Cognitive profile, neuroimaging and fluid biomarkers in post-acute COVID-19 syndrome

Cognitive profile, neuroimaging and fluid biomarkers in post-acute COVID-19 syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cognitive profile, neuroimaging and fluid biomarkers in post-acute COVID-19 syndrome Núria Guillén, Agnès Pérez-Millan, Neus Falgàs, Gema M Lledó-Ibáñez, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3621297/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract We aimed to characterize the cognitive profile of post-acute COVID-19 syndrome (PACS) patients with cognitive complaints, exploring the influence of biological and psychological factors. Participants with confirmed SARS-CoV-2 infection and cognitive complaints ≥ eight weeks post-acute phase were included. A comprehensive neuropsychological battery (NPS) and health questionnaires were administered at inclusion and at 1, 3 and 6 months. Blood samples were collected at each visit, MRI scan at baseline and at 6 months, and, optionally, cerebrospinal fluid. Cognitive features were analyzed in relation to clinical, neuroimaging, and biochemical markers at inclusion and follow-up. Forty-nine participants, with a mean time from symptom onset of 10.4 months, showed attention-executive function (69%) and verbal memory (39%) impairment. Apathy (64%), moderate-severe anxiety (57%), and severe fatigue (35%) were prevalent. Visual memory (8%) correlated with total gray matter (GM) and subcortical GM volume. Neuronal damage and inflammation markers were within normal limits. Over time, cognitive test scores, depression, apathy, anxiety scores, MRI indexes, and fluid biomarkers remained stable, although fewer participants (50% vs. 75.5%; p = 0.012) exhibited abnormal cognitive evaluations at follow-up. Altered attention/executive and verbal memory, common in PACS, persisted in most subjects without association with structural abnormalities, elevated cytokines, or neuronal damage markers. Biological sciences/Neuroscience/Cognitive neuroscience Biological sciences/Neuroscience/Neuroimmunology post-acute COVID-19 cognitive symptoms MRI cytokines longitudinal study Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Post-acute COVID-19 syndrome (PACS) is defined by the continuation or development of new symptoms 3 months after the initial SARS-CoV-2 (COVID-19) infection, lasting for at least 2 months with no other explanation. PACS might affect anyone exposed to COVID-19, regardless of the severity of the acute symptoms or the premorbid condition. Still, non-hospitalized patients with mild, acute illness, between ages of 36 and 50 years, as this population represents most COVID-19 cases. There also is a predominance of woman over men 1 . During the acute phase of COVID-19 infection, a wide variety of neurological complications have been described, including: headache, anosmia, dysgeusia, dizziness, agitation, confusion, impaired level of consciousness, or acute stroke 2 . A recent meta-analysis evaluating the prevalence of persistent symptoms at 12 weeks or more after the acute COVID-19 infection reported that up to 22% of the subjects presented subjective or objective cognitive impairment 3 . In patients with PACS, headache and cognitive complaints are the most frequent neurological symptoms, but a long list of other neurological symptoms has been described. Few studies analyzed cognitive impairment in PACS using standard cognitive batteries; in those cases, cognitive impairment was characterized by altered attention, executive function, and memory 4–8 . Characterization of the long-term cognitive impact in PACS patients have yet to be determined, however, this is an important area for research as long-term cognitive complaints are associated with increased anxiety and depression 9 , and decreased quality of life 10 . PACS usually presents with various other symptoms, such as fatigue, dyspnea, joint or chest pain. A study showed that fatigue was the most frequent symptom in the acute and follow-up phases of COVID-19 11 . A study of non-hospitalized post-COVID-19 patients with complex residual symptoms revealed that one-third of patients had not returned to work within 22 months after the infection despite efforts to recover function through exercise, respiratory, olfactory rehabilitation, cognition/speech therapy and/or psychological support 12 . Several hypotheses exist for the pathogenesis of PACS, including: persisting reservoirs of SARS-CoV-2 in tissues, immune dysregulation, autoimmunity, or damage to the microvasculature 1,13 . Some studies have found not only elevated levels of cytokines but also neuronal damage markers in patients with acute COVID-19 infection and neurological symptoms 14 , and an association of elevated cytokines or neuronal damage markers with post-acute sequelae of COVID-19 14–16 . Moreover, neuroimaging studies in PACS have reported mixed results: whereas some studies describe a reduction in cortical thickness (CTh), gray matter (GM) volume, or cerebral blood flow compared to controls in cross-sectional analyses, 6,17,18 others found an increase in GM volumes in some brain regions such as hippocampus and insula 19,20 . Also, white matter hyperintensities have also been observed 21 . It is unclear how together these may relate with the cognitive impact seen in PACS patients. In this study, we first aimed to characterize the cognitive profile of patients with cognitive complaints in PACS and their recovery over 6 months. Secondly, we aimed to evaluate several markers of neuronal damage (structural MRI and fluid markers) and inflammation to study cross-sectional and longitudinal associations with clinical and cognitive features. 2. MATERIALS AND METHODS 2.1. Participants We performed a prospective evaluation of patients referred to the Neurology Service at the Hospital Clínic de Barcelona, Barcelona, Spain, following cognitive complaints after SARS-CoV2 infection. Participants were consecutively recruited between March 2021 and November 2021. Inclusion criteria were: 1) COVID-19 diagnosis, based on biological diagnosis or medical report; 2) Cognitive symptoms reported by the participant or an observer (family member, co-worker, health professional) 3) presence of cognitive symptoms ≥ 8 weeks after COVID-19 symptoms onset; 4) fluent in Spanish; 5) at least 6 years of formal education; 6) age 35–65 years old (participants above the age of 65 years were not included in order to avoid a possible confusion factor with onset of neurodegenerative diseases symptoms). Exclusion criteria were: 1) Previous diagnosis of any neurological, psychiatric, or medical condition that could affect the baseline cognitive performance, including previous chronic fatigue syndrome diagnosis; 2) any condition that prevented the completion of the cognitive assessment and/or MRI scanning. This study was performed according to the international consensus for research with human subjects (the updated version of Helsinki’s Statement, Fortaleza, 2013) and Spanish regulations. The Hospital Clínic de Barcelona Ethics Committee (HCB/2020/1483) approved the study, and all participants provided informed consent. 2.2. Clinical and neuropsychological assessment Participants were evaluated at baseline, 1 month, 3 months, and 6 months of follow-up. Participants underwent general and neurological assessments and a comprehensive neuropsychological (NPS) battery administered by a trained neuropsychologist. The battery included estimated premorbid IQ (Spanish Word Accentuation Test) 22 , verbal memory tests: Free and Cued Selective Reminding Test (FCSRT) 23 ; visual memory tests: Rey-Osterrieth Complex Figure Test (ROCF) Delayed Recall 24 ; language tests: Boston Naming Test 25 , Vocabulary, Semantic Fluency 26 ; visuospatial abilities: Rey Figure Copy; and attention and executive function tests: Trail Making Test (TMT) A and B 27 , Stroop Test 28 , Symbol Digits Modalities Test (SMDT) 29 , Digit Span Test 30 , Letter-Number sequencing 30 , Symbol Search 30 , and phonemic fluency 31 . The participants also completed the Beck Depression Inventory (BDI) 32,33 , the Beck Anxiety Inventory (BAI) 34 , the Starkstein Apathy Scale (SAS) 35 , the Subjective Cognitive Decline Questionnaire (SCD-Q) 36 , the Multidimensional Fatigue Inventory (MFI-20) 37 and the 36-Item Short Form Health Survey (SF-36) 38,39 . 2.3. Neuroimaging studies MRI scanning was performed at inclusion and the end of the study (6 months) using a 3T Prisma Siemens (Siemens Medical Systems, Germany). A high-resolution 3D structural dataset (T1-weighted, MP-RAGE, repetition time = 2.400ms, echo time = 2.22ms, 208 slices, field-of-view = 256 mm, 0.8 mm isotropic voxel) was acquired for everyone at each time. We used the processing stream available in FreeSurfer version 6.0 ( http://surfer.nmr.mgh.harvard.edu.sire.ub.edu/ ) to perform cortical reconstruction and volumetric segmentation of the T1-weighted acquisitions. FreeSurfer allowed us to obtain cortical thickness (CTh) maps and segment the subcortical structures. For longitudinal data, we used the longitudinal stream in FreeSurfer. All FreeSurfer preprocessing steps are reported in detail elsewhere 40–42 . From the reconstructed data, we obtained global measures of mean CTh and grey matter (GM) volumes of the left and right hemispheres. In addition, we used the summary measures of mean CTh in 68 cortical parcellations and GM volumes of 14 subcortical structures, all derived from atlases available in FreeSurfer 43,44 . All images and individual segmentations were visually inspected and manually corrected if needed. 2.4. Biological measures Blood samples were obtained at baseline (n = 49), at 1 month (n = 48), 3 months (n = 47), and 6 months (n = 46). An optional lumbar puncture to obtain cerebrospinal fluid was offered to the participants at the basal visit (n = 12). Serum levels of neurofilament-light (NfL) and glial fibrillary acidic protein (GFAP) were determined by single molecule array technology (Neurology 2-Plex B Simoa, Quanterix®). A panel of cytokines, chemokines and other soluble mediators that included interferon (IFN)-α, β, and γ, interleukins (IL) IL-1β, IL-6, IL-8, IL-10, IL-17A, IL-18, tumor necrosis factor (TNF)-α2, IL-1 receptor antagonist (IL-1ra), Interferon-γ-Inducible Protein 10 (IP-10), granulocyte colony-stimulating factor (G-CSF), antigen CD25, chemokine ligand 1 (CX3CL1 or fractalkine), chemokine ligand 2 (CCL2), chemokine ligand 7 (CCL7) and ligand 9 (CXCL9) was analyzed in serum and CSF by a multiplexed bead based assay (Human Cyto Panel A, Merck, Germany) in a Luminex®100/200 platform. In addition, a tissue-based assay (TBA) consisting of an indirect immunohistochemistry (IIHC) with rat brain tissue and an indirect immunofluorescence assay (IIFA) with live neurons were performed to screen anti-neuronal immunoreactivity 45,46 . The prospective study did not include cognitive normal non-PACS controls. As most of the biochemical measures evaluated here lack established cut-offs for normal values 47–49 , we included in the analysis 38 serum and 24 CSF samples from non-COVID-19 healthy controls from a previous study in acute COVID-19 14 . Normal results were defined as results within two standard deviations of the mean of the control group. 