The ongoing pandemic of Coronavirus disease 2019 (COVID-19) has infected more than 27 million confirmed cases and 8,90,000 deaths all around the world. Verity of viral infections can infect the nervous system; these viral infections can present a wide range of manifestation. The aim of the current study was to systematically review the COVID-19 associated central nervous system manifestations, mental and neurological symptoms. For that we conducted a comprehensive systematic literature review of four online databases, including Web of Science, PubMed, Scopus and Embase. All relevant articles that reported psychiatric/psychological symptoms or disorders in COVID-19 without considering time and language restrictions were assessed. All the study procedures were performed based on the PRISMA criteria. Due to the screening, 14 studies were included. The current study result indicated that, the pooled prevalence of CNS or mental associated disorders with 95% CI was 50.68% (6.68–93.88). The most prevalence symptoms were hyposmia/anosmia/olfactory dysfunction (number of study: 10) with 36.20% (14.99–60.51). Only one study reported numbness/paresthesia and dysphonia. Pooled prevalence of numbness/paresthesia and dysphonia was 5.83% (2.17–12.25) and 2.39% (10.75–14.22). The pooled prevalence of depression and anxiety was 3.52% (2.62–4.54) and 13.92% (9.44–19.08). Our findings demonstrate that COVID-19 has a certain relation with neurological symptoms. The hypsomia, anosmia or olfactory dysfunction was most frequent symptom. Other symptoms were headache or dizziness, dysgeusia or ageusia, dysphonia and fatigue. Depression, anxiety, and confusion were less frequent symptoms.
The ongoing pandemic of Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2) (Soltani 2020), has infected more than 27 million confirmed cases and 8,90,000 deaths all around the world (World Health Organization 2020). The COVID-19 mean incubation period consider as 2–5 days (Wiersinga et al. 2020). The COVID-19 associated symptoms and death timeline ranged from 6 to 41 days, with a median of 14 days (Wang et al. 2020). The age and the patient’s immune system are critical factors in COVID-19 outcome (Wang et al. 2020). The most common presented symptoms in COVID-19 patients are fever, cough and fatigue; whereas other symptoms include sputum production, headache, hemoptysis, diarrhea, dyspnea, and lymphopenia were reported (Carlos et al. 2020; Huang et al. 2020; Ren et al. 2020).
Verity of viral infections can infect the nervous system; these viral infections can present a wide range of manifestation including severe encephalitis, toxic encephalopathy and severe acute demyelinating lesions developing after viral infections (Michalicova et al. 2017; Wright et al. 2008). Some viruses are neurotropic or infect immune cells of central nervous system (CNS) microglia or astrocytes (Al-Obaidi et al. 2018; Soung and Klein 2018). SARS-CoV-2 is highly homologous with SARS-CoV (Grifoni et al. 2020). Recent emergent coronaviruses, SARS-CoV and MERS-CoV can infect CNS in patients and animal models (Li et al. 2020). The possibility of SARS-CoV-2 infection in the CNS and conducting neurological damage is not negligible. Also, both SARS-CoV-2 and SARS-CoV invades human cells by angiotensin-converting enzyme-2 (ACE-2) (Hoffmann et al. 2020), an essential component of the renin-angiotensin system in the brain (Abiodun and Ola 2020). Neurological symptoms were reported in COVID-19 patients. These manifestations could be presented as headache, disturbed consciousness and paresthesia. A study of 214 COVID-19 patients introduced that the incidence of neurological damage caused by SARS-CoV-2 estimated as 36.4% (Mao et al. 2020). Previous studies in the field of the COVID-19 neurological manifestations are focused on the case reports of neurological manifestations (Ellul et al. 2020; Tsivgoulis et al. 2020). The aim of the current study was to comprehensively update previous studies in the field of the COVID-19 neurological manifestations. Also, due to the ongoing COVID-19 pandemic, the aim of the current review and meta-analysis was to systematically review the COVID-19 associated central nervous system manifestations and neurological symptoms.
Search strategy and screening
All the study procedures were conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Liberati et al. 2009). We conducted a comprehensive systematic literature review of four online databases, including Web of Science, PubMed, Scopus and Embase. All relevant articles that reported psychiatry/psychology symptoms/disorders in COVID-19 without considering time and language restrictions were retrieved. Social Science Research Network and MedRxiv searched to obtain unpublished or preprints. Google scholar was used for grey literature access. Search was conducted by using following keywords, “COVID-19”, “Coronavirus”, “Neurologic”, “Central nervous system”, “SARS-CoV-2”, “Psychiatry”, and “Psychology”. The PICO in this study was as follows:
Population: child and adult with Covid-19
The details of the search strategy are presented in Table 1.
|1. COVID-19 [text word] OR COVID-19 [Mesh term]|
|2. Coronavirus [text word] OR Coronavirus [Mesh term]|
|3. SARS-CoV-2 infection [text word] OR SARS-CoV-2 infection [Mesh term]|
|4. 1 OR 2 OR 3|
|5. Syndromes [text word] OR Syndromes [Mesh term]|
|6. Disorders [text word] OR Disorders [Mesh term]|
|7. characteristic [text word] OR characteristic [Mesh term]|
|8. Symptoms [text word] OR Symptoms [Mesh term]|
|9. Sign [text word] OR Sign [Mesh term]|
|10. 5 OR 6 OR 7 OR 8 OR 9|
|11. Psychiatry [text word] OR Psychiatry [Mesh term]|
|12. Psychology [text word] OR Psychology [Mesh term]|
|13. 11 OR 12|
|14. 4 AND 10 AND 13|
MeSH, Medical Subject Headings.
