1
|
Casula EP, Esposito R, Dezi S, Ortelli P, Sebastianelli L, Ferrazzoli D, Saltuari L, Pezzopane V, Borghi I, Rocchi L, Ajello V, Trinka E, Oliviero A, Koch G, Versace V. Reduced TMS-evoked EEG oscillatory activity in cortical motor regions in patients with post-COVID fatigue. Clin Neurophysiol 2024; 165:26-35. [PMID: 38943790 DOI: 10.1016/j.clinph.2024.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/13/2024] [Accepted: 06/10/2024] [Indexed: 07/01/2024]
Abstract
OBJECTIVE Persistent fatigue is a major symptom of the so-called 'long-COVID syndrome', but the pathophysiological processes that cause it remain unclear. We hypothesized that fatigue after COVID-19 would be associated with altered cortical activity in premotor and motor regions. METHODS We used transcranial magnetic stimulation combined with EEG (TMS-EEG) to explore the neural oscillatory activity of the left primary motor area (l-M1) and supplementary motor area (SMA) in a group of sixteen post-COVID patients complaining of lingering fatigue as compared to a sample of age-matched healthy controls. Perceived fatigue was assessed with the Fatigue Severity Scale (FSS) and Fatigue Rating Scale (FRS). RESULTS Post-COVID patients showed a remarkable reduction of beta frequency in both areas. Correlation analysis exploring linear relation between neurophysiological and clinical measures revealed a significant inverse correlation between the individual level of beta oscillations evoked by TMS of SMA with the individual scores in the FRS (r(15) = -0.596; p = 0.012). CONCLUSIONS Post-COVID fatigue is associated with a reduction of TMS-evoked beta oscillatory activity in SMA. SIGNIFICANCE TMS-EEG could be used to identify early alterations of cortical oscillatory activity that could be related to the COVID impact in central fatigue.
Collapse
Affiliation(s)
- Elias P Casula
- Department of System Medicine, University of Tor Vergata, Via Cracovia 50, 00133, Rome, Italy; Experimental Neuropsychophysiology Laboratory, IRCCS Santa Lucia Foundation, Via Ardeatina 354, 00179, Rome, Italy
| | - Romina Esposito
- Experimental Neuropsychophysiology Laboratory, IRCCS Santa Lucia Foundation, Via Ardeatina 354, 00179, Rome, Italy
| | - Sabrina Dezi
- Department of Neurorehabilitation, Hospital of Vipiteno (SABES-ASDAA), Vipiteno-Sterzing, Italy, Teaching Hospital of the Paracelsus Medical Unversity (PMU), Salzburg, Austria; Teaching Hospital of the Paracelsus Medical University (PMU), Salzburg, Austria
| | - Paola Ortelli
- Department of Neurorehabilitation, Hospital of Vipiteno (SABES-ASDAA), Vipiteno-Sterzing, Italy, Teaching Hospital of the Paracelsus Medical Unversity (PMU), Salzburg, Austria; Teaching Hospital of the Paracelsus Medical University (PMU), Salzburg, Austria
| | - Luca Sebastianelli
- Department of Neurorehabilitation, Hospital of Vipiteno (SABES-ASDAA), Vipiteno-Sterzing, Italy, Teaching Hospital of the Paracelsus Medical Unversity (PMU), Salzburg, Austria; Teaching Hospital of the Paracelsus Medical University (PMU), Salzburg, Austria
| | - Davide Ferrazzoli
- Department of Neurorehabilitation, Hospital of Vipiteno (SABES-ASDAA), Vipiteno-Sterzing, Italy, Teaching Hospital of the Paracelsus Medical Unversity (PMU), Salzburg, Austria; Teaching Hospital of the Paracelsus Medical University (PMU), Salzburg, Austria
| | - Leopold Saltuari
- Department of Neurorehabilitation, Hospital of Vipiteno (SABES-ASDAA), Vipiteno-Sterzing, Italy, Teaching Hospital of the Paracelsus Medical Unversity (PMU), Salzburg, Austria; Teaching Hospital of the Paracelsus Medical University (PMU), Salzburg, Austria
| | - Valentina Pezzopane
- Experimental Neuropsychophysiology Laboratory, IRCCS Santa Lucia Foundation, Via Ardeatina 354, 00179, Rome, Italy
| | - Ilaria Borghi
- Experimental Neuropsychophysiology Laboratory, IRCCS Santa Lucia Foundation, Via Ardeatina 354, 00179, Rome, Italy
| | - Lorenzo Rocchi