2.5. Statistical analysis Raw cognitive scores were converted to scalar scores (SS) according to age and number of formal education, with a normal distribution with a mean scalar score of 10. Abnormal cognitive performance was set at SS < 7, which is the cutoff point used in clinical practice. If one subtest or more showed abnormal scores, the evaluation was considered abnormal. We first evaluated if cognitive test results differed in participants who scored within the normal range versus the pathological range on measures of anxiety, depression, apathy, fatigue, or quality of life scores. For that, we used previously described cut-offs: BDI ≥ 20 indicated moderate or severe depression, BAI ≥ 16 defined moderate or severe anxiety, SAS ≥ 14 was considered clinically significant apathy, SCD-Q ≥ 7 was considered pathological, MFI-20 cutoff of ≥ 60 was used for the description of a high-level versus low-level fatigue. SF36 subscales cutoff of ≥ 50 indicated normative scores. We used permutation tests, adding age, sex, and years of education as covariates. Then, we studied the partial correlation between cognitive measures and physical and mental health scores with continuous variables and added age, sex, and years of education as covariables. We also analyzed the neuropsychological results with a principal component analysis (PCA), a dimensionality reduction method. By analyzing the first component and the individual contribution of each variable to it, we estimated which variables explained the highest variability in the data. We measured the partial correlation of global and regional MRI measures with memory and executive function outcomes, anxiety, depression, fatigue and subjective cognitive complaints (SCD), and added age, sex, and years of education as covariables. All analyses were corrected for multiple comparisons. We measured the partial correlation of inflammatory soluble mediators, NfL, and GFAP levels with SCD, memory and executive function outcomes, anxiety, depression, fatigue, and global and regional MRI measures and added age and sex as covariables. All analyses were corrected for multiple comparisons using the Benjamini-Hochberg adjustment. We performed longitudinal analyses including the baseline and the three follow-up visits with linear mixed-effects models to study changes between visits in cognitive measures, physical and mental health scores, global and regional MRI measures, and biochemical values for all the available data in each case. For cognitive tests, age, sex, and years of education were added as fixed effects. In the MRI and biochemical studies, age and sex were considered fixed effects. Statistical analyses of the cross-sectional and longitudinal results were carried out in the language R version 4.2.1 ( https://www.r-project.org ). 3. RESULTS 3.1. Clinical data and neuropsychological characteristics Fifty-three participants were assessed, 49 were included in the study and 46 completed the follow-up. 39 (80%) participants included were women, the mean age was 50.1 (SD 7.9, range 35–64), mean years of education (YOE) 14 (SD 3), with mean time from the onset of acute symptoms of 10.4 months (SD 3.9). 10 (20%) participants needed hospitalization; 7 (14%) received oxygen support, including 2 (4%) admitted into intensive care units. Participants presented with multiple symptoms other than cognitive complaints: 43 (88%) referred fatigue, 30 (61%) headaches, 31 (63%) dyspnea, 24 (49%) arthralgias, 21 (43%) myalgias, 19 (39%) bowel rhythm disturbances, 12 (24%) anosmia, 13 (27%) dysgeusia and 5 (10%) intermittent febrile. Twenty (41%) participants were on sick leave at inclusion. The sample had high premorbid intelligence, estimated with the Word Accentuation Test. Twelve participants (24.5%) showed a normal cognitive evaluation defined by all test results within the limits of normality for age and years of education. The remaining participants (75.5%) presented abnormal scores in at least 1 test. Abnormal results were most frequently observed in executive functions and verbal memory (Table 1 , Fig. 1 ). For this reason, further analysis with cognitive tests included only memory and executive functions tests. Table 1 Neuropsychological test results FCSRT, Free and Cued Selective Reminding Test; NPS, neuropsychological; SD, standard deviation; SMDT, Symbol Digit Modalities Test; VOSP, Visual Object and Space Perception Battery Cognitive test Baseline N = 49 Mean scalar score (SD); %with abnormal scores + 1 month N = 48 Mean scalar score (SD); %with abnormal scores + 3 months N = 47 Mean scalar score (SD); %with abnormal scores + 6 months N = 46 Mean scalar score (SD); % with abnormal scores MEMORY FCSRT Free learning 8.6 (3.4); 13 (27%) 11.4 (3.3); 4 (8%) 12.4 (3.3); 4 (9%) 12.7 (3.7); 3(7%) FCSRT Total learning 9.8(4.1); 11 (22%) 12.5 (4.7); 6 (12%) 13.1 (4.0); 2(4%) 14.2 (4.1); 4 (9%) FCSRT Delayed Free recall 8.2 (3.4); 13 (27%) 9.7 (3.5); 9 (19%) 11.4 (3.2); 3 (6%) 11.3 (3.0); 2 (4%) FCSRT Delayed total recall 10.2 (4.9); 8 (16%) 13.5 (5.1); 4 (8%) 13.2 (5.2); 5 (11%) 12.9 (4.9); 4 (9%) Rey figure Recall 8.8 (2.4); 4 (8%) 10.3 (3.0); 2 (4%) 11.0 (2.8); 3 (6%) 10.9(2.6); 0 (0%) LANGUAGE Boston naming test 12.2(3.3); 2 (4%) 12.7 (3.5); 2 (4%) 12.7 (3.3); 1 (2%) 13.5(3.6); 1 (2%) Semantic fluency test 9.8(2.8); 5 (10%) 10.1 (3.1); 3 (6%) 10.3 (3.2); 4 (9%) 10.7(2.8); 3 (7%) Vocabulary 11.3(2.2); 2 (4%) 11.3 (1.9); 1 (2%) 11.5 (2.3); 1(2%) 11.8(1.7); 1 (2%) VISUOESPATIAL ABILITIES Rey figure copy accuracy 12.1(3.8); 2(4%) 10.7 (2.9); 1 (2%) 12.9 (3.5); 1 (2%) 12.6(3.7); 1 (2%) Rey figure time 10.5(2.9); 5 (10%) 11.1 (3.3); 5 (10%) 11.9 (3.0); 2 (4%) 12.3 (3.2); 2 (4%) ATTENTION AND EXECUTIVE FUNCTIONS Trail Making Test - A 9.2(3.7); 9 (18%) 9.6 (3.9); 7(15%) 11.0 (3.8); 3 (6%) 11.3(4.1); 5 (11%) Trail Making Test - B 8.3(3.0); 13 (27%) 9.0 (3.4); 9 (19%) 10.3 (3.6); 6 (13%) 9.6 (3.6); 8 (17%) Phonemic fluency 9.9 (2.3); 4 (8%) 10.1 (2.4); 5 (10%) 10.5 (2.7); 4 (9%) 10.6 (2.6); 4 (9%) Digit Span - Forward 9.7(3.2); 7 (14%) 9.0 (3.0); 9 (19%) 9.5 (2.8); 6 (13%) 9.3 (3.1); 11 (24%) Letter-number sequencing 8.7(3.4); 13 (27%) 8.7 (3.1); 8 (17%) 8.4 (2.3); 8 (17%) 8.7 (2.7); 7 (15%) SDMT 8.3(2.8); 12 (24%) 8.9 (3.5); 10 (21%) 9.8 (3.3); 6 (13%) 9.8 (3.3); 6 (13%) Symbol search 10.2 (2.2); 3 (6%) 10.2 (2.4); 2 (4%) 10.9 (2.3); 1(2%) 10.9 (2.1); 1(2%) Stroop word 8.2(3.3); 14 (29%) 8.2 (2.8); 14 (29%) 8.7 (2.6); 10 (21%) 8.5 (2.7); 10 (22%) Stroop color 8.8 (3.3); 9 (18%) 8.6 (2.9); 10 (21%) 8.9 (2.8); 8 (17%) 8.3 (3.1); 12 (26%) Stroop word-color 8.8 (2.9); 12 (24%) 8.8 (3.1); 11 (23%) 9.0 (3.1); 9 (19%) 9.0 (2.6); 9 (20%) Nine (19%) participants presented moderate-severe depression (based on questionnaire results), 26 (57%) showed moderate-severe anxiety, and 29 (64%) showed clinically significant apathy. Regarding subjective cognitive complaints, reports of participants (MyCog) were altered in 48/49 (98%), and reports of proxies (TheirCog) were altered in 33/44 (75%) (Table 1 ). MFI-20 mean total score was 57.7, 60.0 is the limit for severe fatigue; 35.4% of the sample was in the severe fatigue group (Table 3 ). Compared to normative data, PACS participants had lower scores on each of the SF-36 domains. The most affected domain was “Energy/fatigue (mean score of 30, altered in 80% of participants), followed by role limitations due to physical health (mean score of 34, altered in 64%) (Table 4 ). Table 2 Subjective cognitive decline and psychiatric symptoms questionnaires SCD-Q, subjective cognitive decline questionnaire. Baseline Follow-up Beck Anxiety Inventory (0 to 63) N = 46 N = 39 Minimal (0 to 7) 4 (9%) 8 (21%) Mild (8 to 15) 16 (35%) 11 (28%) Moderate (16 to 25) 12 (26%) 10 (26%) Severe (26 to 63) 14 (30%) 10 (256%) Beck Depression Inventory (0 to 40) N = 46 N = 40 Minimal (0 to 13) 22 (48%) 20 (50%) Mild depressive symptoms (14 to 18) 15 (33%) 8 (20%) Moderate clinical depression (19 to 27) 6 (13%) 8 (20%) Severe depression (28 to 63) 3 (6%) 4 (10%) SCD-Q MyCog (0 to 24) N = 46 N = 41 Normal (0 to 6) 1 (2%) 4 (10%) Pathological (7 to 24) 45 (98%) 37 (90%) SCD-Q TheirCog (0 to 24) N = 45 N = 41 Normal (0 to 6) 12 (27%) 11 (27%) Pathological (7 to 24) 33 (73%) 30 (74%) Table 3 Multidimensional Fatigue Inventory (MFI-20) SD, standard deviation Multidimensional Fatigue Inventory (MFI-20) Baseline Mean score (SD) Follow-up Mean score (SD) MFI-20 Total Score (20 to 100) 57.7 (5.1) 61.3 (6.1) MFI-20 Mental Fatigue (4 to 20) 10.2 (2.6) 10.8 (3.0) MFI-20 General Fatigue (4 to 20) 11.3 (1.9) 12.2 (2.1) MFI-20 Physical Fatigue (4 to 20) 12.3 (2.0) 12.8 (2.1) MFI-20 Reduction of Activity (4 to 20) 12.9 (2.1) 12.9 (1.6) MFI-20 Reduction of Motivation (4 to 20) 11.7 (2.9) 13.0 (2.3) Table 4 36-Item Short Form Survey Instrument assessing health-related quality of life 36-Item Short Form Survey Instrument (SF36) Baseline Mean score (SD) / n(%) altered under 50 Follow-up Mean score (SD) / n(%) altered under 50 Physical Functioning (0 to 100) 64.3(23.3) / 9(20.0%) 65.5(27.3) / 13(32.5%) General Health 51.8(17.4) / 23 (51.1%) 48.88(23.5) / 23(57.5%) Role limitations due to physical health 33.9(44.0) / 29(64.4%) 31.25(41.12) / 26 (65.05) Pain 47.3(28.2) / 26 (57.8%) 46.1(27.4) / 23(57.5%) Emotional Well-being 56.2(18.0) / 16(35.6%) 49.9(19.7) / 21(52,5%) Social Functioning 55.8(26.4) / 13(28.9%) 52.3(26.3) / 17(42.5%) Role limitations due to emotional problems 40.9(44.3) / 27(61.4%) 44.9(44.4) / 22(55.0%) Energy/Fatigue 29.8(18.6) / 36(80%) 28.4(18.8) / 34(85%) SD, standard We next sought to stratify participants by levels of anxiety, depression, apathy, fatigue, or quality of life scores according to their questionnaire scores. Participants displaying moderate or severe anxiety showed lower results in the Rey Figure Recall subtest (adjusted p-value = 0.0014. No significant differences were observed in cognitive tests between participants with normal and abnormal values of the other stratification categories. To understand which neuropsychological domains were most affected in this PACS sample, a PCA was performed (cognitive and questionnaire scores). This revealed that executive function scores explained most of the variability of the cognitive results in the PACS sample population (Fig. 2 ). The three cognitive tests that explained most of the variability of the data were: Symbol Search, SDMT, and TMT-A. At 6 months follow-up, the cognitive test scores remained unchanged compared to baseline, with the exception of the Delay Free Recall test, with a mean score of 10.0 at baseline and a mean score of 12.4 after 6 months (0 = 0.0040) (the linear mixed-effects coefficients are shown in the Supplementary Material). Twenty-three participants (50%) had a normal cognitive evaluation at follow-up, whereas the remaining participants (50%) showed abnormal scores on at least one test. The number of participants with a normal NPS was higher at follow-up (50%) compared to baseline (24%) (p-value = 0.012). We did not find longitudinal differences in depression, anxiety, fatigue, or SCD scores at 6 months. 