The conducted search result imported in EndNote Version. X9 (Thomson Reuters) and after duplicate removal the screening was performed. The screening in three steps was performed by title and abstract and full text. All included studies met the inclusion criteria. The screening was performed by two independent authors (RP & SS) and the third expert author (IP) strategy was used for conflicts. Blinding and task separation were applied in the study selection procedure. The inter-rater agreement was 92%. All of the studies on the field of the COVID-19 associated central nervous system manifestations and neurological symptoms were included. The exclusion criteria for current study were case report and case series studies conducted in less than five patients as sample size.
In the current study, any psychiatric disorders/psychology in COVID-19 patient were reviewed in addition other information’s such as name of the author, the publication year, the country, study design, sample size and participants age was extracted. Also other general symptoms include mental disorder, confusion, depression, dementia, anxiety, fatigue/weakness, sleep disorder/drowsiness, delirium, cerebrovascular disease, nervous system disorders, headache, dizziness, dysphonia, hyposmia/anosmia/olfactory dysfunction, dysgeusia/ageusia, auditory dysfunction and numbness/paresthesia were extracted.
The Newcastle-Ottawa Scale (NOS) scale (Wells et al. 1999) was used for assessing the quality of included studies. The scaling of included studies was performed as previous research (Hashemi et al. 2020). This scale has three sections: 1-selection (4 items, maximum score: 4 points), 2-Confounder (1 item, maximum score: 1 points), and 3-Exposure (2 items, maximum score: 2 points). The studies were evaluated by two raters (RP & SS) independently, and a total score was calculated for each study. The studies were then assigned to one of the following categories accordingly: very good studies: 6–7 scores; good studies: 4–5 scores; satisfactory studies: 2–3 scores; unsatisfactory studies: 0–1 score.
The Stata version 14 was used for statistical analysis. Heterogeneity was assessed by Cochran’s Q test of heterogeneity and the I2 index was used to quantify heterogeneity. In accordance with Higgins classification, I2 values more than 0.7 were considered as high heterogeneity. The “metaprop” used for pooled prevalence calculation by using the random-effects model (Hallajzadeh et al. 2018; Hashemi et al. 2018; Hashemi et al. 2019; Hashemi et al. 2020; Pakzad et al. 2018). The exact method was used for standard error calculation. The meta-regression analysis was used to examine the effect of age, gender, sample size, and publication date as factors affecting heterogeneity among studies. The “metabias” command was used to check the publication bias, and if there was any publication bias, the prevalence rate was adjusted with the “metatrim” command using trim-and-fill method (Hallajzadeh et al. 2018; Hashemi et al. 2018; Hashemi et al. 2019; Hashemi et al. 2020; Pakzad et al. 2018). In all analysis a significance level of 0.05 was considered.
Search results and study population
A total of 1995 studies were retrieved from different databases and 681 studies obtain after remove duplicate papers. Due to the screening, 14 studies were included (the detail are illustrated in Figure 1).
The patient’s population in all 14 included studies was 3148 COVID-19 patients. The age was ranged from 19 to 95 years. The study setting assessment indicates 9 (64%) of the studies are Case series and 3 (21%) cross-sectional. The geographical location of the conducted studies indicated, 3 (21%) from China, 3 (21%) Italy and 2 (14.2%) USA. Other data is summarized in Table 2.
|Author||Country||Year||Study Design||Age (Range)||SS||Dementia as comorbidity||Confusion||Depression||Anxiety||Fatigue/Weakness||Sleep disorder/Drowsiness||Delirium|
|Aggarwal et al. 2020||USA||2020||CA||67 (38–95)||16||–||–||–||–||8||–||–|
|Bhat et al. 2020||USA||2020||CA||54.5 ± 11.5||8||–||–||–||–||–||–||–|
|Chen et al. 2020||China||2020||CA||55.5 ± 13.1||99||–||9||–||–||–||–||–|
|Chen et al. 2020||China||2020||CA||47.5||145||–||–||–||–||59||–||–|
|Hung et al. 2020||China||2020||CS||32–62||127||–||–||–||–||–||–||–|
|Speth et al. 2020||Switzerland||2020||CS||46.8 ± 15.9||103||–||–||–||–||–||–||–|
|Bianchetti et al. 2020||Italy||2020||CA||70.7 ± 12.9||627||82||–||–||–||–||–||55|
|Liguori et al. 2020||Italy||2020||CS||55 ± 14.65||103||–||23||39||34||33||51||–|
|Mao et al. 2020||China||2020||CA||52.7 ± 15.5||214||–||–||–||–||–||–||–|
|Lovell et al. 2020||UK||2020||CA||82 (72–89)||101||31||–||–||2||9||36||24|
|Tostmann et al. 2020||Netherlands||2020||CO||-||90||–||–||–||–||–||–||–|
|Lechien et al. 2020||Europe||2020||CS||39.17 ± 12.09||1420||–||–||36||–||–||–||–|
|Heidari et al. 2020||Iran||2020||CA||37.4||23||–||–||–||–||4||–||–|
|Gelardi et al. 2020||Italy||2020||CA||49.7 (19–70)||72||–||–||–||–||29||–||–|
CVD, Cerebrovascular disease; OD, Olfactory dysfunction; SS, Sample size; CA, Case series; CS, Cross-sectional; CO, Cohort.