- Department of Medical Sciences and Public Health, University of Cagliari, Via Università 40, 09124 Cagliari, Italy
| | - Valentina Ajello
- Department of Cardiac Anesthesia, University of Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
| | - Eugen Trinka
- Department of Neurology, Neurocritical Care and Neurorehabilitation, Christian Doppler University Hospital, Centre for Cognitive Neuroscience, Paracelsus Medical University, Member of the European Reference Network EpiCARE, Salzburg, Ignaz-Harrer-Straße 79, 5020 Salzburg, Austria; Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University and Center for Cognitive Neuroscience, Ignaz-Harrer-Straße 79, 5020 Salzburg, Austria; Karl Landsteiner Institute of Neurorehabilitation and Space Neurology, Julius Raab-Promenade 49/1, 3100 St. Pölten, Salzburg, Austria
| | - Antonio Oliviero
- FENNSI Group, Hospital Nacional de Parapléjicos, SESCAM, FINCA DE, Carr. de la Peraleda, S/N, 45004 Toledo, Spain; Center for Clinical Neuroscience, Hospital Los Madroños, M-501 Km 17, 900 - 28690 Brunete, Spain
| | - Giacomo Koch
- Experimental Neuropsychophysiology Laboratory, IRCCS Santa Lucia Foundation, Via Ardeatina 354, 00179, Rome, Italy; Department of Neuroscience and Rehabilitation, University of Ferrara, Via Ludovico Ariosto 35, 44121 Ferrara, Italy
| | - Viviana Versace
- Department of Neurorehabilitation, Hospital of Vipiteno (SABES-ASDAA), Vipiteno-Sterzing, Italy, Teaching Hospital of the Paracelsus Medical Unversity (PMU), Salzburg, Austria; Teaching Hospital of the Paracelsus Medical University (PMU), Salzburg, Austria; Department of Neurology, Neurocritical Care and Neurorehabilitation, Christian Doppler University Hospital, Centre for Cognitive Neuroscience, Paracelsus Medical University, Member of the European Reference Network EpiCARE, Salzburg, Ignaz-Harrer-Straße 79, 5020 Salzburg, Austria.
| |
Collapse
|
2
|
Pascarella A, Manzo L, Ferlazzo E. Modern neurophysiological techniques indexing normal or abnormal brain aging. Seizure 2024:S1059-1311(24)00194-8. [PMID: 38972778 DOI: 10.1016/j.seizure.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/01/2024] [Indexed: 07/09/2024] Open
Abstract
Brain aging is associated with a decline in cognitive performance, motor function and sensory perception, even in the absence of neurodegeneration. The underlying pathophysiological mechanisms remain incompletely understood, though alterations in neurogenesis, neuronal senescence and synaptic plasticity are implicated. Recent years have seen advancements in neurophysiological techniques such as electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP) and transcranial magnetic stimulation (TMS), offering insights into physiological and pathological brain aging. These methods provide real-time information on brain activity, connectivity and network dynamics. Integration of Artificial Intelligence (AI) techniques promise as a tool enhancing the diagnosis and prognosis of age-related cognitive decline. Our review highlights recent advances in these electrophysiological techniques (focusing on EEG, ERP, TMS and TMS-EEG methodologies) and their application in physiological and pathological brain aging. Physiological aging is characterized by changes in EEG spectral power and connectivity, ERP and TMS parameters, indicating alterations in neural activity and network function. Pathological aging, such as in Alzheimer's disease, is associated with further disruptions in EEG rhythms, ERP components and TMS measures, reflecting underlying neurodegenerative processes. Machine learning approaches show promise in classifying cognitive impairment and predicting disease progression. Standardization of neurophysiological methods and integration with other modalities are crucial for a comprehensive understanding of brain aging and neurodegenerative disorders. Advanced network analysis techniques and AI methods hold potential for enhancing diagnostic accuracy and deepening insights into age-related brain changes.