3.2. Correlations between neuroimaging and cognitive data To identify whether neuropsychological tests correlate with the neuroimaging data, partial correlations were performed. Rey Figure Recall scores showed a moderate correlation with total GM volume (r = 0.46, adjusted p-value = 0.046), subcortical GM volume (r = 0.52, adjusted p-value = 0.024), and left cerebral white matter (WM) (r = 0.47, adjusted p-value = 0.046. At the regional level, the ROCF recall scores also showed a significant correlation with GM volume in left hippocampus GM (r = 0.51, adjusted p-value = 0.042), right hippocampus GM (r = 0.49, adjusted p-value = 0.042), and right thalamus GM (r = 0.48, adjusted p-value = 0.042, Fig. 3 ). Other cognitive tests and clinical outcomes (SCD, anxiety, depression questionnaires) were not significantly associated with global or regional structural MRI indexes after correction for age, sex, years of education, and multiple comparisons. When probing for longitudinal changes, we did not identify significance in global structural MRI measures at 6 months in PACS participants compared with baseline. At the regional level, we found GM loss at 6 months in the GM volume of left pallidum (p-value = 0.0098) and left transverse temporal thickness (p-value = 0.018) (Fig. 4 ). 3.3. Biochemical analyses Finally, we assessed group serum levels of NfL, GFAP, and cytokines, as well as CSF levels of cytokines. We also sought to assess whether serum or CSF samples contained anti-neuronal immunoreactivity. We looked for differences in NfL, GFAP and cytokines levels between PACS and controls. Serum NfL and GFAP did not differ between groups. In serum, G-CSF, 1P10, MIG, and MCP1 levels were significantly lower in PACS. CSF, IL-18, and IL-17a were significantly higher in PACS, and IL10, IL1AR, IL-8, IP10, and MCP1 were significantly lower in PACS. However, the magnitude of the differences was small, and all values were within the limits of normality (Supplementary material). All the serum and CSF samples were negative for anti-neuronal immunoreactivity. At the longitudinal level, we did not find changes in cytokines, NFL, or GFAP levels in PACS participants, except for the IL17A, which presented a significant decrease between baseline and at 6 months (still within the normal levels). We found a moderate positive correlation between GFAP levels and the Stroop Word test scores (r = 0.48, adjusted p-value = 0.018). Global and regional MRI indexes and cognitive scores did not significantly correlate with serum or CSF cytokine or NFL levels after adjusting for multiple comparisons. 4. DISCUSSION In the present study, we evaluated PACS patients with subjective cognitive complaints and their evolution over a 6-month period. Our analysis revealed that thirty-seven (75.5%) participants presented with abnormal cognitive evaluations at baseline, with executive functions and memory as the most affected domains. Rey Figure Recall scores were the only cognitive abnormality identified to be associated with MRI indexes, showing a relationship with global and region GM and WM loss. Participants also reported high levels of depression, anxiety, apathy, and fatigue in addition to other frequent physical symptoms. Fluid biomarkers in serum and CSF, including neuronal damage and inflammatory markers, were within the normal limits yet included some statistically significant differences with controls. All test samples were negative for neuronal antibody reactivity. Finally, longitudinal analyses of cognitive symptoms, cognitive scores and mental health outcomes revealed that most of the initial symptoms of PACS did not improve with time, although 50% of participants showed normal cognition at evaluation at follow-up. Cognitive evaluations in PASC showed that attention-executive and verbal memory were the most affected domains (Fig. 1 ), which has been described in previous published works; however, the pattern of alterations was broader and more heterogeneous between patients 5–8,50,51 . The sample had a high premorbid intelligence and would not be expected to perform below average on cognitive testing. At a 6-month follow-up, we determined that only the Delayed Free Recall of verbal memory scores improved significantly from baseline. Nevertheless, if we consider the percentage of normal evaluations (defined as the proportion of tests within clinical limits of normality), there was a significant improvement with time. We cannot exclude that a learning effect could partially explain this result, as the same tests were administered four times in 6 months. Given that most of the participants with abnormal results were near the threshold for normal performance, only a small improvement in these tests would enable them to reach normal threshold values. Our results are in line with previous published works showing both improvements and persisting cognitive deficits in PACS 52,53 . Participants also reported depressive symptoms, anxiety, apathy, fatigue, and low scores in general health. These symptoms did not improve during this 6-month study (Table 2 ). Given that our analysis demonstrated a significant relationship between one memory test and stratification in anxiety scores, we believe that the coexistence of cognitive and mental health symptoms could not be interpreted as causality. While we cannot definitively exclude that comorbid mental health issues may impede cognitive symptom improvement, it is worth noting that these mental health issues could be a consequence of the cognitive impairment as described elsewhere 54 . In addition, both types of symptoms could be driven by fatigue, which was almost universal and severe in 35% of participants. Our next approach in this study was to correlate clinical and neuroimaging features of this PACS cohort longitudinally. While a previous study has included both cognitive and neuroimaging assessment of PACS 55 , to our knowledge, this is the first study to include longitudinal analysis of both cognitive and neuroimaging tests. We found significant correlations of both global and focal measures with visual memory but not with other cognitive tests. This correlation indicated that worse visual memory was associated with lower total and subcortical GM volume together with left cerebral WM volume. Furthermore, subcortical GM volumes, especially the hippocampus and thalamus, significantly corresponded with worse visual memory performance. Previous studies also explored the association between GM volume and cognitive symptoms; it has been reported that worse memory and visuospatial test performance is associated with a loss of GM volume 6,20 . In line with previous studies, our longitudinal analyses neither found improvement at a 6-month period, nor evidence of progressive volume loss. The majority of previous studies 13,14,16,56–59 , have reported high levels of plasma and/or CSF cytokines, NfL and GFAP in the acute or subacute phase of COVID-19 infection that normalize at follow-up, albeit using differing follow-up intervals 57,60,61 . Some of these studies related these biochemical changes with the severity of the infection or the gravity of neurological symptoms; however, there is no consensus on how fluid biomarkers relate to acute COVID-19 symptom severity, PASC symptoms, or PASC progression/resolution. In our study, the levels of plasma and CSF cytokines, NfL and GFAP were within pre-specified normal limits. We found some differences in cytokine levels between PACS and control participants, but these differences were of small magnitude. Despite achieving statistical significance, we find this difficult to interpret and potentially inconclusive, and in our opinion, without clinical significance. However, it is worth mentioning that other studies in neurocognitive disorders show relationships between select cytokines with measures of cognitive function, and this warrants further examination. We did not observe significant differences in either GFAP or NfL levels between PACS participants relative to controls, and all the samples were negative for antineuronal antibodies. We next sought to clarify whether these biochemical markers related to neuropsychological test results in PACS patients, as previous studies have inconsistent results regarding the association of inflammatory marker levels and neuropsychological tests. Results have ranged from no association 62 to an association between cytokine levels and fatigue or executive functions (Stroop Color Word test) 63 , or TNF-α levels and memory 64 . Here, we found that high levels of GFAP were associated with better performance on the Stroop Word test. No association was observed between cytokines, NfL, or GFAP levels and global or regional MRI measures after adjusting for multiple comparisons The fact that the positive association of GFAP levels and the Stroop Word test is contrary to what could be expected and the lack of congruency of previous results suggests that this could be a type I error, although this is an interesting finding that warrants further investigation. Finally, we found that patients’ serum or CSF samples did not immunoreact with brain tissue or live neurons, suggesting that brain autoantibodies are not involved in PACS symptoms. An interesting finding elucidated by this work is the breakdown of PACS amongst sex. Whereas COVID-19 infects women and men equally, related publications indicate that there is a higher prevalence of females with PACS, with percentages ranging from 63–74% 13,16,65 , in line with these observations, 79% of participants in this study were women. Interestingly, in a study including 377 patients with COVID-19 infection, the female sex was independently associated with PACS within the multivariable analysis 65 . This may indicate that the female sex is a risk factor for developing PACS and warrants further investigation. A major limitation of the study is the sample. Firstly, the sample size is small, evaluating only 49 participants at baseline and 46 with a follow-up visit after 6 months. Secondly, the present study neither has healthy participant controls nor participants with COVID-19 infection without cognitive complaints for neuropsychological or neuroimaging analyses. This was due to the review of the local Ethics Committee, which considered the inclusion of controls as too high of a demand. This study may face referral bias, as participants were referred by healthcare providers, potentially overrepresenting severe cases. Further research should consider a more diverse and randomized sample for a comprehensive understanding. Finally, we believe the current duration of this study was limited and that the inclusion of a longer endpoint with greater distance between measurement intervals may be more suitable for studying PACS cognitive symptoms. However, the study was designed during the last quarter of 2020, even before the formal definition of PACS, and most studies then were designed with short follow-up periods 20,57 . In conclusion, our study showed cognitive impairment, mainly affecting attention/executive and verbal memory functions at a mean of 10 months after the acute infection and persisting for at least 6 months. Cognitive impairment was accompanied by depressive symptoms, apathy, anxiety, fatigue, and low health status. These findings (except for visual memory loss) were not associated with brain structural abnormalities, elevated cytokines, markers of neuronal damage, or neuronal antibodies. Longitudinal studies of greater durations are needed to determine the long-term evolution and underlying biological mechanisms of cognitive impairment in PACS. Declarations Conflict of Interest The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contribution "NG, APM, NF and RSV wrote the main manuscript text. NG prepared tables 1-4 and APM figures 1-4. All authors reviewed the manuscript. Acknowledgments The authors thank patients for their participation in the research. This study was partially funded by Sage Therapeutics through an Investigator Sponsored Study. Dr. N. Falgàs is a recipient of a Juan Rodes research contract from the Instituto de Salud Carlos III, Spain. Data Availability Statement The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. References Davis, H. E., McCorkell, L., Vogel, J. M. & Topol, E. J. