropy, which can
be caused by damage, structural changes, or disruptions in fiber density or
orientation. In this study, there were two samples included in category 3,
contributing to approximately 6.7% of the total samples. This indicates that a
small number of Covid-19 survivors experienced anisotropy changes leading to
fiber damage or disruption. Category 4 is the dominant category, with 28
samples or approximately 93.3% of the total samples in this category. The FA
value range below 0.3 indicates a very low level of anisotropy, indicating
serious damage to nerve fibers or significant structural loss.
Figure 1. Fractional Anisotropy of CN I,
and CN II from samples with FA value range between 0.6 to 0.8 (moderate level
of anisotropy), and below 0.6 (low level of anisotropy).
By using Fiber Tracking, Infiltrated
condition found on Cranial Nerve II (Optic Nerve), Cranial Nerve III
(Oculomotor Nerve), Cranial Nerve IV (Trochlear Nerve), Cranial Nerve V
(Trigeminal Nerve), Cranial Nerve VI (Abducens Nerve), Cranial Nerves VII and
VIII (Facial and Vestibulocochlear Nerves), Cranial Nerves IX, X, and XI
(Glossopharyngeal, Vagus, and Accessory Nerves). While both Infiltrated and
disrupted condition found on Cranial Nerve I (Olfactory Nerve) and Cranial
Nerve XII (Hypoglossal Nerve). These findings underscore the complex and varied
neurological manifestations that COVID-19 can have on cranial nerves, affecting
sensory and motor functions related to vision, eye movements, facial
expressions, auditory perception, and more.
Figure 2. Fiber Tracking CN I (Olfactory)
and CN IX, X, and XI (Glossopharyngeal, Vagus, and Accessory) from samples with
infiltrated and disrupted condition
CONCLUSION
This study provides strong evidence of the
impact of Covid-19 on the human brain. Brain MRI results revealed significant
pathological findings in Covid-19 patients. Notably, 14% had right-sided brain pathology,
10% had left-sided pathology, and a striking 76% had bilateral brain pathology.
Most of these findings (79%) were categorized as circulatory system lesions,
while 21% fell into other categories. Fiber Tracking analysis showed
disruptions in nerve fiber pathways associated with affected brain areas,
including the prefrontal cortex, corpus callosum, and corticospinal tract. This
disruption relevant with anosmia (loss of smell) in Covid-19 patients,
highlighting changes in brain connectivity and nerve fiber pathways. Fractional
Anisotropy analysis indicated reduced FA values in white matter brain tissue,
potentially signifying nerve damage or dysfunction in regions related to
cognitive and emotional functions. However, it's crucial to note that not all
pathological findings directly correspond to neurological symptoms. While the
direct relationship between these changes and Covid-19 requires more
investigation, these techniques are useful for exploring the virus's
neurological effects. Further research is needed to fully understand Covid-19's
neurological impact.
REFERENCE
Berger, J. R. (2020). COVID-19 and the nervous system. Journal of
Neurovirology, 26, 143–148.
Bozkurt, B., Aguilar, D., Deswal, A., Dunbar, S. B., Francis, G. S.,
Horwich, T., Jessup, M., Kosiborod, M., Pritchett, A. M., Ramasubbu, K.,
Rosendorff, C., & Yancy, C. (2016). Contributory Risk and Management of
Comorbidities of Hypertension, Obesity, Diabetes Mellitus, Hyperlipidemia, and
Metabolic Syndrome in Chronic Heart Failure: A Scientific Statement From the American Heart Association. Circulation, 134(23).
https://doi.org/10.1161/CIR.0000000000000450
Comisión Económica para América Latina y el Caribe, Naciones Unidas,
& Organización Panamericana de la Salud. (2021). COVID-19 Report: The
prolongation of the health crisis and its impact on health, the economy and
social development. Coediciones, October, 1–37.
