Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19.

The risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function. Study registration: www.ClinicalTrials.gov , identifier NCT05307575.

Cognitive and emotional predictors of quality of life and functioning after COVID-19.

OBJECTIVE: A long-term decline in health-related quality of life (HRQoL) has been reported after coronavirus disease 2019 (COVID-19). Studies with people with persistent symptoms showed inconsistent outcomes. Cognition and emotion are important determinants in HRQoL, but few studies have examined their prognostic significance for HRQoL and functionality in post-COVID patients with persisting symptoms. We aimed to describe QoL, HRQoL, and functioning in individuals post-COVID with varying COVID-19 severities and to investigate the predictive value of cognitive and emotional variables for QoL, HRQoL, and functioning. METHODS: In total, 492 participants (398 post-COVID and 124 healthy controls) underwent a neurobehavioral examination that included assessments of cognition, mood, QoL/HRQoL (WHOQOL-BREF, EQ-5D), and functioning (WHODAS-II). Analysis of covariance and linear regression models were used to study intergroup differences and the relationship between cognitive and emotional variables and QoL and functioning. RESULTS: The Physical and Psychological dimensions of WHOQoL, EQ-5D, and WHODAS Cognition, Mobility, Life Activities, and Participation dimensions were significantly lower in post-COVID groups compared with a control group. Regression models explaining 23.9%-53.9% of variance were obtained for the WHOQoL-BREF dimensions and EQ-5D, with depressive symptoms, post-COVID symptoms, employment status, income, and mental speed processing as main predictors. For the WHODAS, models explaining 17%-60.2% of the variance were obtained. Fatigue, depressive symptoms, mental speed processing, and post-COVID symptoms were the main predictors. INTERPRETATION: QoL/HRQoL and functioning after COVID-19 in individuals with persistent symptoms were lower than in non-affected persons. Depressive symptoms, fatigue, and slower mental processing speed were predictors of lower QoL/HRQoL and functioning.

COVID-19 severity is related to poor executive function in people with post-COVID conditions.

Patients with post-coronavirus disease 2019 (COVID-19) conditions typically experience cognitive problems. Some studies have linked COVID-19 severity with long-term cognitive damage, while others did not observe such associations. This discrepancy can be attributed to methodological and sample variations. We aimed to clarify the relationship between COVID-19 severity and long-term cognitive outcomes and determine whether the initial symptomatology can predict long-term cognitive problems. Cognitive evaluations were performed on 109 healthy controls and 319 post-COVID individuals categorized into three groups according to the WHO clinical progression scale: severe-critical (n = 77), moderate-hospitalized (n = 73), and outpatients (n = 169). Principal component analysis was used to identify factors associated with symptoms in the acute-phase and cognitive domains. Analyses of variance and regression linear models were used to study intergroup differences and the relationship between initial symptomatology and long-term cognitive problems. The severe-critical group performed significantly worse than the control group in general cognition (Montreal Cognitive Assessment), executive function (Digit symbol, Trail Making Test B, phonetic fluency), and social cognition (Reading the Mind in the Eyes test). Five components of symptoms emerged from the principal component analysis: the "Neurologic/Pain/Dermatologic" "Digestive/Headache", "Respiratory/Fever/Fatigue/Psychiatric" and "Smell/ Taste" components were predictors of Montreal Cognitive Assessment scores; the "Neurologic/Pain/Dermatologic" component predicted attention and working memory; the "Neurologic/Pain/Dermatologic" and "Respiratory/Fever/Fatigue/Psychiatric" components predicted verbal memory, and the "Respiratory/Fever/Fatigue/Psychiatric," "Neurologic/Pain/Dermatologic," and "Digestive/Headache" components predicted executive function. Patients with severe COVID-19 exhibited persistent deficits in executive function. Several initial symptoms were predictors of long-term sequelae, indicating the role of systemic inflammation and neuroinflammation in the acute-phase symptoms of COVID-19." Study Registration: www.ClinicalTrials.gov , identifier NCT05307549 and NCT05307575.

Neuropsychological impairment in post-COVID condition individuals with and without cognitive complaints.

One of the most prevalent symptoms of post-COVID condition is cognitive impairment, which results in a significant degree of disability and low quality of life. In studies with large sample sizes, attention, memory, and executive function were reported as long-term cognitive symptoms. This study aims to describe cognitive dysfunction in large post-COVID condition individuals, compare objective neuropsychological performance in those post-COVID condition individuals with and without cognitive complaints, and identify short cognitive exams that can differentiate individuals with post-COVID symptoms from controls. To address these aims, the Nautilus project was started in June 2021. During the first year, we collected 428 participants' data, including 319 post-COVID and 109 healthy controls (18-65 years old) from those who underwent a comprehensive neuropsychological battery for cognitive assessment. Scores on tests assessing global cognition, learning and long-term memory, processing speed, language and executive functions were significantly worse in the post-COVID condition group than in healthy controls. Montreal Cognitive Assessment, digit symbol test, and phonetic verbal fluency were significant in the binomial logistic regression model and could effectively distinguish patients from controls with good overall sensitivity and accuracy. Neuropsychological test results did not differ between those with and without cognitive complaints. Our research suggests that patients with post-COVID conditions experience significant cognitive impairment and that routine tests like the Montreal Cognitive Assessment, digit symbol, and phonetic verbal fluency test might identify cognitive impairment. Thus, the administration of these tests would be helpful for all patients with post-COVID-19 symptoms, regardless of whether cognitive complaints are present or absent. Study registration: www.ClinicalTrials.gov, identifiers NCT05307549 and NCT05307575.