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol 21, 133–146 (2023). Bodro, M., Compta, Y. & Sánchez-Valle, R. Presentations and mechanisms of CNS disorders related to COVID-19. Neurol Neuroimmunol Neuroinflamm 8, e923 (2021). Ceban, F. et al. Fatigue and cognitive impairment in Post-COVID-19 Syndrome: A systematic review and meta-analysis. Brain, Behavior, and Immunity 101, 93–135 (2022). Ariza, M. et al. Neuropsychological impairment in post-COVID condition individuals with and without cognitive complaints. Front Aging Neurosci 14, 1029842 (2022). Delgado-Alonso, C. et al. 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Journal of Psychiatric Research 129, 98–102 (2020). Ferrando, S. J. et al. Neuropsychological, Medical, and Psychiatric Findings After Recovery From Acute COVID-19: A Cross-sectional Study. Journal of the Academy of Consultation-Liaison Psychiatry 63, 474–484 (2022). Nuber-Champier, A. et al. Acute TNFα levels predict cognitive impairment 6–9 months after COVID-19 infection. Psychoneuroendocrinology 106104 (2023) doi: 10.1016/j.psyneuen.2023.106104 . Bai, F. et al. Female gender is associated with long COVID syndrome: a prospective cohort study. Clinical Microbiology and Infection 28, 611.e9-611.e16 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Jan, 2024 Reviews received at journal 16 Dec, 2023 Reviewers agreed at journal 15 Dec, 2023 Reviewers invited by journal 21 Nov, 2023 Editor assigned by journal 19 Nov, 2023 Editor invited by journal 19 Nov, 2023 Submission checks completed at journal 19 Nov, 2023 First submitted to journal 16 Nov, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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A) Number of participants with altered/normal scores; (B) Mean scores are presented as scalar scores, adjusted by age and years of education, scores ≥ 7 are normal.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3621297/v1/9420d8055ffbd59ef3a88f84.png"},{"id":46875751,"identity":"f1cdf538-0773-4833-b564-c94f506fab0a","added_by":"auto","created_at":"2023-11-21 19:47:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1391971,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis results of neuropsychological studies.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3621297/v1/0aacbab577046f3bdab87894.png"},{"id":46875817,"identity":"30ceece5-7ad7-40cc-893b-e565ef7b81f5","added_by":"auto","created_at":"2023-11-21 19:55:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":608454,"visible":true,"origin":"","legend":"\u003cp\u003eBrain plots with the correlation between cortical thickness measures and gray matter volumes with the Rey Figure Recall. We only show the significant correlation corrected by multiple comparisons.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3621297/v1/556e8082dbd7f770c7a597c9.png"},{"id":46875753,"identity":"7e70cef9-6ba1-44bc-8491-bc3f1f84853f","added_by":"auto","created_at":"2023-11-21 19:47:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1058292,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal differences in regional MRI indexes (baseline vs 6-month follow-up).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3621297/v1/17d58da5d2464e003dca1996.png"},{"id":46875921,"identity":"c7824b77-d2c7-4f82-81da-6c49f7ec4067","added_by":"auto","created_at":"2023-11-21 20:03:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1233403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3621297/v1/b89fb6e3-d913-47be-a5f5-d3a4c31a822e.pdf"},{"id":46875755,"identity":"10b7142b-7eba-4714-b13c-464bceebf33c","added_by":"auto","created_at":"2023-11-21 19:47:56","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1074369,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3621297/v1/1d89dbb2f4f332b11730aa19.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive profile, neuroimaging and fluid biomarkers in post-acute COVID-19 syndrome","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003ePost-acute COVID-19 syndrome (PACS) is defined by the continuation or development of new symptoms 3 months after the initial SARS-CoV-2 (COVID-19) infection, lasting for at least 2 months with no other explanation. PACS might affect anyone exposed to COVID-19, regardless of the severity of the acute symptoms or the premorbid condition. Still, non-hospitalized patients with mild, acute illness, between ages of 36 and 50 years, as this population represents most COVID-19 cases. There also is a predominance of woman over men \u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDuring the acute phase of COVID-19 infection, a wide variety of neurological complications have been described, including: headache, anosmia, dysgeusia, dizziness, agitation, confusion, impaired level of consciousness, or acute stroke \u003csup\u003e2\u003c/sup\u003e. A recent meta-analysis evaluating the prevalence of persistent symptoms at 12 weeks or more after the acute COVID-19 infection reported that up to 22% of the subjects presented subjective or objective cognitive impairment \u003csup\u003e3\u003c/sup\u003e. In patients with PACS, headache and cognitive complaints are the most frequent neurological symptoms, but a long list of other neurological symptoms has been described. Few studies analyzed cognitive impairment in PACS using standard cognitive batteries; in those cases, cognitive impairment was characterized by altered attention, executive function, and memory \u003csup\u003e4\u0026ndash;8\u003c/sup\u003e. Characterization of the long-term cognitive impact in PACS patients have yet to be determined, however, this is an important area for research as long-term cognitive complaints are associated with increased anxiety and depression \u003csup\u003e9\u003c/sup\u003e, and decreased quality of life \u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePACS usually presents with various other symptoms, such as fatigue, dyspnea, joint or chest pain. A study showed that fatigue was the most frequent symptom in the acute and follow-up phases of COVID-19 \u003csup\u003e11\u003c/sup\u003e. A study of non-hospitalized post-COVID-19 patients with complex residual symptoms revealed that one-third of patients had not returned to work within 22 months after the infection despite efforts to recover function through exercise, respiratory, olfactory rehabilitation, cognition/speech therapy and/or psychological support \u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral hypotheses exist for the pathogenesis of PACS, including: persisting reservoirs of SARS-CoV-2 in tissues, immune dysregulation, autoimmunity, or damage to the microvasculature \u003csup\u003e1,13\u003c/sup\u003e. Some studies have found not only elevated levels of cytokines but also neuronal damage markers in patients with acute COVID-19 infection and neurological symptoms \u003csup\u003e14\u003c/sup\u003e, and an association of elevated cytokines or neuronal damage markers with post-acute sequelae of COVID-19 \u003csup\u003e14\u0026ndash;16\u003c/sup\u003e. Moreover, neuroimaging studies in PACS have reported mixed results: whereas some studies describe a reduction in cortical thickness (CTh), gray matter (GM) volume, or cerebral blood flow compared to controls in cross-sectional analyses, \u003csup\u003e6,17,18\u003c/sup\u003e others found an increase in GM volumes in some brain regions such as hippocampus and insula \u003csup\u003e19,20\u003c/sup\u003e. Also, white matter hyperintensities have also been observed \u003csup\u003e21\u003c/sup\u003e. It is unclear how together these may relate with the cognitive impact seen in PACS patients.\u003c/p\u003e \u003cp\u003eIn this study, we first aimed to characterize the cognitive profile of patients with cognitive complaints in PACS and their recovery over 6 months. Secondly, we aimed to evaluate several markers of neuronal damage (structural MRI and fluid markers) and inflammation to study cross-sectional and longitudinal associations with clinical and cognitive features.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eWe performed a prospective evaluation of patients referred to the Neurology Service at the Hospital Cl\u0026iacute;nic de Barcelona, Barcelona, Spain, following cognitive complaints after SARS-CoV2 infection. Participants were consecutively recruited between March 2021 and November 2021. Inclusion criteria were: 1) COVID-19 diagnosis, based on biological diagnosis or medical report; 2) Cognitive symptoms reported by the participant or an observer (family member, co-worker, health professional) 3) presence of cognitive symptoms\u0026thinsp;\u0026ge;\u0026thinsp;8 weeks after COVID-19 symptoms onset; 4) fluent in Spanish; 5) at least 6 years of formal education; 6) age 35\u0026ndash;65 years old (participants above the age of 65 years were not included in order to avoid a possible confusion factor with onset of neurodegenerative diseases symptoms). Exclusion criteria were: 1) Previous diagnosis of any neurological, psychiatric, or medical condition that could affect the baseline cognitive performance, including previous chronic fatigue syndrome diagnosis; 2) any condition that prevented the completion of the cognitive assessment and/or MRI scanning.