Patients recovering from COVID-19 who presented with anosmia during their acute episode have behavioral, functional, and structural brain alterations…

Demography

We aimed to evaluate for cognitive, structural, and functional alteration in patients recovering from COVID-19 and how this alteration depends on the clinical profile of the patients (see Figs.1 and 2). Two clinical factors were assessed: Anosmia (An, involving anosmia and hyposmia/microsmia, see below) during the acute episode as a potential marker for neurological involvement, and hospitalization (HR) during the acute episode to indicate the severity of respiratory symptoms. Using linear modeling, we observed variations in age and the time elapsed between diagnosis and the first session, which includes MRI, behavioral task and clinical anamnesis (see Methods), among patients exhibiting these factors. Concerning age, there were no significant differences observed between patients with and without COVID-19 (age in z-score, beta=0.05, se=0.2, t=0.2, p=0.8) or between patients with and without anosmia (beta=0.05, se=0.2, t=0.2, p=0.8). However, patients with COVID-19 who required hospitalization were older than those with COVID-19 who did not require hospitalization (beta=0.7, se=0.2, t=3.4, r=0.35 [0.15 0.55], p=0.0008). Additionally, patients with COVID-19 requiring hospitalization presented a longer time interval between diagnosis and the first session (time in z-score, beta=0.6, se=0.2, t=3.1, r=0.32 [0.12 0.52], p=0.002). We did not find a difference in educational level between groups (betas<0.2, ts<1, ps>0.3). Consequently, age and time between diagnosis and the first session were used as control regressors in all analyses comparing clinical factors, as indicated in Fig.2A.

Reversal learning task. (A) Timeline of a trial. (B) Earnings for the three phases of the task for the complete sample. (C) Trial means and standard errors of the evaluation of earnings during the three phases of the task for the complete sample.

Behavioral and functional results. (A) Model applied to behavioral and brain data. B-C. Behavioral data from Reversal Learning Task. (B) Regressor effects over the rate of option change after a negative outcome during shift periods. (C) Regressor effects over learning rate following a negative outcome. (D) BOLD activity during the Reversal Learning Task. The left panel shows the global effect of the task. The right panel indicates the negative effect of the Anosmia regressor. HR: Hospitalization required; An: Anosmia; CTD: cluster-threshold detection.

All patients were queried about persistent post-COVID symptoms during initial anamnesis in the first session (see Methods).24 Twenty-two patients diagnosed with COVID-19 reported experiencing some degree of attention and memory issues, which persisted at the time of cognitive assessment battery administered in the study (second sessions, see Methods). The frequency of these reported cognitive symptoms did not show modulation by clinical factors (linear model dof=93, Anosmia: beta=0.05, se=0.09, t=0.5, r=0.06 [0.17 0.29], p=0.6; Hospitalization required: beta=0.08, se=0.09, r=0.09 [0.13 0.32], t=0.8, p=0.4). Additionally, seven patients reported cephalea, and six reported fatigue. Only four patients reported persistent olfactory alteration post-acute episodes. Patients reported an average duration of 1.3months (range: 0.514months) for their olfactory dysfunction. Of these patients, 68% (n=29) experienced a complete loss of smell (anosmia), while 32% (n=14) experienced varying degrees of changes in their sense of smell (hyposmia/microsmia). For the following analysis, we pooled these categories as 'patients with anosmia.' Patients underwent screening for olfactory alterations associated with SARS-CoV-2 using the KOR test.25 In addition to self-reported olfactory alterations, 6 out of 43 patients with anosmia during the acute episode identified less than 5 odors, suggesting a persistent olfactory dysfunction (for details, see Methods). Despite this, when evaluating the KOR test scores with the model (Fig.2A), no group differences were observed (linear model dof=93, Anosmia: beta=0.39, se=0.24, t=1.6, r=0.19 [0.44 0.05], p=0.11; Hospitalization required: beta=0.14, se=0.24, t=0.5, r=0.07 [0.30 0.17], p=0.5). Functional capacity was also evaluated using 6MWT.26 We did not find differences in this score between groups (linear model dof=93, An beta=0.003, se=0.02, t=0.16, r=0.06 [0.17 0.29], p=0.8; HR beta=0.007, se=0.02, t=0.3, r=0.09 [0.13 0.32], p=0.7).