Supraspinal Shaping of Adaptive Transitions in the State of Functional Connectivity Between Segmentally Distributed Dorsal Horn Neuronal Populations in Response to Nociception and Antinociception.

In the anesthetized cat the correlation between the ongoing cord dorsum potentials (CDPs) recorded from different lumbar spinal segments has a non-random structure, suggesting relatively stable patterns of functional connectivity between the dorsal horn neuronal ensembles involved in the generation of these potentials. During the nociception induced by the intradermic injection of capsaicin, the patterns of segmental correlation between the spontaneous CDPs acquire other non-random configurations that are temporarily reversed to their pre-capsaicin state by the systemic injection of lidocaine, a procedure known to decrease the manifestation of neuropathic pain in both animals and humans. We have now extended these studies and utilized machine learning for the automatic extraction and selection of particular classes of CDPs according to their shapes and amplitudes. By using a Markovian analysis, we disclosed the transitions between the different kinds of CDPs induced by capsaicin and lidocaine and constructed a global model based on the changes in the behavior of the CDPs generated along the whole set of lumbar segments. This allowed the identification of the different states of functional connectivity within the whole ensemble of dorsal horn neurones attained during nociception and their transitory reversal by systemic administration of lidocaine in preparations with the intact neuroaxis and after spinalization. The present observations provide additional information on the state of self-organized criticality that leads to the adaptive behavior of the dorsal horn neuronal networks during nociception and antinociception both shaped by supraspinal descending influences.

Markovian Analysis of the Sequential Behavior of the Spontaneous Spinal Cord Dorsum Potentials Induced by Acute Nociceptive Stimulation in the Anesthetized Cat.

In a previous study we developed a Machine Learning procedure for the automatic identification and classification of spontaneous cord dorsum potentials (CDPs). This study further supported the proposal that in the anesthetized cat, the spontaneous CDPs recorded from different lumbar spinal segments are generated by a distributed network of dorsal horn neurons with structured (non-random) patterns of functional connectivity and that these configurations can be changed to other non-random and stable configurations after the noceptive stimulation produced by the intradermic injection of capsaicin in the anesthetized cat. Here we present a study showing that the sequence of identified forms of the spontaneous CDPs follows a Markov chain of at least order one. That is, the system has memory in the sense that the spontaneous activation of dorsal horn neuronal ensembles producing the CDPs is not independent of the most recent activity. We used this markovian property to build a procedure to identify portions of signals as belonging to a specific functional state of connectivity among the neuronal networks involved in the generation of the CDPs. We have tested this procedure during acute nociceptive stimulation produced by the intradermic injection of capsaicin in intact as well as spinalized preparations. Altogether, our results indicate that CDP sequences cannot be generated by a renewal stochastic process. Moreover, it is possible to describe some functional features of activity in the cord dorsum by modeling the CDP sequences as generated by a Markov order one stochastic process. Finally, these Markov models make possible to determine the functional state which produced a CDP sequence. The proposed identification procedures appear to be useful for the analysis of the sequential behavior of the ongoing CDPs recorded from different spinal segments in response to a variety of experimental procedures including the changes produced by acute nociceptive stimulation. They are envisaged as a useful tool to examine alterations of the patterns of functional connectivity between dorsal horn neurons under normal and different pathological conditions, an issue of potential clinical concern.

A machine learning methodology for the selection and classification of spontaneous spinal cord dorsum potentials allows disclosure of structured (non-random) changes in neuronal connectivity induced by nociceptive stimulation.

Previous studies aimed to disclose the functional organization of the neuronal networks involved in the generation of the spontaneous cord dorsum potentials (CDPs) generated in the lumbosacral spinal segments used predetermined templates to select specific classes of spontaneous CDPs. Since this procedure was time consuming and required continuous supervision, it was limited to the analysis of two specific types of CDPs (negative CDPs and negative positive CDPs), thus excluding potentials that may reflect activation of other neuronal networks of presumed functional relevance. We now present a novel procedure based in machine learning that allows the efficient and unbiased selection of a variety of spontaneous CDPs with different shapes and amplitudes. The reliability and performance of the present method is evaluated by analyzing the effects on the probabilities of generation of different classes of spontaneous CDPs induced by the intradermic injection of small amounts of capsaicin in the anesthetized cat, a procedure known to induce a state of central sensitization leading to allodynia and hyperalgesia. The results obtained with the selection method presently described allowed detection of spontaneous CDPs with specific shapes and amplitudes that are assumed to represent the activation of functionally coupled sets of dorsal horn neurones that acquire different, structured configurations in response to nociceptive stimuli. These changes are considered as responses tending to adequate transmission of sensory information to specific functional requirements as part of homeostatic adjustments.

Clustering algorithm for determining community structure in large networks.

We propose an algorithm to find the community structure in complex networks based on the combination of spectral analysis and modularity optimization. The clustering produced by our algorithm is as accurate as the best algorithms on the literature of modularity optimization; however, the main asset of the algorithm is its efficiency. The best match for our algorithm is Newman's fast algorithm, which is the reference algorithm for clustering in large networks due to its efficiency. When both algorithms are compared, our algorithm outperforms the fast algorithm both in efficiency and accuracy of the clustering, in terms of modularity. Thus, the results suggest that the proposed algorithm is a good choice to analyze the community structure of medium and large networks in the range of tens and hundreds of thousand vertices.