\u003c/p\u003e \u003cp\u003e This study was performed according to the international consensus for research with human subjects (the updated version of Helsinki\u0026rsquo;s Statement, Fortaleza, 2013) and Spanish regulations. The Hospital Cl\u0026iacute;nic de Barcelona Ethics Committee (HCB/2020/1483) approved the study, and all participants provided informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Clinical and neuropsychological assessment\u003c/h2\u003e \u003cp\u003eParticipants were evaluated at baseline, 1 month, 3 months, and 6 months of follow-up. Participants underwent general and neurological assessments and a comprehensive neuropsychological (NPS) battery administered by a trained neuropsychologist. The battery included estimated premorbid IQ (Spanish Word Accentuation Test) \u003csup\u003e22\u003c/sup\u003e, verbal memory tests: Free and Cued Selective Reminding Test (FCSRT) \u003csup\u003e23\u003c/sup\u003e; visual memory tests: Rey-Osterrieth Complex Figure Test (ROCF) Delayed Recall \u003csup\u003e24\u003c/sup\u003e; language tests: Boston Naming Test \u003csup\u003e25\u003c/sup\u003e, Vocabulary, Semantic Fluency \u003csup\u003e26\u003c/sup\u003e; visuospatial abilities: Rey Figure Copy; and attention and executive function tests: Trail Making Test (TMT) A and B \u003csup\u003e27\u003c/sup\u003e, Stroop Test \u003csup\u003e28\u003c/sup\u003e, Symbol Digits Modalities Test (SMDT) \u003csup\u003e29\u003c/sup\u003e, Digit Span Test \u003csup\u003e30\u003c/sup\u003e, Letter-Number sequencing \u003csup\u003e30\u003c/sup\u003e, Symbol Search \u003csup\u003e30\u003c/sup\u003e, and phonemic fluency \u003csup\u003e31\u003c/sup\u003e. The participants also completed the Beck Depression Inventory (BDI) \u003csup\u003e32,33\u003c/sup\u003e, the Beck Anxiety Inventory (BAI) \u003csup\u003e34\u003c/sup\u003e, the Starkstein Apathy Scale (SAS) \u003csup\u003e35\u003c/sup\u003e, the Subjective Cognitive Decline Questionnaire (SCD-Q) \u003csup\u003e36\u003c/sup\u003e, the Multidimensional Fatigue Inventory (MFI-20) \u003csup\u003e37\u003c/sup\u003e and the 36-Item \u003cem\u003eShort Form Health Survey (SF-36)\u003c/em\u003e \u003csup\u003e38,39\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Neuroimaging studies\u003c/h2\u003e \u003cp\u003eMRI scanning was performed at inclusion and the end of the study (6 months) using a 3T Prisma Siemens (Siemens Medical Systems, Germany). A high-resolution 3D structural dataset (T1-weighted, MP-RAGE, repetition time\u0026thinsp;=\u0026thinsp;2.400ms, echo time\u0026thinsp;=\u0026thinsp;2.22ms, 208 slices, field-of-view\u0026thinsp;=\u0026thinsp;256 mm, 0.8 mm isotropic voxel) was acquired for everyone at each time. We used the processing stream available in FreeSurfer version 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://surfer.nmr.mgh.harvard.edu.sire.ub.edu/\u003c/span\u003e\u003cspan address=\"http://surfer.nmr.mgh.harvard.edu.sire.ub.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to perform cortical reconstruction and volumetric segmentation of the T1-weighted acquisitions. FreeSurfer allowed us to obtain cortical thickness (CTh) maps and segment the subcortical structures. For longitudinal data, we used the longitudinal stream in FreeSurfer. All FreeSurfer preprocessing steps are reported in detail elsewhere \u003csup\u003e40\u0026ndash;42\u003c/sup\u003e. From the reconstructed data, we obtained global measures of mean CTh and grey matter (GM) volumes of the left and right hemispheres. In addition, we used the summary measures of mean CTh in 68 cortical parcellations and GM volumes of 14 subcortical structures, all derived from atlases available in FreeSurfer \u003csup\u003e43,44\u003c/sup\u003e. All images and individual segmentations were visually inspected and manually corrected if needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Biological measures\u003c/h2\u003e \u003cp\u003eBlood samples were obtained at baseline (n\u0026thinsp;=\u0026thinsp;49), at 1 month (n\u0026thinsp;=\u0026thinsp;48), 3 months (n\u0026thinsp;=\u0026thinsp;47), and 6 months (n\u0026thinsp;=\u0026thinsp;46). An optional lumbar puncture to obtain cerebrospinal fluid was offered to the participants at the basal visit (n\u0026thinsp;=\u0026thinsp;12). Serum levels of neurofilament-light (NfL) and glial fibrillary acidic protein (GFAP) were determined by single molecule array technology (Neurology 2-Plex B Simoa, Quanterix\u0026reg;). A panel of cytokines, chemokines and other soluble mediators that included interferon (IFN)-α, β, and γ, interleukins (IL) IL-1β, IL-6, IL-8, IL-10, IL-17A, IL-18, tumor necrosis factor (TNF)-α2, IL-1 receptor antagonist (IL-1ra), Interferon-γ-Inducible Protein 10 (IP-10), granulocyte colony-stimulating factor (G-CSF), antigen CD25, chemokine ligand 1 (CX3CL1 or fractalkine), chemokine ligand 2 (CCL2), chemokine ligand 7 (CCL7) and ligand 9 (CXCL9) was analyzed in serum and CSF by a multiplexed bead based assay (Human Cyto Panel A, Merck, Germany) in a Luminex\u0026reg;100/200 platform. In addition, a tissue-based assay (TBA) consisting of an indirect immunohistochemistry (IIHC) with rat brain tissue and an indirect immunofluorescence assay (IIFA) with live neurons were performed to screen anti-neuronal immunoreactivity \u003csup\u003e45,46\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe prospective study did not include cognitive normal non-PACS controls. As most of the biochemical measures evaluated here lack established cut-offs for normal values \u003csup\u003e47\u0026ndash;49\u003c/sup\u003e, we included in the analysis 38 serum and 24 CSF samples from non-COVID-19 healthy controls from a previous study in acute COVID-19 \u003csup\u003e14\u003c/sup\u003e. Normal results were defined as results within two standard deviations of the mean of the control group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eRaw cognitive scores were converted to scalar scores (SS) according to age and number of formal education, with a normal distribution with a mean scalar score of 10. Abnormal cognitive performance was set at SS\u0026thinsp;\u0026lt;\u0026thinsp;7, which is the cutoff point used in clinical practice. If one subtest or more showed abnormal scores, the evaluation was considered abnormal.\u003c/p\u003e \u003cp\u003eWe first evaluated if cognitive test results differed in participants who scored within the normal range versus the pathological range on measures of anxiety, depression, apathy, fatigue, or quality of life scores. For that, we used previously described cut-offs: BDI\u0026thinsp;\u0026ge;\u0026thinsp;20 indicated moderate or severe depression, BAI\u0026thinsp;\u0026ge;\u0026thinsp;16 defined moderate or severe anxiety, SAS\u0026thinsp;\u0026ge;\u0026thinsp;14 was considered clinically significant apathy, SCD-Q\u0026thinsp;\u0026ge;\u0026thinsp;7 was considered pathological, MFI-20 cutoff of \u0026ge;\u0026thinsp;60 was used for the description of a high-level versus low-level fatigue. SF36 subscales cutoff of \u0026ge;\u0026thinsp;50 indicated normative scores. We used permutation tests, adding age, sex, and years of education as covariates. Then, we studied the partial correlation between cognitive measures and physical and mental health scores with continuous variables and added age, sex, and years of education as covariables.\u003c/p\u003e \u003cp\u003eWe also analyzed the neuropsychological results with a principal component analysis (PCA), a dimensionality reduction method. By analyzing the first component and the individual contribution of each variable to it, we estimated which variables explained the highest variability in the data. We measured the partial correlation of global and regional MRI measures with memory and executive function outcomes, anxiety, depression, fatigue and subjective cognitive complaints (SCD), and added age, sex, and years of education as covariables. All analyses were corrected for multiple comparisons.\u003c/p\u003e \u003cp\u003eWe measured the partial correlation of inflammatory soluble mediators, NfL, and GFAP levels with SCD, memory and executive function outcomes, anxiety, depression, fatigue, and global and regional MRI measures and added age and sex as covariables. All analyses were corrected for multiple comparisons using the Benjamini-Hochberg adjustment.\u003c/p\u003e \u003cp\u003eWe performed longitudinal analyses including the baseline and the three follow-up visits with linear mixed-effects models to study changes between visits in cognitive measures, physical and mental health scores, global and regional MRI measures, and biochemical values for all the available data in each case. For cognitive tests, age, sex, and years of education were added as fixed effects. In the MRI and biochemical studies, age and sex were considered fixed effects.\u003c/p\u003e \u003cp\u003eStatistical analyses of the cross-sectional and longitudinal results were carried out in the language R version 4.2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Clinical data and neuropsychological characteristics\u003c/h2\u003e\n\u003cp\u003eFifty-three participants were assessed, 49 were included in the study and 46 completed the follow-up. 39 (80%) participants included were women, the mean age was 50.1 (SD 7.9, range 35\u0026ndash;64), mean years of education (YOE) 14 (SD 3), with mean time from the onset of acute symptoms of 10.4 months (SD 3.9). 10 (20%) participants needed hospitalization; 7 (14%) received oxygen support, including 2 (4%) admitted into intensive care units. Participants presented with multiple symptoms other than cognitive complaints: 43 (88%) referred fatigue, 30 (61%) headaches, 31 (63%) dyspnea, 24 (49%) arthralgias, 21 (43%) myalgias, 19 (39%) bowel rhythm disturbances, 12 (24%) anosmia, 13 (27%) dysgeusia and 5 (10%) intermittent febrile. Twenty (41%) participants were on sick leave at inclusion.\u003c/p\u003e\n\u003cp\u003eThe sample had high premorbid intelligence, estimated with the Word Accentuation Test. Twelve participants (24.5%) showed a normal cognitive evaluation defined by all test results within the limits of normality for age and years of education. The remaining participants (75.5%) presented abnormal scores in at least 1 test. Abnormal results were most frequently observed in executive functions and verbal memory (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For this reason, further analysis with cognitive tests included only memory and executive functions tests.