By the assessment of 14 included studies, the CNS or mental associated disorders in all population study were listed in Table 1. Figure 2 and Table 3 are shown pool prevalence of symptoms/psychiatric disorders/psychology.
|Symptom||Heterogeneity||Number of studies||PPE%||95% CI|
|Mental disorder||I2 = 98.21%; Tau2 = 0.72; p < 0.001||3||50.68||(6.85–93.88)|
|Confusion||I2 = 0%; Tau2 < 0.001; p:0.346||2||15.27||(10.57–20.62)|
|Depression||I2 = 91.7%; Tau2 = 0.057; p:0.001||2||3.52||(2.62–4.54)|
|Anxiety||I2 = 79.7%; Tau2 = 0.038; p:0.026||2||13.92||(9.44–19.08)|
|Fatigue/weakness||I2 = 88.90; Tau2 = 0.11; p < 0.001||6||29.80||(17.13–44.18)|
|Sleep disorder/drowsiness||I2 = 0.01%; Tau2 < 0.001; p:0.321||2||42.57||(35.83–49.46)|
|Delirium||I2 = 49.1%; Tau2 = 0.006; p:0.161||2||10.34||(8.21–12.69)|
|CVD/ NSD||I2 = 98.38%; Tau2 = 0.320; p < 0.001||5||12.19||(0.59–33.35)|
|Headache/dizziness||I2 = 98.89%; Tau2 = 0.52; p < 0.001||9||28.77||(9.93–52.39)|
|Dysphonia||I2 = 0% ; Tau2 = -; p = -||1||12.39||(10.75–14.22)|
|Hyposmia/anosmia/OD||I2 = 98.71%; Tau2 = 0.57; p < 0.001||10||36.20||(14.99–60.51)|
|Dysgeusia/ageusia||I2 = 97.84%; Tau2 = 0.32; p < 0.001||7||35.20||(15.84–57.32)|
|Auditory dysfunction||I2 = 0.01%; Tau2 < 0.001; p:0.965||2||1.59||(0.28–3.68)|
|Numbness/paresthesia||I2 = 0%; Tau2 = -; p = -||1||5.83||(2.17–12.25)|
CVD, Cerebrovascular disease; NSD, Nervous system disorders; OD, Olfactory dysfunction; CI, Confidence interval; PPE, Pooled prevalence estimate.
According to this, the pooled prevalence of CNS or mental associated disorders with 95% CI was 50.68% (6.68–93.88). The most prevalence symptoms were hyposmia/anosmia/olfactory dysfunction (number of study: 10) with 36.20% (14.99–60.51). Only one study reported numbness/paresthesia and dysphonia. Pooled prevalence of numbness/paresthesia and dysphonia was 5.83% (2.17–12.25) and 2.39% (10.75–14.22); respectively. The pooled prevalence of depression and anxiety was 3.52% (2.62–4.54) and 13.92% (9.44–19.08). The pooled prevalence of the other disorders has been shown in Figure 2 and Table 3.
Heterogeneity and meta-regression
The Table 3 presents the results of the heterogeneity. According to Cochran’s Q test of heterogeneity, there was significant heterogeneity among studies for all outcomes except confusion, sleep disorder/drowsiness, delirium, dysphonia (because there was only 1 study for this sub group), auditory dysfunction and numbness/paresthesia (because there was only 1 study for this sub group) and Auditory dysfunction. The I2 index for most outcomes (including mental disorder, depression, anxiety, fatigue/weakness, CVD/NSD, headache/dizziness, hyposmia/anosmia/OD and dysgeusia/ageusia) was up to 80%. According to meta-regression results, the age had the significant effect on hyposmia/anosmia/OD prevalence; so that by increasing age, prevalence of hyposmia/anosmia/OD (coefficient: −2.81; p: 0.025) decreased significantly (Figure 3). For other outcomes the age has no effect on heterogeneity.
Based on the results of Egger’s test, there is no significant publication bias in our meta-analysis.
The aim of the current review and meta-analysis was to systematically review the COVID-19 associated central nervous system manifestations and neurological symptoms. Due to the screening and inclusion criteria 14 studies with 3148 sample size were assessed. Neurological symptoms in COVID-19 patients were reported from USA, China, Switzerland, Italy, UK, Netherland, Europe and Iran (Table 2). The current study result indicated that, the pooled prevalence of CNS or mental associated disorders with 95% CI was 50.68% (6.68–93.88). The most prevalence symptoms were hyposmia/anosmia/olfactory dysfunction with 36.20% (14.99–60.51), numbness/paresthesia 5.83% (2.17–12.25), dysphonia 2.39% (10.75–14.22), depression 3.52% (2.62–4.54) and anxiety 13.92% (9.44–19.08). Dysphonia and numbness or paresthesia was only seen in Europe and Italy in 39.17 ± 12.09 and 55 ± 14.65 ages (Range) respectively (Lechien et al. 2020; Liguori et al. 2020).
Neurological disease have reported in some respiratory viruses such as RSV (Bohmwald et al. 2018; Halfhide et al. 2011), influenza A and B (Xu et al. 1998) and enterovirus D68 (Imamura et al. 2014). The viruses can be detected in blood and they may use circulatory system to reach the CNS (Desforges et al. 2020). Respiratory syncytial virus (RSV) is a cause of lower respiratory tract infection in infects and children. The virus classified in the Orthopneumovirus genus (Walker et al. 2019). There are evidences due to the RSV neuroinvasive properties (Antonucci et al. 2010; Bohmwald et al. 2018; Nair et al. 2010). The RSV neurological manifestations could be including convulsions, febrile seizures and different types of encephalopathy and ataxia (Picone et al. 2019; Wallace and Zealley 1970). Also, human metapneumovirus (hMPV) is a respiratory pathogen and sporadically can induce seizures, encephalitis and encephalopathies (associated with epileptic symptoms) (Fernández et al. 2012; Wallace and Zealley 1970). Most influenza virus infections are limited to the upper respiratory tract but also its complications can involve the CNS (Kuiken and Taubenberger 2008; Popescu et al. 2017). Several studies have demonstrate that influenza A can be associated with encephalitis, Reye’s syndrome, febrile seizure, Guillain–Barré syndrome, acute necrotizing encephalopathy and probably acute disseminated encephalomyelitis (Jang et al. 2009; Sivadon-Tardy et al. 2009; Zeng et al. 2013). The influenza a virus can also increasing the risk of Parkinson’s disease developing (Jang et al. 2009; World Health Organization 2020). Recent investigations on suggested that, the influenza virus could induce experimental autoimmune encephalomyelitis (EAE), which reminds that recurrence of multiple sclerosis (MS) have been associated with viral infections (including influenza A) (Chen et al. 2017; Edwards et al. 1998). Various respiratory viruses have neurotropic features and can affect the nervous system, result in neuropathological outcomes, in the high-risk groups (Yachou et al. 2020). The MERS-CoV and the SARS-CoV can cause CNS symptoms such as neuroinvasive capabilities, Also the neurological manifestations have been seen in the SARS-CoV-2 infected patients (Yachou et al. 2020). HCoV-OC43 also is a coronavirus that can invade the CNS (Zubair et al. 2020). Seizures and encephalitis are common symptoms in the reviewed viruses except HCoV-OC43. Encephalitis, confusion, fatigue, headache, ataxia, dizziness, anosmia and Guillain–Barré syndrome are mental symptoms of COVID-19 that are common symptoms in above viruses.