Collapse
Affiliation(s)
- Angelo Pascarella
- Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy.
| | - Lucia Manzo
- Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy
| | - Edoardo Ferlazzo
- Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy
| |
Collapse
|
3
|
Palmisano A, Pandit S, Smeralda CL, Demchenko I, Rossi S, Battelli L, Rivolta D, Bhat V, Santarnecchi E. The Pathophysiological Underpinnings of Gamma-Band Alterations in Psychiatric Disorders. Life (Basel) 2024; 14:578. [PMID: 38792599 PMCID: PMC11122172 DOI: 10.3390/life14050578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 05/26/2024] Open
Abstract
Investigating the biophysiological substrates of psychiatric illnesses is of great interest to our understanding of disorders' etiology, the identification of reliable biomarkers, and potential new therapeutic avenues. Schizophrenia represents a consolidated model of γ alterations arising from the aberrant activity of parvalbumin-positive GABAergic interneurons, whose dysfunction is associated with perineuronal net impairment and neuroinflammation. This model of pathogenesis is supported by molecular, cellular, and functional evidence. Proof for alterations of γ oscillations and their underlying mechanisms has also been reported in bipolar disorder and represents an emerging topic for major depressive disorder. Although evidence from animal models needs to be further elucidated in humans, the pathophysiology of γ-band alteration represents a common denominator for different neuropsychiatric disorders. The purpose of this narrative review is to outline a framework of converging results in psychiatric conditions characterized by γ abnormality, from neurochemical dysfunction to alterations in brain rhythms.
Collapse
Affiliation(s)
- Annalisa Palmisano
- Chair of Lifespan Developmental Neuroscience, Faculty of Psychology, TUD Dresden University of Technology, 01069 Dresden, Germany
- Precision Neuroscience and Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA (E.S.)
- Department of Education, Psychology, and Communication, University of Bari Aldo Moro, 70121 Bari, Italy;
| | - Siddhartha Pandit
- Precision Neuroscience and Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA (E.S.)
| | - Carmelo L. Smeralda
- Precision Neuroscience and Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA (E.S.)
- Siena Brain Investigation and Neuromodulation (SI-BIN) Laboratory, Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, 53100 Siena, Italy;
| | - Ilya Demchenko
- Interventional Psychiatry Program, St. Michael’s Hospital—Unity Health Toronto, Toronto, ON M5B 1W8, Canada; (I.D.)
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Simone Rossi
- Siena Brain Investigation and Neuromodulation (SI-BIN) Laboratory, Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, 53100 Siena, Italy;
| | - Lorella Battelli
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
- Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Davide Rivolta
- Department of Education, Psychology, and Communication, University of Bari Aldo Moro, 70121 Bari, Italy;
| | - Venkat Bhat
- Interventional Psychiatry Program, St. Michael’s Hospital—Unity Health Toronto, Toronto, ON M5B 1W8, Canada; (I.D.)
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Emiliano Santarnecchi
- Precision Neuroscience and Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA (E.S.)
- Department of Neurology and Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| |
Collapse
|
4
|
Easwaran K, Ramakrishnan K, Jeyabal SN. Classification of cognitive impairment using electroencephalography for clinical inspection. Proc Inst Mech Eng H 2024; 238:358-371. [PMID: 38366360 DOI: 10.1177/09544119241228912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
Impairment in cognitive skill though set-in due to various diseases, its progress is based on neuronal degeneration. In general, cognitive impairment (CI) is divided into three stages: mild, moderate and severe. Quantification of CI is important for deciding/changing therapy. Attempted in this work is to quantify electroencephalograph (EEG) signal and group it into four classes (controls and three stages of CI). After acquiring resting state EEG signal from the participants, non-local and local synchrony measures are derived from phase amplitude coupling and phase locking value. This totals to 160 features per individual for each task. Two types of classification networks are constructed. The first one is an artificial neural network (ANN) that takes derived features and gives a maximum accuracy of 85.11%. The second network is convolutional neural network (CNN) for which topographical images constructed from EEG features becomes the input dataset. The network is trained with 60% of data and then tested with remaining 40% of data. This process is performed in 5-fold technique, which yields an average accuracy of 94.75% with only 30 numbers of inputs for every individual. The result of the study shows that CNN outperforms ANN with a relatively lesser number of inputs. From this it can be concluded that this method proposes a simple task for acquiring EEG (which can be done by CI subjects) and quantifies CI stages with no overlapping between control and test group, thus making it possible for identifying early symptoms of CI.