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eNeuropsychological test results FCSRT, Free and Cued Selective Reminding Test; NPS, neuropsychological; SD, standard deviation; SMDT, Symbol Digit Modalities Test; VOSP, Visual Object and Space Perception Battery\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCognitive test\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBaseline N\u0026thinsp;=\u0026thinsp;49\u003c/p\u003e\n\u003cp\u003eMean scalar score (SD);\u003c/p\u003e\n\u003cp\u003e%with abnormal scores\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;1 month N\u0026thinsp;=\u0026thinsp;48\u003c/p\u003e\n\u003cp\u003eMean scalar score (SD);\u003c/p\u003e\n\u003cp\u003e%with abnormal scores\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;3 months N\u0026thinsp;=\u0026thinsp;47\u003c/p\u003e\n\u003cp\u003eMean scalar score (SD);\u003c/p\u003e\n\u003cp\u003e%with abnormal scores\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;6 months N\u0026thinsp;=\u0026thinsp;46 Mean scalar score (SD); % with abnormal scores\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eMEMORY\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFCSRT Free learning\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.6 (3.4); 13 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.4 (3.3); 4 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.4 (3.3); 4 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.7 (3.7); 3(7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFCSRT Total learning\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.8(4.1); 11 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.5 (4.7); 6 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.1 (4.0); 2(4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.2 (4.1); 4 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFCSRT Delayed Free recall\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.2 (3.4); 13 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.7 (3.5); 9 (19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.4 (3.2); 3 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.3 (3.0); 2 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFCSRT Delayed total recall\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.2 (4.9); 8 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.5 (5.1); 4 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.2 (5.2); 5 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.9 (4.9); 4 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRey figure Recall\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.8 (2.4); 4 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.3 (3.0); 2 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.0 (2.8); 3 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.9(2.6); 0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eLANGUAGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBoston naming test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.2(3.3); 2 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.7 (3.5); 2 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.7 (3.3); 1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.5(3.6); 1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSemantic fluency test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.8(2.8); 5 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.1 (3.1); 3 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.3 (3.2); 4 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.7(2.8); 3 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVocabulary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.3(2.2); 2 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.3 (1.9); 1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.5 (2.3); 1(2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.8(1.7); 1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVISUOESPATIAL ABILITIES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRey figure copy accuracy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.1(3.8); 2(4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.7 (2.9); 1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.9 (3.5); 1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.6(3.7); 1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRey figure time\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.5(2.9); 5 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.1 (3.3); 5 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.9 (3.0); 2 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.3 (3.2); 2 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"10\" align=\"left\"\u003e\n\u003cp\u003eATTENTION AND EXECUTIVE FUNCTIONS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTrail Making Test - A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.2(3.7); 9 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.6 (3.9); 7(15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.0 (3.8); 3 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.3(4.1); 5 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTrail Making Test - B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.3(3.0); 13 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.0 (3.4); 9 (19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.3 (3.6); 6 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.6 (3.6); 8 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhonemic fluency\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.9 (2.3); 4 (8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.1 (2.4); 5 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.5 (2.7); 4 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.6 (2.6); 4 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDigit Span - Forward\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.7(3.2); 7 (14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.0 (3.0); 9 (19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.5 (2.8); 6 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.3 (3.1); 11 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLetter-number sequencing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.7(3.4); 13 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.7 (3.1); 8 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.4 (2.3); 8 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.7 (2.7); 7 (15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSDMT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.3(2.8); 12 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.9 (3.5); 10 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.8 (3.3); 6 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.8 (3.3); 6 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSymbol search\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.2 (2.2); 3 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.2 (2.4); 2 (4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.9 (2.3); 1(2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.9 (2.1); 1(2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStroop word\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.2(3.3); 14 (29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.2 (2.8); 14 (29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.7 (2.6); 10 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.5 (2.7); 10 (22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStroop color\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.8 (3.3); 9 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.6 (2.9); 10 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.9 (2.8); 8 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.3 (3.1); 12 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStroop word-color\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.8 (2.9); 12 (24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.8 (3.1); 11 (23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.0 (3.1); 9 (19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.0 (2.6); 9 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNine (19%) participants presented moderate-severe depression (based on questionnaire results), 26 (57%) showed moderate-severe anxiety, and 29 (64%) showed clinically significant apathy. Regarding subjective cognitive complaints, reports of participants (MyCog) were altered in 48/49 (98%), and reports of proxies (TheirCog) were altered in 33/44 (75%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). MFI-20 mean total score was 57.7, 60.0 is the limit for severe fatigue; 35.4% of the sample was in the severe fatigue group (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared to normative data, PACS participants had lower scores on each of the SF-36 domains. The most affected domain was \u0026ldquo;Energy/fatigue (mean score of 30, altered in 80% of participants), followed by role limitations due to physical health (mean score of 34, altered in 64%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSubjective cognitive decline and psychiatric symptoms questionnaires SCD-Q, subjective cognitive decline questionnaire.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFollow-up\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBeck Anxiety Inventory\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(0 to 63)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMinimal (0 to 7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMild (8 to 15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (28%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate (16 to 25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSevere (26 to 63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (256%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBeck Depression Inventory\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(0 to 40)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMinimal (0 to 13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMild depressive symptoms (14 to 18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate clinical depression (19 to 27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSevere depression (28 to 63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSCD-Q MyCog\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(0 to 24)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;41\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal (0 to 6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathological (7 to 24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45 (98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSCD-Q TheirCog\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(0 to 24)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;41\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal (0 to 6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathological (7 to 24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultidimensional Fatigue Inventory (MFI-20) SD, standard deviation\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMultidimensional Fatigue Inventory (MFI-20)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003cp\u003eMean score (SD)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFollow-up\u003c/p\u003e\n\u003cp\u003eMean score (SD)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMFI-20 Total Score (20 to 100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e57.7 (5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.3 (6.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMFI-20 Mental Fatigue (4 to 20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.2 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.8 (3.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMFI-20 General Fatigue (4 to 20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.3 (1.