One of the frequent complications of COVID-19 patient with neurologic disorders was cerebrovascular problems among which acute ischemic cerebrovascular accident was the most frequent. A rare neurologic complication caused by cytokine storm and damage to the blood-brain barrier is acute necrotizing encephalopathy (ANE), It is more common associated with influenza but SARS-CoV-2 has also been associated with this condition (Reshef et al. 2014; Rossi 2008). Guillain-Barré Syndrome (GBS) is another complication of SARS-CoV-2 that reported in 5 cases in Italy and 2 cases from Wuhan, China (Toscano et al. 2020; Zhao et al. 2020). Hemophagocytic lymphohistiocytosis (HLH) is the next complication which often observes in hematologic malignancy, immunosuppression, or critical infections but has also been described in patients with SARS-CoV-2 (Al-Samkari and Berliner 2018).
Many drugs used for COVID-19 patients treating such as antivirals (e.g., remdesivir, ribavirin, lopinavir/ritonavir, favipiravir), biologic agents (tocilizumab) and antimalarials (hydroxychloroquine, chloroquine). Remdesivir is a nucleotide-analog inhibitor of RNA polymerases and neurologic effects and medication interactions of it is unknown (Wang et al. 2020). Ribavirin is RNA and DNA virus replication inhibitor and interferon alpha have neuropathic and neuropsychiatric sequelae (Fried and Russo 2003; Sleijfer et al. 2005). Chloroquine and hydroxychloroquine are endosomal/organelle pH modifications which correlate with neuropsychiatric side effects, ataxia and seizures. Methylprednisolone have an inflammation reduction mechanism and correlates with delirium (Bridwell et al. 2020). Ribavirine also used for SARS-CoV in patients with seizure, anosmia, myalgia and GTC (Hung et al. 2003; Hwang 2006; Lau et al. 2004). Intravenous immunoglobulin (IVIg), methylpred nisolone, ribavarin, convalescent serum and low molecular weight heparin (LMWH) use for loss of consciousness treating in SARS-CoV patients (Umapathi et al. 2004). Tazocine use in MERS-CoV patient for headache, dizziness, and intracerebral hemorrhage treating (Algahtani et al. 2016). Ribavirine and methylprednisolone also usage in therapy of intracerebral hemorrhage in MERS-CoV patients (Al-Hameed 2017). Interferon α2a, Ribavirin, and Lopinavir/Ritonavir use in Guillain–Barré syndrome, acute sensory neuropathy, headache, Confusion and seizure treating of MERS-CoV (Kim et al. 2017).
Our results indicated that COVID-19 could represent neurological and mental manifestations. The current study result shows that, headache and dizziness are the most prevalence neurological symptoms in adults in Netherlands and Europe (Lechien et al. 2020; Tostmann et al. 2020). By the assessment of all included studies, the Hypsomia, Anosmia or olfactory dysfunction was most frequent symptom. Other symptoms were headache or dizziness, dysgeusia or ageusia, dysphonia, fatigue, cerebrovascular disease, delirium and sleep disorder or drwosiness. Also, other less frequent symptoms were includes depression, anxiety, and confusion, auditory dysfunction and numbness or paresthesia. The headache as one of the most common neurologic symptoms in COVID-19 was reported earlier. Also, it’s reported that headache can be found in the early stages of the disease (Pinzon et al. 2020). In another studies headache was the most prevalence after myalgia (Nepal et al. 2020; Wang et al. 2020). Neurologic symptoms in SARS-CoV and MERS-CoV are included anxiety, depressed mood, insomnia and exacerbation of a panic disorder. According to studies delirium is common in SARS and MERS (Rogers et al. 2020). All of the mentioned highlights the importance of further investigations for the assessment of neurologic, CNS and mental symptoms in COVID-19 patients.
Our study had some strong points. We do an extensive search in different data-base to achieve of a large number of articles. Also this was the first study that used a meta-regression analysis to identify the determinants of heterogeneity that is novelty of our study. However, our study had some weakness. The major limitation of the current study was the limited number in primary studies in this particular field. Also most of the studies that included in this review came from low to moderate quality studies because have low sample size and case series design.
Our findings demonstrate that COVID-19 has a certain relation with neurological symptoms. the hypsomia, anosmia or olfactory dysfunction was most frequent symptom. Other symptoms were headache or dizziness, dysgeusia or ageusia, dysphonia, fatigue, cerebrovascular disease, delirium, and sleep disorder or drowsiness. Oder less frequent symptoms were includes depression, anxiety, and confusion, auditory dysfunction and numbness or paresthesia. Moreover, knowledge about mental disorders in COVID-19 patients in different ages and region is a step forwards to better knowledge for the COVID-19 disease progression and treating patients.
Funding source: Tehran University of Medical Sciences
Award Identifier / Grant number: 50001
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: The authors are grateful to the Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran. (Grant no: 50001).
