Neurology

Early postmortem brain MRI findings in COVID-19 non-survivors

1. Tim Coolen
2. Valentina Lolli
3. Niloufar Sadeghi
4. Antonin Rovaï
5. Nicola Trotta
6. Fabio Silvio Taccone
7. Jacques Creteur
8. Sophie Henrard
9. Jean-Christophe Goffard
10. Olivier De Witte
11. Gilles Naeije
12. Serge Goldman
13. Xavier De Tiège

Reviewed by ScreenIT

COVID-19 in Children with Brain-Based Developmental Disabilities: A Rapid Review

1. Michèle Dugas
2. Valérie Carnovale
3. Andrée-Anne Poirier
4. Benoit Mailhot
5. Becky Skidmore
6. Lena Faust
7. Carrie Costello
8. Donna Thomson
9. Annette Majnemer
10. Dan Goldowitz
11. Steven P. Miller
12. Annie LeBlanc

Reviewed by ScreenIT

Clinical characteristics and outcomes of inpatients with neurologic disease and COVID-19 in Brescia, Lombardy, Italy

1. Alberto Benussi
2. Andrea Pilotto
3. Enrico Premi
4. Ilenia Libri
5. Marcello Giunta
6. Chiara Agosti
7. Antonella Alberici
8. Enrico Baldelli
9. Matteo Benini
10. Sonia Bonacina
11. Laura Brambilla
12. Salvatore Caratozzolo
13. Matteo Cortinovis
14. Angelo Costa
15. Stefano Cotti Piccinelli
16. Elisabetta Cottini
17. Viviana Cristillo
18. Ilenia Delrio
19. Massimiliano Filosto
20. Massimo Gamba
21. Stefano Gazzina
22. Nicola Gilberti
23. Stefano Gipponi
24. Alberto Imarisio
25. Paolo Invernizzi
26. Ugo Leggio
27. Matilde Leonardi
28. Paolo Liberini
29. Martina Locatelli
30. Stefano Masciocchi
31. Loris Poli
32. Renata Rao
33. Barbara Risi
34. Luca Rozzini
35. Andrea Scalvini
36. Francesca Schiano di Cola
37. Raffaella Spezi
38. Veronica Vergani
39. Irene Volonghi
40. Nicola Zoppi
41. Barbara Borroni
42. Mauro Magoni
43. Alessandro Pezzini
44. Alessandro Padovani

Reviewed by ScreenIT

Methods of An Open - Label Proof - of - Concept Trial of Intravenous Valproic Acid for Severe COVID-19

1. Erwin Chiquete
2. Liz Toapanta-Yanchapaxi
3. Carlos Cantú-Brito

Reviewed by ScreenIT

Two patients with acute meningoencephalitis concomitant with SARS‐CoV‐2 infection

1. R. Bernard‐Valnet
2. B. Pizzarotti
3. A. Anichini
4. Y. Demars
5. E. Russo
6. M. Schmidhauser
7. J. Cerutti‐Sola
8. A. O. Rossetti
9. R. Du Pasquier

Reviewed by ScreenIT

Steroid‐Responsive Encephalitis in Coronavirus Disease 2019

1. Andrea Pilotto
2. Silvia Odolini
3. Stefano Masciocchi
4. Agnese Comelli
5. Irene Volonghi
6. Stefano Gazzina
7. Sara Nocivelli
8. Alessandro Pezzini
9. Emanuele Focà
10. Arnaldo Caruso
11. Matilde Leonardi
12. Maria P. Pasolini
13. Roberto Gasparotti
14. Francesco Castelli
15. Nicholas J. Ashton
16. Kaj Blennow
17. Henrik Zetterberg
18. Alessandro Padovani

Reviewed by ScreenIT

Acute myelitis after SARS-CoV-2 infection: a case report

1. Kang Zhao
2. Jucun Huang
3. Dan Dai
4. Yuwei Feng
5. Liming Liu
6. Shuke Nie

Reviewed by ScreenIT

The evaluation of sleep disturbances for Chinese frontline medical workers under the outbreak of COVID-19

1. Jing Qi
2. Jing Xu
3. Bo-Zhi Li
4. Jin-Sha Huang
5. Yuan Yang
6. Zhen-Tao Zhang
7. Dong-Ai Yao
8. Qun-Hui Liu
9. Min Jia
10. Dao-Kai Gong
11. Xiao-Hong Ni
12. Qi-Mei Zhang
13. Fu-Rong Shang
14. Nian Xiong
15. Chun-Li Zhu
16. Tao Wang
17. Xi Zhang

Reviewed by ScreenIT

Conscious processing of global and local auditory irregularities causes differentiated heartbeat-evoked responses

1. Diego Candia-Rivera
2. Federico Raimondo
3. Pauline Pérez
4. Lionel Naccache
5. Catherine Tallon-Baudry
6. Jacobo D Sitt

• Curated by eLife

  eLife assessment

  Motivated by previous demonstrations that cognitive modulation of heart beat evoked responses (HER) might distinguish minimally consciousness state and unresponsive wakefulness syndrome patients, the present work sought to determine whether contextual processing of auditory regularities (local-global paradigm) differentially affects HER in these patient groups. The results provide preliminary evidence for the usefulness of EEG and oddball paradigms in informing diagnosis of the state of consciousness. This paper will be of interest to those researchers studying signs of consciousness in post-comatose patients and more broadly to those studying brain-body interactions. However, some aspects of the study design and data analysis need to be clarified, particularly as these affect the conclusions that can be drawn.

Reviewed by eLife

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex

1. Irene Cumplido-Mayoral
2. Marina García-Prat
3. Grégory Operto
4. Carles Falcon
5. Mahnaz Shekari
6. Raffaele Cacciaglia
7. Marta Milà-Alomà
8. Luigi Lorenzini
9. Silvia Ingala
10. Alle Meije Wink
11. Henk JMM Mutsaerts
12. Carolina Minguillón
13. Karine Fauria
14. José Luis Molinuevo
15. Sven Haller
16. Gael Chetelat
17. Adam Waldman
18. Adam J Schwarz
19. Frederik Barkhof
20. Ivonne Suridjan
21. Gwendlyn Kollmorgen
22. Anna Bayfield
23. Henrik Zetterberg
24. Kaj Blennow
25. Marc Suárez-Calvet
26. Verónica Vilaplana
27. Juan Domingo Gispert
28. ALFA study
29. EPAD study
30. ADNI study
31. OASIS study

• Curated by eLife

  eLife assessment

  The study has some significance for the field of dementia research and neurodegenerative diseases more broadly. Using the brain-age paradigm, the main findings are that having an older-appearing brain is associated with more advanced stages of amyloid and tau pathology, higher white matter hyperintensities, higher plasma NfL and carrying the APOE 34 allele. Findings were broadly similar in cognitively normal people and people with mild cognitive impairment and the evidence for these findings is convincing. Although sex differences are emphasized, the evidence for this is generally incomplete.

Reviewed by eLife