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.2 (2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMFI-20 Physical Fatigue (4 to 20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.3 (2.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.8 (2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMFI-20 Reduction of Activity (4 to 20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.9 (2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.9 (1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMFI-20 Reduction of Motivation (4 to 20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.7 (2.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.0 (2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e36-Item Short Form Survey Instrument assessing health-related quality of life\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e36-Item Short Form Survey Instrument (SF36)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003cp\u003eMean score (SD) / n(%) altered under 50\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFollow-up\u003c/p\u003e\n\u003cp\u003eMean score (SD) / n(%) altered under 50\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical Functioning\u003c/p\u003e\n\u003cp\u003e(0 to 100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e64.3(23.3) / 9(20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e65.5(27.3) / 13(32.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeneral Health\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e51.8(17.4) / 23 (51.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48.88(23.5) / 23(57.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRole limitations due to physical health\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.9(44.0) / 29(64.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.25(41.12) / 26 (65.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePain\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e47.3(28.2) / 26 (57.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.1(27.4) / 23(57.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmotional Well-being\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56.2(18.0) / 16(35.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49.9(19.7) / 21(52,5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSocial Functioning\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.8(26.4) / 13(28.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52.3(26.3) / 17(42.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRole limitations due to emotional problems\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.9(44.3) / 27(61.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.9(44.4) / 22(55.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnergy/Fatigue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.8(18.6) / 36(80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.4(18.8) / 34(85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eSD, standard\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWe next sought to stratify participants by levels of anxiety, depression, apathy, fatigue, or quality of life scores according to their questionnaire scores. Participants displaying moderate or severe anxiety showed lower results in the Rey Figure Recall subtest (adjusted p-value\u0026thinsp;=\u0026thinsp;0.0014. No significant differences were observed in cognitive tests between participants with normal and abnormal values of the other stratification categories.\u003c/p\u003e\n\u003cp\u003eTo understand which neuropsychological domains were most affected in this PACS sample, a PCA was performed (cognitive and questionnaire scores). This revealed that executive function scores explained most of the variability of the cognitive results in the PACS sample population (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The three cognitive tests that explained most of the variability of the data were: Symbol Search, SDMT, and TMT-A.\u003c/p\u003e\n\u003cp\u003eAt 6 months follow-up, the cognitive test scores remained unchanged compared to baseline, with the exception of the Delay Free Recall test, with a mean score of 10.0 at baseline and a mean score of 12.4 after 6 months (0\u0026thinsp;=\u0026thinsp;0.0040) (the linear mixed-effects coefficients are shown in the Supplementary Material). Twenty-three participants (50%) had a normal cognitive evaluation at follow-up, whereas the remaining participants (50%) showed abnormal scores on at least one test. The number of participants with a normal NPS was higher at follow-up (50%) compared to baseline (24%) (p-value\u0026thinsp;=\u0026thinsp;0.012). We did not find longitudinal differences in depression, anxiety, fatigue, or SCD scores at 6 months.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Correlations between neuroimaging and cognitive data\u003c/h2\u003e\n\u003cp\u003eTo identify whether neuropsychological tests correlate with the neuroimaging data, partial correlations were performed. Rey Figure Recall scores showed a moderate correlation with total GM volume (r\u0026thinsp;=\u0026thinsp;0.46, adjusted p-value\u0026thinsp;=\u0026thinsp;0.046), subcortical GM volume (r\u0026thinsp;=\u0026thinsp;0.52, adjusted p-value\u0026thinsp;=\u0026thinsp;0.024), and left cerebral white matter (WM) (r\u0026thinsp;=\u0026thinsp;0.47, adjusted p-value\u0026thinsp;=\u0026thinsp;0.046. At the regional level, the ROCF recall scores also showed a significant correlation with GM volume in left hippocampus GM (r\u0026thinsp;=\u0026thinsp;0.51, adjusted p-value\u0026thinsp;=\u0026thinsp;0.042), right hippocampus GM (r\u0026thinsp;=\u0026thinsp;0.49, adjusted p-value\u0026thinsp;=\u0026thinsp;0.042), and right thalamus GM (r\u0026thinsp;=\u0026thinsp;0.48, adjusted p-value\u0026thinsp;=\u0026thinsp;0.042, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Other cognitive tests and clinical outcomes (SCD, anxiety, depression questionnaires) were not significantly associated with global or regional structural MRI indexes after correction for age, sex, years of education, and multiple comparisons.\u003c/p\u003e\n\u003cp\u003eWhen probing for longitudinal changes, we did not identify significance in global structural MRI measures at 6 months in PACS participants compared with baseline. At the regional level, we found GM loss at 6 months in the GM volume of left pallidum (p-value\u0026thinsp;=\u0026thinsp;0.0098) and left transverse temporal thickness (p-value\u0026thinsp;=\u0026thinsp;0.018) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Biochemical analyses\u003c/h2\u003e\n\u003cp\u003eFinally, we assessed group serum levels of NfL, GFAP, and cytokines, as well as CSF levels of cytokines. We also sought to assess whether serum or CSF samples contained anti-neuronal immunoreactivity. We looked for differences in NfL, GFAP and cytokines levels between PACS and controls. Serum NfL and GFAP did not differ between groups. In serum, G-CSF, 1P10, MIG, and MCP1 levels were significantly lower in PACS. CSF, IL-18, and IL-17a were significantly higher in PACS, and IL10, IL1AR, IL-8, IP10, and MCP1 were significantly lower in PACS. However, the magnitude of the differences was small, and all values were within the limits of normality (Supplementary material). All the serum and CSF samples were negative for anti-neuronal immunoreactivity. At the longitudinal level, we did not find changes in cytokines, NFL, or GFAP levels in PACS participants, except for the IL17A, which presented a significant decrease between baseline and at 6 months (still within the normal levels). We found a moderate positive correlation between GFAP levels and the Stroop Word test scores (r\u0026thinsp;=\u0026thinsp;0.48, adjusted p-value\u0026thinsp;=\u0026thinsp;0.018). Global and regional MRI indexes and cognitive scores did not significantly correlate with serum or CSF cytokine or NFL levels after adjusting for multiple comparisons.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn the present study, we evaluated PACS patients with subjective cognitive complaints and their evolution over a 6-month period. Our analysis revealed that thirty-seven (75.5%) participants presented with abnormal cognitive evaluations at baseline, with executive functions and memory as the most affected domains. Rey Figure Recall scores were the only cognitive abnormality identified to be associated with MRI indexes, showing a relationship with global and region GM and WM loss. Participants also reported high levels of depression, anxiety, apathy, and fatigue in addition to other frequent physical symptoms. Fluid biomarkers in serum and CSF, including neuronal damage and inflammatory markers, were within the normal limits yet included some statistically significant differences with controls. All test samples were negative for neuronal antibody reactivity. Finally, longitudinal analyses of cognitive symptoms, cognitive scores and mental health outcomes revealed that most of the initial symptoms of PACS did not improve with time, although 50% of participants showed normal cognition at evaluation at follow-up.\u003c/p\u003e \u003cp\u003eCognitive evaluations in PASC showed that attention-executive and verbal memory were the most affected domains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which has been described in previous published works; however, the pattern of alterations was broader and more heterogeneous between patients \u003csup\u003e5\u0026ndash;8,50,51\u003c/sup\u003e. The sample had a high premorbid intelligence and would not be expected to perform below average on cognitive testing. At a 6-month follow-up, we determined that only the Delayed Free Recall of verbal memory scores improved significantly from baseline. Nevertheless, if we consider the percentage of normal evaluations (defined as the proportion of tests within clinical limits of normality), there was a significant improvement with time. We cannot exclude that a learning effect could partially explain this result, as the same tests were administered four times in 6 months. Given that most of the participants with abnormal results were near the threshold for normal performance, only a small improvement in these tests would enable them to reach normal threshold values. Our results are in line with previous published works showing both improvements and persisting cognitive deficits in PACS \u003csup\u003e52,53\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eParticipants also reported depressive symptoms, anxiety, apathy, fatigue, and low scores in general health. These symptoms did not improve during this 6-month study (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Given that our analysis demonstrated a significant relationship between one memory test and stratification in anxiety scores, we believe that the coexistence of cognitive and mental health symptoms could not be interpreted as causality. While we cannot definitively exclude that comorbid mental health issues may impede cognitive symptom improvement, it is worth noting that these mental health issues could be a consequence of the cognitive impairment as described elsewhere \u003csup\u003e54\u003c/sup\u003e. In addition, both types of symptoms could be driven by fatigue, which was almost universal and severe in 35% of participants.