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Purpose: We present two patients who developed neurogenic stuttering after long COVID-19 related to SARS-CoV-2 infection. Methods and Results: Both patients experienced both physical (e.g., fatigue) and cognitive difficulties, which led to impaired function of attention, lexical retrieval, and memory consolidation. Both patients had new-onset stuttering-like speech dysfluencies: Blocks and repetitions were especially evident at the initial part of words and sentences, sometimes accompanied by effortful and associated movements (e.g., facial grimaces and oro-facial movements). Neuropsychological evaluations confirmed the presence of difficulties in cognitive tasks, while neurophysiological evaluations (i.e., electroencephalography) suggested the presence of “slowed” patterns of brain activity. Neurogenic stuttering and cognitive difficulties were evident for 4–5 months after negativization of SARS-CoV-2 nasopharyngeal swab, with gradual improvement and near-to-complete recovery. Conclusions: It is now evident that SARS-CoV-2 infection may significantly involve the central nervous system, also resulting in severe and long-term consequences, even if the precise mechanisms are still unknown. In the present report, long COVID-19 resulted in neurogenic stuttering, as the likely consequence of a “slowed” metabolism of (pre)frontal and sensorimotor brain regions (as suggested by the present and previous clinical evidence). As a consequence, the pathophysiological mechanisms related to the appearance of neurogenic stuttering have been hypothesized, which help to better understand the broader and possible neurological consequences of COVID-19.

Background and purpose: Among the most common post-COVID symptoms, many patients experienced subjective cognitive deficit, commonly named "brain fog," that might be present also in those individuals without severe acute COVID-19 respiratory involvement. Some studies have investigated some of the mechanisms that might be associated with the brain fog with objective techniques including transcranial magnetic stimulation and neuroimaging. Methods: The aim of this study was to investigate the presence of electroencephalographic (EEG) alterations in people with post-COVID self-reported cognitive deficit. Results: Out of the 90 patients attending the post-COVID neurology ambulatory service, twenty patients presenting brain fog at least 4 weeks after acute non-severe COVID-19 infection, and without previous history of epilepsy, were investigated with 19-channel EEG, Montreal Cognitive Assessment (MoCA), and magnetic resonance imaging (MRI). EEG was found altered in 65% of the sample, among which 69% presented a slowing activity and 31% were characterized by epileptic discharges principally in the frontal areas. None of the patients showed DWI MRI lesions. Conclusions: These findings highlight the usefulness of EEG analysis to objectively describe possible neurophysiological abnormalities in post-COVID patients presenting subjective cognitive deficit.

Objective In this phase II psychometric study on the Montreal cognitive assessment (MoCA), we tested the clinicometric properties of Italian norms for patients with mild cognitive impairment (PwMCI) and early dementia (PwD) and provided optimal cutoffs for diagnostic purposes. Methods Retrospective data collection was performed for consecutive patients with clinically and biologically defined MCI and early dementia. Forty-five patients (24 PwMCI and 21 PwD) and 25 healthy controls were included. Raw MoCA scores were adjusted according to the conventional 1-point correction (Nasreddine) and Italian norms (Conti, Santangelo, Aiello). The diagnostic properties of the original cutoff ( Results The original cutoff demonstrated high sensitivity (0.93 [95% CI 0.84–0.98]) but low specificity (0.44 [0.32–0.56]) in discriminating between patients and controls. Nominal normative cutoffs (ES0 uLs) showed excellent specificity (SP range = 0.96–1.00 [0.88–1.00]) but poor sensitivity (SE range = 0.09–0.24 [0.04–0.36]). The optimal cutoff for Nasreddine’s method was 23.50 (SE = 0.82 [0.71–0.90]; SP = 0.72 [0.60–0.82]). Optimal cutoffs were 20.97, 22.85, and 22.29 (SE range = 0.69–0.73 [0.57–0.83], SP range = 0.88–0.92 [0.77–0.97]) for Conti’s, Santangelo’s, and Aiello’s methods, respectively. Conclusion Using the 1-point correction, combined with a cutoff of 23.50, might be useful in ambulatory settings with a large turnout. Our optimal cutoffs can offset the poor sensitivity of Italian cutoffs.