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
Abiodun, O. and Ola, M. (2020). Role of brain renin angiotensin system in neurodegeneration: an update. Saudi J. Biol. Sci. 27: 905–912.10.1016/j.sjbs.2020.01.026Search in Google Scholar PubMed PubMed Central
Aggarwal, S., Garcia-Telles, N., Aggarwal, G., Lavie, C., Lippi, G., and Henry, B.M. (2020). Clinical features, laboratory characteristics, and outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19): early report from the United States. Diagnosis 7: 91–96.10.1515/dx-2020-0046Search in Google Scholar PubMed
Al-Hameed, F. (2017). Spontaneous intracranial hemorrhage in a patient with Middle East respiratory syndrome corona virus. Saudi Med. J. 38: 196–200.10.15537/smj.2017.2.16255Search in Google Scholar PubMed PubMed Central
Al-Obaidi, M., Bahadoran, A., Wang, S., Manikam, R., Raju, C., and Sekaran, S. (2018). Disruption of the blood brain barrier is vital property of neurotropic viral infection of the central nervous system. Acta Virol. 62: 16–27.10.4149/av_2018_102Search in Google Scholar PubMed
Algahtani, H., Subahi, A., and Shirah, B. (2016). Neurological complications of Middle East respiratory syndrome coronavirus: a report of two cases and review of the literature. Case. Rep. Neurol. Med. 2016: 3502683.10.1155/2016/3502683Search in Google Scholar PubMed PubMed Central
Antonucci, R., Chiappe, S., Porcella, A., Rosatelli, D., and Fanos, V. (2010). Bronchiolitis-associated encephalopathy in critically-ill infants: an underestimated complication? J. Matern. Fetal Neonatal Med. 23: 431–436.10.3109/14767050903184181Search in Google Scholar
Bhat, R., Hamid, A., Kuni, J., Saboo, S.S., Batra, K., Baruah, D., and Bhat, A. (2020). Chest imaging in patients hospitalized with COVID-19 infection – a case series. Curr. Probl. Diagn. Radiol. 49: 294–301.10.1067/j.cpradiol.2020.04.001Search in Google Scholar PubMed PubMed Central
Bianchetti, A., Rozzini, R., Guerini, F., Boffelli, S., Ranieri, P., Minelli, G., Bianchetti, L., and Trabucchi, M. (2020). Clinical presentation of COVID-19 in dementia patients. J. Nutr. Health Aging 24: 560–562.10.1007/s12603-020-1389-1Search in Google Scholar PubMed PubMed Central
Bohmwald, K., Galvez, N., Ríos, M., and Kalergis, A. (2018). Neurologic alterations due to respiratory virus infections. Front. Cell. Neurosci. 12: 386.10.3389/fncel.2018.00386Search in Google Scholar PubMed PubMed Central
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Ha, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al.. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395: 507–513.10.1016/S0140-6736(20)30211-7Search in Google Scholar
Chen, Q., Liu, Y., Lu, A., Ni, K., Xiang, Z., Wen, K., and Tu, W. (2017). Influenza virus infection exacerbates experimental autoimmune encephalomyelitis disease by promoting type IT cells infiltration into central nervous system. J. Autoimmun. 77: 1–10.10.1016/j.jaut.2016.10.006Search in Google Scholar
Chen, Q., Zheng, Z., Zhang, C., Zhang, X., Wu, H., Wang, J., Wang, S., and Zheng, C. (2020). Clinical characteristics of 145 patients with corona virus disease 2019 (COVID-19) in Taizhou, Zhejiang, China. Infection 48: 1–9.10.1007/s15010-020-01432-5Search in Google Scholar
Desforges, M., Le Coupanec, A., Dubeau, P., Bourgouin, A., Lajoie, L., Dubé, M., and Talbot, P. (2020). Human coronaviruses and other respiratory viruses: underestimated opportunistic pathogens of the central nervous system? Viruses 12: 1–28.10.3390/v12010014Search in Google Scholar
Edwards, S., Zvartau, M., Clarke, H., Irving, W., and Blumhardt, L. (1998). Clinical relapses and disease activity on magnetic resonance imaging associated with viral upper respiratory tract infections in multiple sclerosis. J. Neurol. Neurosurg. Psychiatr. 64: 736–741.10.1136/jnnp.64.6.736Search in Google Scholar
Ellul, M., Benjamin, L., Singh, B., Lant, S., Michael, B., Kneen, R., Defres, S., Sejvar, J., and Solomon, T. (2020). Neurological associations of COVID-19. Lancet Neurol. 19: 767–783.10.1016/S1474-4422(20)30221-0Search in Google Scholar
Fernández, I., Polo, M., Muñoz-Almagro, C., Carretero, L., Ureña, S., Muñoz, A., Roura, R., and Dueñas, B. (2012). Human metapneumovirus in the cerebrospinal fluid of a patient with acute encephalitis. Arch. Neurol. 69: 649–652.10.1001/archneurol.2011.1094Search in Google Scholar
Gelardi, M., Trecca, E., Cassano, M., and Ciprandi, G. (2020). Smell and taste dysfunction during the COVID-19 outbreak: a preliminary report. Acta Biomed. 91: 230–231.Search in Google Scholar
Grifoni, A., Sidney, J., Zhang, Y., Scheuermann, R.H., Peters, B., and Sette, A. (2020). A sequence homology and bioinformatic approach can predict candidate targets for immune responses to SARS-CoV-2. Cell Host Microbe 27: 671–680.e672.10.1016/j.chom.2020.03.002Search in Google Scholar