Huang, Y., Ling, Q., Manyande, A., Wu, D., & Xiang, B. (2022). Brain
Imaging Changes in Patients Recovered From COVID-19: A Narrative Review. Frontiers
in Neuroscience, 16(October 2021), 1–12.
https://doi.org/10.3389/fnins.2022.855868
Iglay, K., Hannachi, H., Joseph Howie, P., Xu, J., Li, X., Engel, S. S.,
Moore, L. M., & Rajpathak, S. (2016). Prevalence and co-prevalence of
comorbidities among patients with type 2 diabetes mellitus. Current Medical
Research and Opinion, 32(7), 1243–1252.
https://doi.org/10.1185/03007995.2016.1168291
Koralnik, I. J., & Tyler, K. L. (2020). <scp>COVID</scp>
‐19: A Global Threat to the Nervous System. Annals of Neurology, 88(1),
1–11. https://doi.org/10.1002/ana.25807
MacDonald, M. E., & Frayne, R. (2015). Cerebrovascular MRI: a review
of state‐of‐the‐art approaches, methods and techniques. NMR
in Biomedicine, 28(7), 767–791. https://doi.org/10.1002/nbm.3322
Marshall, M. (2020). How COVID-19 can damage the brain. Nature, 585(7825),
342–343.
Murray, K. D. (2021). Quantifying magnetic susceptibility to explore
the pathomechanisms of cerebral small vessel disease. University of
Rochester.
Neishaboori, A. M., Moshrefiaraghi, D., Ali, K. M., Toloui, A.,
Yousefifard, M., & Hosseini, M. (2020). Central Nervous System
Complications in COVID-19 Patients; a Systematic Review and Meta-Analysis
based on Current Evidence. Archives of Academic Emergency Medicine, 8(1),
e62. https://doi.org/10.22037/aaem.v8i1.798
Pelizzari, L., Cazzoli, M., Lipari, S., Laganà, M. M., Cabinio, M.,
Isernia, S., Pirastru, A., Clerici, M., & Baglio, F. (2022). Mid-term MRI
evaluation reveals microstructural white matter alterations in COVID-19 fully
recovered subjects with anosmia presentation. Therapeutic Advances in
Neurological Disorders, 15, 1–10.
https://doi.org/10.1177/17562864221111995
Prezzi, D., & Goh, V. (2016). Rectal Cancer Magnetic Resonance
Imaging: Imaging Beyond Morphology. Clinical Oncology, 28(2),
83–92. https://doi.org/10.1016/j.clon.2015.10.010
Sarma, S., Sockalingam, S., & Dash, S. (2021). Obesity as a
<scp>multisystem</scp> disease: Trends in obesity rates and
<scp>obesity‐related</scp> complications. Diabetes,
Obesity and Metabolism, 23(S1), 3–16.
https://doi.org/10.1111/dom.14290
Spudich, S., & Nath, A. (2022). Nervous system consequences of
COVID-19. Science, 375(6578), 267–269.
https://doi.org/10.1126/science.abm2052
Surbakti, R. D., Studi, P., Terapan, M., Diagnostik, I., Pascasarjana,
P., & Semarang, P. K. (2023). EVALUASI DAMPAK CORONAVIRUS DISEASE 2019 ( COVID-19 ) TERHADAP SARAF KRANIAL DENGAN MRI TRACTOGRAPHY
DIFFUSION TENSOR IMAGING : 2019.
Wang, L., Davis, P. B., Volkow, N. D., Berger, N. A., Kaelber, D. C.,
& Xu, R. (2022). Association of COVID-19 with New-Onset Alzheimer’s
Disease. Journal of Alzheimer’s Disease, 89(2), 411–414.
https://doi.org/10.3233/JAD-220717
Weiskopf, N., Edwards, L. J., Helms, G., Mohammadi, S., & Kirilina,
E. (2021). Quantitative magnetic resonance imaging of brain anatomy and in
vivo histology. Nature Reviews Physics, 3(8), 570–588. https://doi.org/10.1038/s42254-021-00326-1
Wesselingh, R. (2023). Prevalence, pathogenesis and spectrum of
neurological symptoms in COVID-19 and post-COVID-19 syndrome: a narrative
review. Medical Journal of Australia, 219(5), 230–236.
https://doi.org/10.5694/mja2.52063
|
|
©
2024 by the authors. It was submitted for possible open-access publication
under the terms and conditions of the Creative Commons Attribution (CC BY SA)
license (https://creativecommons.org/licenses/by-sa/4.0/). |