Patients were evaluated using cognitive and psychological assessment batteries. ACE-III evaluation showed that the sample had a mean score of 92 with no differences between groups (linear model, df=93, COVID-19 diagnosis beta=1.9, se=3.0, t=0.6, r=0.08 [0.33 0.17], p=0.5; Anosmia beta=3.2, se=2.6, t=1.1, r=0.15 [0.10 0.39], p=0.2; Hospitalization required beta=-3.0, se=2.7, t=1.0,r=0.13 [0.3 0.11], p=0.2). In the same way, IFS-Ch frontal screening evaluation showed a mean of 21.8 with no differences between groups (COVID-19 diagnosis beta=1.8, se=0.9, t=1.8, r=0.22 [0.02 0.46], p=0.06; Anosmia beta=0.4, se=0.8, t=0.4, r=0.05 [0.18 0.28], p=0.6; Hospitalization required beta=-0.37, se=0.8, t=0.4, r=0.05 [0.29 0.18], p=0.6). We found similar results when analyzing the PHQ-9 (COVID-19 diagnosis beta=2.0, se=1.4, t=1.4, r=0.18 [0.07 0.43], p=0.15; Anosmia beta=0.9, se=1.2, t=0.7, r=0.09 [0.34 0.15], p=0.4; Hospitalization required beta=-0.5, se=1.2, t=0.4, r=0.05 [0.29 0.19], p=0.6), and GAD-7 screenings (COVID-19 diagnosis beta=2.5, se=1.4, t=1.8, r=0.22 [0.02 0.47], p=0.07; Anosmia beta=0.2, se=1.2, t=0.1, r=0.02 [0.22 0.26], p=0.8; Hospitalization required beta=-0.07, se=1.2, t=0.06, r=0.007 [0.24 0.23], p=0.9).

Initially, we assessed whether participants adapted their behavior during the game (Fig.1). For this purpose, we analyzed three phases during the game: a phase we labeled as 'Shift,' which encompasses the five trials following the programmed probability change; a 'Pre-Shift' phase, comprising the last five trials of the initial stable phase of each game, and a final Post-Shift phase, representing the final five trials of each game (corresponding to the conclusion of the second stable phase, see Fig.1C).

All participants decreased their earnings during the Shift phase, but increased them in the Post-Shift phase, reflecting learning and adaptation (Friedman test, stat=44.8, df=2, Kendall W=0.22, p=2e10; mixed model over single trials, b=0.14, se=0.01, t=10.29, r=0.11 [0.130.09], p=2e16, Fig.1B). Subsequently, we investigated an indicator of the strategies individuals employ during transitions. To do so, we assessed the rate of alternative change following a negative outcome. An exceedingly low value in this indicator suggests a tendency for individuals to uphold the value of the chosen option after experiencing negative outcomes, a phenomenon known as perseverative decision-making27. Conversely, exceedingly high values in this indicator may signify impulsive shifts or the tendency to alter one's choice immediately following an error without updating the value. This indicator decreases in value during the Shift compared to the Pre-Shift phase, reflecting the tendency to accumulate more evidence before shifting from the previously advantageous option (linear model, averaged data: beta=0.04, se=0.01, t=2.6, r=0.11 [0.19 -0.03], p=0.008; mixed-effects logistic model over single trials: beta=0.24, se=0.05, t=4.4, r=0.11 [0.15 -0.06], p=9e-6). These initial analyses indicate that the entire sample exhibited the expected behavior in the task, adapting their decisions after a shift with a cost in the transition.

Next, we applied the strategy indicator during the Shift phase to investigate potential differences among groups using the model described in Fig.2A. We observed that the clinical characteristics of COVID-19 patients differentially influenced the strategy indicator. The diagnosis of COVID-19 did not significantly impact the indicator (linear model: beta=0.01, se=0.03, t=0.4, r=0.03 [0.13 0.20], p=0.6); however, patients requiring hospitalization exhibited a decrease in this parameter (linear model: beta=0.1, se=0.03, t=3.2, r=0.26 [0.43 -0.10], p=0.001, similar results from Bayesian estimation shown in Fig.2B). In contrast, patients presenting anosmia demonstrated an increase in this parameter (linear model: beta=0.09, se=0.03, t=2.9, r=0.25 [0.08 0.41], p=0.003, similar results from Bayesian estimation shown in Fig.2B). None of this modulation occurred in the other phase of the task (ps>0.1). This strategic modulation significantly impacted total earnings, leading to higher earnings among patients with anosmia (linear model, beta=0.02, se=0.01, t=2.43, r=0.10 [0.02 0.19], p=0.015).