Halfhide, C.P., Flanagan, B.F., Brearey, S.P., Hunt, J.A., Fonceca, A.M., McNamara, P.S., Howarth, D., Edwards, S., and Smyth, R.L. (2011). Respiratory syncytial virus binds and undergoes transcription in neutrophils from the blood and airways of infants with severe bronchiolitis. J. Infect. Dis. 204: 451–458.10.1093/infdis/jir280Search in Google Scholar
Hallajzadeh, J., Khoramdad, M., Izadi, N., Karamzad, N., Almasi-Hashiani, A., Ayubi, E., Qorbani, M., Pakzad, R., Hasanzadeh, A., and Sullman, M.J. (2018). Metabolic syndrome and its components in premenopausal and postmenopausal women: a comprehensive systematic review and meta-analysis on observational studies. Menopause 25: 1155–1164.10.1097/GME.0000000000001136Search in Google Scholar
Hashemi, H., Pakzad, R., Heydarian, S., Yekta, A., Aghamirsalim, M., Shokrollahzadeh, F., Khoshhal, F., Pakbin, M., Ramin, S., and Khabazkhoob, M. (2019). Global and regional prevalence of strabismus: a comprehensive systematic review and meta-analysis. Strabismus 27: 54–65.10.1080/09273972.2019.1604773Search in Google Scholar
Hashemi, H., Pakzad, R., Yekta, A., Aghamirsalim, M., Pakbin, M., Ramin, S., and Khabazkhoob, M. (2020). Global and regional prevalence of age-related cataract: a comprehensive systematic review and meta-analysis. Eye 34: 1357–1370.10.1038/s41433-020-0806-3Search in Google Scholar
Hashemi, H., Pakzad, R., Yekta, A., Bostamzad, P., Aghamirsalim, M., Sardari, S., Valadkhan, M., Pakbin, M., Heydarian, S., and Khabazkhoob, M. (2018). Global and regional estimates of prevalence of amblyopia: a systematic review and meta-analysis. Strabismus 26: 168–183.10.1080/09273972.2018.1500618Search in Google Scholar
Heidari, F., Karimi, E., Firouzifar, M., Khamushian, P., Ansari, R., Mohammadi Ardehali, M., and Heidari, F. (2020). Anosmia as a prominent symptom of COVID-19 infection. Rhinology 58: 302–303.10.4193/Rhin20.140Search in Google Scholar
Hoffmann, M., Kleine-Weber, H., Schroeder, S., Krüger, N., Herrler, T., Erichsen, S., Schiergens, T.S., Herrler, G., Wu, N.H., Nitsche, A., and Ma, M. (2020). SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181: 271–280.e278.10.1016/j.cell.2020.02.052Search in Google Scholar
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al.. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395: 497–506.10.1016/S0140-6736(20)30183-5Search in Google Scholar
Hung, E.C., Chim, S.S., Chan, P.K., Tong, Y.K., Ng, E.K., Chiu, R.W., Leung, C.-B., Sung, J.J., Tam, J.S., and Lo, Y.D. (2003). Detection of SARS coronavirus RNA in the cerebrospinal fluid of a patient with severe acute respiratory syndrome. Clin. Chem. 49: 2108–2109.10.1373/clinchem.2003.025437Search in Google Scholar PubMed PubMed Central
Hung, I.F., Lung, K.C., Tso, E.Y., Liu, R., Chung, T.W., Chu, M.Y., Ng, Y.Y., Lo, J., Chan, J., Tam, A.R., et al.. (2020). Triple combination of interferon β-1b, lopinavir-ritonavir, and ribavirin in the treatment of patients admitted to hospital with COVID-19: an open-label, randomised, phase 2 trial. Lancet 395: 1695–1704.10.1016/S0140-6736(20)31042-4Search in Google Scholar
Hwang, C.S. (2006). Olfactory neuropathy in severe acute respiratory syndrome: report of a case. Acta Neurol. Taiwan 15: 26–28.Search in Google Scholar
Imamura, T., Suzuki, A., Lupisan, S., Kamigaki, T., Okamoto, M., Roy, C.N., Olveda, R., and Oshitani, H. (2014). Detection of enterovirus 68 in serum from pediatric patients with pneumonia and their clinical outcomes. Influenza Other Respir Viruses 8: 21–24.10.1111/irv.12206Search in Google Scholar PubMed PubMed Central
Jang, H., Boltz, D., Sturm-Ramirez, K., Shepherd, K.R., Jiao, Y., Webster, R., and Smeyne, R.J. (2009). Highly pathogenic H5N1 influenza virus can enter the central nervous system and induce neuroinflammation and neurodegeneration. Proc. Natl. Acad. Sci. U.S.A. 106: 14063–14068.10.1073/pnas.0900096106Search in Google Scholar PubMed PubMed Central
Kim, J.E., Heo, J.H., Kim, H.O., Song, S.H., Park, S.S., Park, T.H., Ahn, J.Y., Kim, M.K., and Choi, J.P. (2017). Neurological complications during treatment of Middle East respiratory syndrome. J. Clin. Neurol. 13: 227–233.10.3988/jcn.2017.13.3.227Search in Google Scholar PubMed PubMed Central
Lau, K.K., Yu, W.C., Chu, C.M., Lau, S.T., Sheng, B., and Yuen, K.Y. (2004). Possible central nervous system infection by SARS coronavirus. Emerg. Infect. Dis. 10: 342–344.10.3201/eid1002.030638Search in Google Scholar PubMed PubMed Central
Lechien, J.R., Chiesa-Estomba, C.M., Place, S., Van Laethem, Y., Cabaraux, P., Mat, Q., Huet, K., Plzak, J., Horoi, M., Hans, S., et al.. (2020). Clinical and epidemiological characteristics of 1,420 European patients with mild-to-moderate coronavirus disease 2019. J. Intern. Med. 288: 335–344.10.1111/joim.13089Search in Google Scholar PubMed PubMed Central
Li, Y.C., Bai, W.Z., and Hashikawa, T. (2020). The neuroinvasive potential of SARS‐CoV2 may play a role in the respiratory failure of COVID‐19 patients. J. Med. Virol. 92: 552–555.10.1002/jmv.25728Search in Google Scholar PubMed PubMed Central