Collapse
Affiliation(s)
- Karuppathal Easwaran
- Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India
| | - Kalpana Ramakrishnan
- Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India
| | | |
Collapse
|
5
|
Hall PA, Burhan AM, MacKillop JC, Duarte D. Next-generation cognitive assessment: Combining functional brain imaging, system perturbations and novel equipment interfaces. Brain Res Bull 2023; 204:110797. [PMID: 37875208 DOI: 10.1016/j.brainresbull.2023.110797] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/14/2023] [Accepted: 10/19/2023] [Indexed: 10/26/2023]
Abstract
Conventional cognitive assessment is widely used in clinical and research settings, in educational institutions, and in the corporate world for personnel selection. Such approaches involve having a client, a patient, or a research participant complete a series of standardized cognitive tasks in order to challenge specific and global cognitive abilities, and then quantify performance for the desired end purpose. The latter may include a diagnostic confirmation of a disease, description of a state or ability, or matching cognitive characteristics to a particular occupational role requirement. Metrics derived from cognitive assessments are putatively informative about important features of the brain and its function. For this reason, the research sector also makes use of cognitive assessments, most frequently as a stimulus for cognitive activity from which to extract functional neuroimaging data. Such "task-related activations" form the core of the most widely used neuroimaging technologies, such as fMRI. Much of what we know about the brain has been drawn from the interleaving of cognitive assessments of various types with functional brain imaging technologies. Despite innovation in neuroimaging (i.e., quantifying the neural response), relatively little innovation has occurred on task presentation and volitional response measurement; yet these together comprise the core of cognitive performance. Moreover, even when cognitive assessment is interleaved with functional neuroimaging, this is most often undertaken in the research domain, rather than the primary applications of cognitive assessment in diagnosis and monitoring, education and personnel selection. There are new ways in which brain imaging-and even more importantly, brain modulation-technologies can be combined with automation and artificial intelligence to deliver next-generation cognitive assessment methods. In this review paper, we describe some prototypes for how this can be done and identify important areas for progress (technological and otherwise) to enable it to happen. We will argue that the future of cognitive assessment will include semi- and fully-automated assessments involving neuroimaging, standardized perturbations via neuromodulation technologies, and artificial intelligence. Furthermore, the fact that cognitive assessments take place in a social/interpersonal context-normally between the patient and clinician-makes the human-machine interface consequential, and this will also be discussed.
Collapse
Affiliation(s)
- Peter A Hall
- School of Public Health Sciences, Faculty of Health, University of Waterloo, Waterloo, Ontario, Canada; Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, Ontario, Canada.
| | - Amer M Burhan
- Ontario Shores Centre for Mental Health Sciences, Whitby, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - James C MacKillop
- Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Dante Duarte
- Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada; Seniors Mental Health Program, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| |
Collapse
|
6
|
Ajra Z, Xu B, Dray G, Montmain J, Perrey S. Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia. Front Neurol 2023; 14:1270405. [PMID: 37900600 PMCID: PMC10602655 DOI: 10.3389/fneur.2023.1270405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
Introduction Dementia is a neurological disorder associated with aging that can cause a loss of cognitive functions, impacting daily life. Alzheimer's disease (AD) is the most common cause of dementia, accounting for 50-70% of cases, while frontotemporal dementia (FTD) affects social skills and personality. Electroencephalography (EEG) provides an effective tool to study the effects of AD on the brain. Methods In this study, we propose to use shallow neural networks applied to two sets of features: spectral-temporal and functional connectivity using four methods. We compare three supervised machine learning techniques to the CNN models to classify EEG signals of AD / FTD and control cases. We also evaluate different measures of functional connectivity from common EEG frequency bands considering multiple thresholds. Results and discussion Results showed that the shallow CNN-based models achieved the highest accuracy of 94.54% with AEC in test dataset when considering all connections, outperforming conventional methods and providing potentially an additional early dementia diagnosis tool.
Collapse
Affiliation(s)
- Zaineb Ajra
- EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France
| | - Binbin Xu
- EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France
| | - Gérard Dray
- EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France
| | - Jacky Montmain
- EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France
| | - Stéphane Perrey
- EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France
| |
Collapse
|