Then, we test if this behavioral modulation is related to specific cognitive computation. We fitted a cognitive model of participants responses using prospect theory and a Rescorla-Wagner algorithm to estimate the individual learning of the probability of each desk. We used a different learning rate estimated following a win and a no-win. Based on the preceding results, we tested if the clinical condition of hospitalization and anosmia modulated the differences between the learning rates. We found a similar pattern to the prior results: COVID-19 diagnosis per se did not affect the learning rate (linear model df=90, b=0.1, se=0.2, t=0.6, r=0.1 [0.11 0.21], p=0.3), Hospitalization generated a decrease in the learning rate after negative outcome (b=0.44, se=0.16, t=2.7, r=0.21 [0.37 -0.06], p=0.007, similar results from Bayesian estimation shown in Fig.2C), and Anosmia presents an increased learning rate (b=0.4, se=0.15, t=2.5, r=0.2 [0.05 0.35], p=0.01, similar results from Bayesian estimation shown in Fig.2C).

In summary, participants adjusted to the changing probabilities, resulting in increased earnings following the decreases caused by the shift in probability. A behavioral indicator shows participants' ability to employ different strategies during reversals. Clinical characteristics of COVID-19-recovered patients influenced this indicator, with hospitalized patients decreasing and anosmic patients increasing, impacting total earnings. When testing specific cognitive computations, the modulation due to hospitalization affected the individual learning rate.

We evaluated the BOLD signal of the participants while they engaged in the Reversal Learning Task. Cognitive modeling was used to estimate the utility of the chosen option (see Materials and Methods). During the feedback period, we contrasted wins and no wins.

Initially, we assessed the consistent activity across the entire sample to identify the activity associated with value and feedback as classically described in this type of task. We found that during the decision-making process, the value of the chosen option correlated with an extensive frontal-parietal-striatal network, consistent with the literature, including ventromedial prefrontal, medial parietal, and striatal regions.28,29 Conversely, during feedback, we observed that the contrast between win and non-win revealed activity in the ventral striatum, consistent with prior research.29 Subsequently, we assessed the modulation of clinical parameters on BOLD activity. COVID-19 diagnosis and hospitalization required regressors did not show modulation in decision-related or feedback-related activity. However, the regressor associated with anosmia negatively modulated the BOLD signal during decision-making in a network that includes lateral prefrontal, medial frontal, and left temporoparietal regions.

The gray and white matter were segmented using T1w and T2w images. Cortical thickness was analyzed using the specified model in the methods (Fig.2A). We found that neither the COVID-19 diagnostic regressor nor the hospitalization requirement showed significant modulation in cortical thickness. However, the anosmia correlated with a thinning of the cortical thickness in parietal areas (Fig.3A).

Brain structural results. (A) The anosmia regressor effect over the cortical thickness. (B) The anosmia regressor effect over the fraction of anisotropy in a whole-brain analysis of white matter integrity. (C) Regressor effects over axial diffusivity measured in segmented white matter tracts. HR: hospitalization required, CTD: cluster-threshold detection, TFCE: threshold-free cluster enhancement.

The integrity of the white matter was assessed through diffusion images. First, we conducted a whole-brain analysis, evaluating changes in the fractional anisotropy (FA). Statistical modulations were calculated using the specified model outlined in the methods (Fig.2A), and cluster-based statistics were performed using TFCE. Anosmia was the only regressor with significant modulation, demonstrating decreased FA (Fig.3B). The main tracts involved in the affected areas were the corticospinal tract, arcuate fasciculus, inferior fronto-occipital fasciculus, thalamus-parietal fasciculus, thalamus-occipital fasciculus, and posterior corpus callosum.

Next, we conducted statistical analyses for individual tracts. Long and short fibers were segmented using deterministic tractography. Various diffusion measures were evaluated to assess the integrity of each tract (FA, radial diffusion, axial diffusion, and mean diffusion). Each tract was evaluated using the model specified in the methods. The analysis revealed that no modulation survived multiple comparisons (Bonferroni correction). However, when applying an uncorrected threshold (Z>3.1, commonly used for cluster detection in whole-brain functional and structural imaging studies), it was observed that frontal and parietal fascicles exhibited an increase in axial and mean diffusion, indicating a disruption in white matter integrity. This white matter integrity disruption correlated with the hospitalization requirement and COVID-19 diagnosis (Fig.3C).

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Patients recovering from COVID-19 who presented with anosmia during their acute episode have behavioral, functional, and structural brain alterations...