Liberati, A., Altman, D.G., Tetzlaff, J., Mulrow, C., Gøtzsche, P.C., Ioannidis, J.P., Clarke, M., Devereaux, P.J., Kleijnen, J., and Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J. Clin. Epidemiol. 62: e1–e34.10.1016/j.jclinepi.2009.06.006Search in Google Scholar PubMed
Liguori, C., Pierantozzi, M., Spanetta, M., Sarmati, L., Cesta, N., Iannetta, M., Ora, J., Genga Mina, G., Puxeddu, E., Balbi, O., et al.. (2020). Subjective neurological symptoms frequently occur in patients with SARS-CoV2 infection. Brain Behav. Immun. 88: 11–16.10.1016/j.bbi.2020.05.037Search in Google Scholar PubMed PubMed Central
Lovell, N., Maddocks, M., Etkind, S.N., Taylor, K., Carey, I., Vora, V., Marsh, L., Higginson, I.J., Prentice, W., Edmonds, P., and Sleeman, K.E. (2020). Characteristics, symptom management, and outcomes of 101 patients with COVID-19 referred for hospital palliative care. J Pain Symptom 60: e77–e81.10.1016/j.jpainsymman.2020.04.015Search in Google Scholar
Mao, L., Jin, H., Wang, M., Hu, Y., Chen, S., He, Q., Chang, J., Hong, C., Zhou, Y., and Wang, D. (2020). Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China. JAMA Neurol. 77: 683–690.10.1001/jamaneurol.2020.1127Search in Google Scholar
Nair, H., Nokes, D.J., Gessner, B.D., Dherani, M., Madhi, S.A., Singleton, R.J., O’Brien, K.L., Roca, A., Wright, P.F., and Bruce, N. (2010). Global burden of acute lower respiratory infections due to respiratory syncytial virus in young children: a systematic review and meta-analysis. Lancet 375: 1545–1555.10.1016/S0140-6736(10)60206-1Search in Google Scholar
Nepal, G., Rehrig, J.H., Shrestha, G.S., Shing, Y.K., Yadav, J.K., Ojha, R., Pokhrel, G., Tu, Z.L., and Huang, D.Y. (2020). Neurological manifestations of COVID-19: a systematic review. Crit. Care 24: 1–11.10.1186/s13054-020-03121-zSearch in Google Scholar PubMed PubMed Central
Pakzad, R., Pakzad, I., Safiri, S., Shirzadi, M.R., Mohammadpour, M., Behroozi, A., Sullman, M.J., and Janati, A. (2018). Spatiotemporal analysis of brucellosis incidence in Iran from 2011 to 2014 using GIS. Int. J. Infect. Dis. 67: 129–136.10.1016/j.ijid.2017.10.017Search in Google Scholar PubMed
Picone, S., Mondì, V., Di Palma, F., Martini, L., and Paolillo, P. (2019). Neonatal encephalopathy and SIADH during RSV infection. Am. J. Perinatol. 36: S106–S109.10.1055/s-0039-1692132Search in Google Scholar PubMed
Pinzon, R.T., Wijaya, V.O., Buana, R.B., Al Jody, A., and Nunsio, P.N. (2020). Neurologic characteristics in coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis. Front. Neurol. 11: 565.10.3389/fneur.2020.00565Search in Google Scholar PubMed PubMed Central
Popescu, C.P., Florescu, S.A., Lupulescu, E., Zaharia, M., Tardei, G., Lazar, M., Ceausu, E., and Ruta, S.M. (2017). Neurologic complications of influenza B virus infection in adults, Romania. Emerg. Infect. Dis. 23: 574–581.10.3201/eid2304.161317Search in Google Scholar PubMed PubMed Central
Ren, L.-L., Wang, Y.M., Wu, Z.Q., Xiang, Z.C., Guo, L., Xu, T., Jiang, Y.Z., Xiong, Y., Li, Y.J., and Li, X.W. (2020). Identification of a novel coronavirus causing severe pneumonia in human: a descriptive study. Chin. Med. J. 133: 1015–1024.10.1097/CM9.0000000000000722Search in Google Scholar PubMed PubMed Central
Reshef, R., Kreisel, T., Kay, D.B., and Yirmiya, R. (2014). Microglia and their CX3CR1 signaling are involved in hippocampal-but not olfactory bulb-related memory and neurogenesis. Brain Behav. Immun. 41: 239–250.10.1016/j.bbi.2014.04.009Search in Google Scholar
Rogers, J.P., Chesney, E., Oliver, D., Pollak, T.A., McGuire, P., Fusar-Poli, P., Zandi, M.S., Lewis, G., and David, A.S. (2020). Psychiatric and neuropsychiatric presentations associated with severe coronavirus infections: a systematic review and meta-analysis with comparison to the COVID-19 pandemic. Lancet Psychiatr. 7: 611–627.10.1016/S2215-0366(20)30203-0Search in Google Scholar
Sivadon-Tardy, V., Orlikowski, D., Porcher, R., Sharshar, T., Durand, M.-C., Enouf, V., Rozenberg, F., Caudie, C., Annane, D., and Van Der Werf, S. (2009). Guillain-Barré syndrome and influenza virus infection. Clin. Infect. Dis. 48: 48–56.10.1086/594124Search in Google Scholar PubMed
Soltani, S. (2020). The hemagglutinin-esterase gene in human coronaviruses SARS-CoV-2, HKU1 and OC43. Eur. Rev. Med. Pharmacol. Sci. 24: 6484–6485.Search in Google Scholar
Soung, A. and Klein, R.S. (2018). Viral encephalitis and neurologic diseases: focus on astrocytes. Trends Mol. Med. 24: 950–962.10.1016/j.molmed.2018.09.001Search in Google Scholar PubMed PubMed Central
Speth, M.M., Singer-Cornelius, T., Obere, M., Gengler, I., Brockmeier, S.J., and Sedaghat, A.R. (2020). Olfactory dysfunction and sinonasal symptomatology in COVID-19: prevalence, severity, timing, and associated characteristics. Otolaryngol. Head Neck Surg. 163: 114–120, https://doi.org/10.1177/0194599820929185.Search in Google Scholar