\u003c/p\u003e \u003cp\u003eOur next approach in this study was to correlate clinical and neuroimaging features of this PACS cohort longitudinally. While a previous study has included both cognitive and neuroimaging assessment of PACS \u003csup\u003e55\u003c/sup\u003e, to our knowledge, this is the first study to include longitudinal analysis of both cognitive and neuroimaging tests. We found significant correlations of both global and focal measures with visual memory but not with other cognitive tests. This correlation indicated that worse visual memory was associated with lower total and subcortical GM volume together with left cerebral WM volume. Furthermore, subcortical GM volumes, especially the hippocampus and thalamus, significantly corresponded with worse visual memory performance. Previous studies also explored the association between GM volume and cognitive symptoms; it has been reported that worse memory and visuospatial test performance is associated with a loss of GM volume \u003csup\u003e6,20\u003c/sup\u003e. In line with previous studies, our longitudinal analyses neither found improvement at a 6-month period, nor evidence of progressive volume loss.\u003c/p\u003e \u003cp\u003eThe majority of previous studies \u003csup\u003e13,14,16,56\u0026ndash;59\u003c/sup\u003e, have reported high levels of plasma and/or CSF cytokines, NfL and GFAP in the acute or subacute phase of COVID-19 infection that normalize at follow-up, albeit using differing follow-up intervals \u003csup\u003e57,60,61\u003c/sup\u003e. Some of these studies related these biochemical changes with the severity of the infection or the gravity of neurological symptoms; however, there is no consensus on how fluid biomarkers relate to acute COVID-19 symptom severity, PASC symptoms, or PASC progression/resolution. In our study, the levels of plasma and CSF cytokines, NfL and GFAP were within pre-specified normal limits. We found some differences in cytokine levels between PACS and control participants, but these differences were of small magnitude. Despite achieving statistical significance, we find this difficult to interpret and potentially inconclusive, and in our opinion, without clinical significance. However, it is worth mentioning that other studies in neurocognitive disorders show relationships between select cytokines with measures of cognitive function, and this warrants further examination. We did not observe significant differences in either GFAP or NfL levels between PACS participants relative to controls, and all the samples were negative for antineuronal antibodies.\u003c/p\u003e \u003cp\u003eWe next sought to clarify whether these biochemical markers related to neuropsychological test results in PACS patients, as previous studies have inconsistent results regarding the association of inflammatory marker levels and neuropsychological tests. Results have ranged from no association \u003csup\u003e62\u003c/sup\u003e to an association between cytokine levels and fatigue or executive functions (Stroop Color Word test) \u003csup\u003e63\u003c/sup\u003e, or TNF-α levels and memory \u003csup\u003e64\u003c/sup\u003e. Here, we found that high levels of GFAP were associated with better performance on the Stroop Word test. No association was observed between cytokines, NfL, or GFAP levels and global or regional MRI measures after adjusting for multiple comparisons The fact that the positive association of GFAP levels and the Stroop Word test is contrary to what could be expected and the lack of congruency of previous results suggests that this could be a type I error, although this is an interesting finding that warrants further investigation. Finally, we found that patients\u0026rsquo; serum or CSF samples did not immunoreact with brain tissue or live neurons, suggesting that brain autoantibodies are not involved in PACS symptoms.\u003c/p\u003e \u003cp\u003eAn interesting finding elucidated by this work is the breakdown of PACS amongst sex. Whereas COVID-19 infects women and men equally, related publications indicate that there is a higher prevalence of females with PACS, with percentages ranging from 63\u0026ndash;74% \u003csup\u003e13,16,65\u003c/sup\u003e, in line with these observations, 79% of participants in this study were women. Interestingly, in a study including 377 patients with COVID-19 infection, the female sex was independently associated with PACS within the multivariable analysis \u003csup\u003e65\u003c/sup\u003e. This may indicate that the female sex is a risk factor for developing PACS and warrants further investigation.\u003c/p\u003e \u003cp\u003eA major limitation of the study is the sample. Firstly, the sample size is small, evaluating only 49 participants at baseline and 46 with a follow-up visit after 6 months. Secondly, the present study neither has healthy participant controls nor participants with COVID-19 infection without cognitive complaints for neuropsychological or neuroimaging analyses. This was due to the review of the local Ethics Committee, which considered the inclusion of controls as too high of a demand. This study may face referral bias, as participants were referred by healthcare providers, potentially overrepresenting severe cases. Further research should consider a more diverse and randomized sample for a comprehensive understanding. Finally, we believe the current duration of this study was limited and that the inclusion of a longer endpoint with greater distance between measurement intervals may be more suitable for studying PACS cognitive symptoms. However, the study was designed during the last quarter of 2020, even before the formal definition of PACS, and most studies then were designed with short follow-up periods \u003csup\u003e20,57\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn conclusion, our study showed cognitive impairment, mainly affecting attention/executive and verbal memory functions at a mean of 10 months after the acute infection and persisting for at least 6 months. Cognitive impairment was accompanied by depressive symptoms, apathy, anxiety, fatigue, and low health status. These findings (except for visual memory loss) were not associated with brain structural abnormalities, elevated cytokines, markers of neuronal damage, or neuronal antibodies. Longitudinal studies of greater durations are needed to determine the long-term evolution and underlying biological mechanisms of cognitive impairment in PACS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\"NG, APM, NF and RSV wrote the main manuscript text. NG prepared tables 1-4 and APM figures 1-4. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors thank patients for their participation in the research. This study was partially funded by Sage Therapeutics through an Investigator Sponsored Study. Dr. N. Falg\u0026agrave;s is a recipient of a Juan Rodes research contract from the Instituto de Salud Carlos III, Spain.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDavis, H. E., McCorkell, L., Vogel, J. M. \u0026amp; Topol, E. J. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol 21, 133\u0026ndash;146 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBodro, M., Compta, Y. \u0026amp; S\u0026aacute;nchez-Valle, R. Presentations and mechanisms of CNS disorders related to COVID-19. 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Clinical Microbiology and Infection 28, 611.e9-611.e16 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"post-acute COVID-19, cognitive symptoms, MRI, cytokines, longitudinal study","lastPublishedDoi":"10.21203/rs.3.rs-3621297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3621297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe aimed to characterize the cognitive profile of post-acute COVID-19 syndrome (PACS) patients with cognitive complaints, exploring the influence of biological and psychological factors. Participants with confirmed SARS-CoV-2 infection and cognitive complaints\u0026thinsp;\u0026ge;\u0026thinsp;eight weeks post-acute phase were included. A comprehensive neuropsychological battery (NPS) and health questionnaires were administered at inclusion and at 1, 3 and 6 months. Blood samples were collected at each visit, MRI scan at baseline and at 6 months, and, optionally, cerebrospinal fluid. Cognitive features were analyzed in relation to clinical, neuroimaging, and biochemical markers at inclusion and follow-up. Forty-nine participants, with a mean time from symptom onset of 10.4 months, showed attention-executive function (69%) and verbal memory (39%) impairment. Apathy (64%), moderate-severe anxiety (57%), and severe fatigue (35%) were prevalent. Visual memory (8%) correlated with total gray matter (GM) and subcortical GM volume. Neuronal damage and inflammation markers were within normal limits. Over time, cognitive test scores, depression, apathy, anxiety scores, MRI indexes, and fluid biomarkers remained stable, although fewer participants (50% vs. 75.5%; p\u0026thinsp;=\u0026thinsp;0.012) exhibited abnormal cognitive evaluations at follow-up. Altered attention/executive and verbal memory, common in PACS, persisted in most subjects without association with structural abnormalities, elevated cytokines, or neuronal damage markers.\u003c/p\u003e","manuscriptTitle":"Cognitive profile, neuroimaging and fluid biomarkers in post-acute COVID-19 syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-21 19:47:51","doi":"10.21203/rs.3.rs-3621297/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-01-16T06:56:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-12-16T17:54:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"00ed00e0-6b17-4838-950f-fa8222f98138","date":"2023-12-15T15:12:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-11-21T10:07:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-11-20T04:39:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-11-20T02:50:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-11-20T02:46:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-11-16T15:50:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96de5121-a190-4b3c-b269-bfbbec8fdb82","owner":[],"postedDate":"November 21st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":26508284,"name":"Biological sciences/Neuroscience/Cognitive neuroscience"},{"id":26508285,"name":"Biological sciences/Neuroscience/Neuroimmunology"}],"tags":[],"updatedAt":"2024-05-24T05:36:52+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-21 19:47:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3621297","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3621297","identity":"rs-3621297","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}