Toscano, G., Palmerini, F., Ravaglia, S., Ruiz, L., Invernizzi, P., Cuzzoni, M.G., Franciotta, D., Baldanti, F., Daturi, R., and Postorino, P. (2020). Guillain–Barré syndrome associated with SARS-CoV-2. N. Engl. J. Med. 382: 2574–2576.10.1056/NEJMc2009191Search in Google Scholar PubMed PubMed Central
Tostmann, A., Bradley, J., Bousema, T., Yiek, W.K., Holwerda, M., Bleeker-Rovers, C., Ten Oever, J., Meijer, C., Rahamat-Langendoen, J., Hopman, J., et al.. (2020). Strong associations and moderate predictive value of early symptoms for SARS-CoV-2 test positivity among healthcare workers, The Netherlands, March 2020. Euro Surveill. 25: 2000508.10.2807/1560-7917.ES.2020.25.16.2000508Search in Google Scholar PubMed PubMed Central
Tsivgoulis, G., Palaiodimou, L., Katsanos, A.H., Caso, V., Köhrmann, M., Molina, C., Cordonnier, C., Fischer, U., Kelly, P., and Sharma, V.K. (2020). Neurological manifestations and implications of COVID-19 pandemic. Ther. Adv. Neurol. Disord. 13: 1–14, https://doi.org/10.1177/1756286420932036.Search in Google Scholar
Umapathi, T., Kor, A.C., Venketasubramanian, N., Lim, C.T., Pang, B.C., Yeo, T.T., Lee, C.C., Lim, P.L., Ponnudurai, K., and Chuah, K.L. (2004). Large artery ischaemic stroke in severe acute respiratory syndrome (SARS). J. Neurol. 251: 1227–1231.10.1007/s00415-004-0519-8Search in Google Scholar
Walker, P.J., Siddell, S.G., Lefkowitz, E.J., Mushegian, A.R., Dempsey, D.M., Dutilh, B.E., Harrach, B., Harrison, R.L., Hendrickson, R.C., and Junglen, S. (2019). Changes to virus taxonomy and the international code of virus classification and nomenclature ratified by the international committee on taxonomy of viruses (2019). Arch. Virol. 164: 2417–2429.10.1007/s00705-019-04306-wSearch in Google Scholar
Wang, M., Guo, L., Chen, Q., Xia, G., and Wang, B. (2020). Typical radiological progression and clinical features of patients with coronavirus disease 2019. Aging 12: 7652–7659.10.18632/aging.103170Search in Google Scholar
Wang, W., Tang, J., and Wei, F. (2020). Updated understanding of the outbreak of 2019 novel coronavirus (2019‐nCoV) in Wuhan, China. J. Med. Virol. 92: 441–447.10.1002/jmv.25689Search in Google Scholar
Wang, Y., Zhang, D., Du, G., Du, R., Zhao, J., Jin, Y., Fu, S., Gao, L., Cheng, Z., Lu, Q., and Hu, Y. (2020). Remdesivir in adults with severe COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial. Lancet 395: 1569–1578.10.1016/S0140-6736(20)31022-9Search in Google Scholar
Wells, G.A., Shea, B., O’Connell, D., Peterson, J., Welch, V., Losos, M., and Tugwell, P. (1999). The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Clinical Epidemiology Unit, University of Ottawa, Ottawa.Search in Google Scholar
Wiersinga, W.J., Rhodes, A., Cheng, A.C., Peacock, S.J., and Prescott, H.C. (2020). Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. J. Am. Med. Assoc. 324: 782–793.10.1001/jama.2020.12839Search in Google Scholar PubMed
World Health Organization. (2020). Coronavirus disease 2019 (COVID-19). World Health Organization, Available at: www.who.int/docs/default-source/coronaviruse/weekly-updates (Accessed 9 September 2020).Search in Google Scholar
Wright, E.J., Brew, B.J., and Wesselingh, S.L. (2008). Pathogenesis and diagnosis of viral infections of the nervous system. Neurol. Clin. 26: 617–633.10.1016/j.ncl.2008.03.006Search in Google Scholar PubMed
Xu, H., Yasui, O., Tsuruoka, H., Kuroda, K., Hayashi, K., Yamada, A., Ishizaki, T., Yamada, Y., Watanabe, T., and Hosaka, Y. (1998). Isolation of type B influenza virus from the blood of children. Clin. Infect. Dis. 27: 654–655.10.1086/517146Search in Google Scholar PubMed
Yachou, Y., El Idrissi, A., Belapasov, V., and Benali, S.A. (2020). Neuroinvasion, neurotropic, and neuroinflammatory events of SARS-CoV-2: understanding the neurological manifestations in COVID-19 patients. Neurol. Sci. 41: 1–3.10.1007/s10072-020-04575-3Search in Google Scholar PubMed PubMed Central
Zeng, H., Quinet, S., Huang, W., Gan, Y., Han, C., He, Y., and Wang, Y. (2013). Clinical and MRI features of neurological complications after influenza A (H1N1) infection in critically ill children. Pediatr. Radiol. 43: 1182–1189.10.1007/s00247-013-2682-5Search in Google Scholar
Zhao, H., Shen, D., Zhou, H., Liu, J., and Chen, S. (2020). Guillain-Barré syndrome associated with SARS-CoV-2 infection: causality or coincidence? Lancet Neurol. 19: 383–384.10.1016/S1474-4422(20)30109-5Search in Google Scholar
Zubair, A.S., McAlpine, L.S., Gardin, T., Farhadian, S., Kuruvilla, D.E., and Spudich, S. (2020). Neuropathogenesis and neurologic manifestations of the coronaviruses in the age of coronavirus disease 2019: a review. JAMA Neurol. 77: 1018–1027.10.1001/jamaneurol.2020.2065Search in Google Scholar PubMed PubMed Central
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