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Jin J, Zheng Q, Liu H, Feng K, Bai Y, Ni G. Musical experience enhances time discrimination: Evidence from cortical responses. Ann N Y Acad Sci 2024; 1536:167-176. [PMID: 38829709 DOI: 10.1111/nyas.15153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Time discrimination, a critical aspect of auditory perception, is influenced by numerous factors. Previous research has suggested that musical experience can restructure the brain, thereby enhancing time discrimination. However, this phenomenon remains underexplored. In this study, we seek to elucidate the enhancing effect of musical experience on time discrimination, utilizing both behavioral and electroencephalogram methodologies. Additionally, we aim to explore, through brain connectivity analysis, the role of increased connectivity in brain regions associated with auditory perception as a potential contributory factor to time discrimination induced by musical experience. The results show that the music-experienced group demonstrated higher behavioral accuracy, shorter reaction time, and shorter P3 and mismatch response latencies as compared to the control group. Furthermore, the music-experienced group had higher connectivity in the left temporal lobe. In summary, our research underscores the positive impact of musical experience on time discrimination and suggests that enhanced connectivity in brain regions linked to auditory perception may be responsible for this enhancement.
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Affiliation(s)
- Jiaqi Jin
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Qi Zheng
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Hongxing Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Kunyun Feng
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yanru Bai
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Guangjian Ni
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
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2
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Liuzzi P, Cassioli T, Secci S, Hakiki B, Scarpino M, Burali R, di Palma A, Toci T, Grippo A, Cecchi F, Frosini A, Mannini A. A neurophysiological profiling of the heartbeat-evoked potential in severe acquired brain injuries: A focus on unconsciousness. Eur J Neurosci 2024. [PMID: 38797841 DOI: 10.1111/ejn.16394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 05/29/2024]
Abstract
Unconsciousness in severe acquired brain injury (sABI) patients occurs with different cognitive and neural profiles. Perturbational approaches, which enable the estimation of proxies for brain reorganization, have added a new avenue for investigating the non-behavioural diagnosis of consciousness. In this prospective observational study, we conducted a comparative analysis of the topological patterns of heartbeat-evoked potentials (HEP) between patients experiencing a prolonged disorder of consciousness (pDoC) and patients emerging from a minimally consciousness state (eMCS). A total of 219 sABI patients were enrolled, each undergoing a synchronous EEG-ECG resting-state recording, together with a standardized consciousness diagnosis. A number of graph metrics were computed before/after the HEP (Before/After) using the R-peak on the ECG signal. The peak value of the global field power of the HEP was found to be significantly higher in eMCS patients with no difference in latency. Power spectrum was not able to discriminate consciousness neither Before nor After. Node assortativity and global efficiency were found to vary with different trends at unconsciousness. Lastly, the Perturbational Complexity Index of the HEP was found to be significantly higher in eMCS patients compared with pDoC. Given that cortical elaboration of peripheral inputs may serve as a non-behavioural determinant of consciousness, we have devised a low-cost and translatable technique capable of estimating causal proxies of brain functionality with an endogenous, non-invasive stimulus. Thus, we present an effective means to enhance consciousness assessment by incorporating the interaction between the autonomic nervous system (ANS) and central nervous system (CNS) into the loop.
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Affiliation(s)
- Piergiuseppe Liuzzi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
- Istituto di BioRobotica, Scuola Superiore Sant'Anna, Pisa, Italy
| | | | - Sara Secci
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
| | - Bahia Hakiki
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
- Dipartimento di Medicina Sperimentale e Clinica, Università di Firenze, Florence, Italy
| | | | - Rachele Burali
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
| | | | - Tanita Toci
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
| | | | - Francesca Cecchi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
- Dipartimento di Medicina Sperimentale e Clinica, Università di Firenze, Florence, Italy
| | - Andrea Frosini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
- Dipartimento di Matematica Ulisse Dini, Università di Firenze, Florence, Italy
| | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
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3
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Lanzone J, Motolese F, Ricci L, Tecchio F, Tombini M, Zappasodi F, Cruciani A, Capone F, Di Lazzaro V, Assenza G. Quantitative measures of the resting EEG in stroke: a systematic review on clinical correlation and prognostic value. Neurol Sci 2023; 44:4247-4261. [PMID: 37542545 DOI: 10.1007/s10072-023-06981-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 07/26/2023] [Indexed: 08/07/2023]
Abstract
OBJECT Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. We systematically reviewed published papers that focus on qEEG metrics in the resting EEG of patients with mono-hemispheric stroke, to summarize current knowledge and pave the way for future research. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched the literature for papers that fitted our inclusion criteria. Rayyan QCRR was used to allow deduplication and collaborative blinded paper review. Due to multiple outcomes and non-homogeneous literature, a scoping review approach was used to address the topic. RESULTS Or initial search (PubMed, Embase, Google scholar) yielded 3200 papers. After proper screening, we selected 71 papers that fitted our inclusion criteria and we developed a scoping review thar describes the current state of the art of qEEG in stroke. Notably, among selected papers 53 (74.3%) focused on spectral power; 11 (15.7%) focused on symmetry indexes, 17 (24.3%) on connectivity metrics, while 5 (7.1%) were about other metrics (e.g. detrended fluctuation analysis). Moreover, 42 (58.6%) studies were performed with standard 19 electrodes EEG caps and only a minority used high-definition EEG. CONCLUSIONS We systematically assessed major findings on qEEG and stroke, evidencing strengths and potential pitfalls of this promising branch of research.
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Affiliation(s)
- J Lanzone
- Istituti Clinici Scientifici Maugeri IRCCS, Neurorehabilitation Department of the Milano Institute, Milan, Italy.
| | - F Motolese
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - L Ricci
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - F Tecchio
- Laboratory of Electrophysiology for Translational Neuroscience LET'S, Institute of Cognitive Sciences and Technologies ISTC, Consiglio Nazionale Delle Ricerche CNR, Rome, Italy
| | - M Tombini
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - F Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences and Institute for Advanced Biomedical Technologies, 'Gabriele D'Annunzio' University, Chieti, Italy
| | - A Cruciani
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - F Capone
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - V Di Lazzaro
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
| | - G Assenza
- Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Roma, Italy
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Hatlestad-Hall C, Bruña R, Liljeström M, Renvall H, Heuser K, Taubøll E, Maestú F, Haraldsen IH. Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough? Clin Neurophysiol 2023; 150:1-16. [PMID: 36972647 DOI: 10.1016/j.clinph.2023.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/03/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities. METHODS EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested. RESULTS The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated. CONCLUSIONS Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data. SIGNIFICANCE Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.
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Affiliation(s)
| | - Ricardo Bruña
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Radiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Kjell Heuser
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway; BrainSymph AS, Oslo, Norway
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Miraglia F, Pappalettera C, Guglielmi V, Cacciotti A, Manenti R, Judica E, Vecchio F, Rossini PM. The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment (MCI) condition. GeroScience 2023:10.1007/s11357-023-00733-5. [PMID: 36692591 PMCID: PMC10400506 DOI: 10.1007/s11357-023-00733-5] [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: 09/09/2022] [Accepted: 01/10/2023] [Indexed: 01/25/2023] Open
Abstract
Hyperventilation (HV) is a voluntary activity that causes changes in the neuronal firing characteristics noticeable in the electroencephalogram (EEG) signals. HV-related changes have been scribed to modulation of pO2/pCO2 blood contents. Therefore, an HV test is routinely used for highlighting brain abnormalities including those depending to neurobiological mechanisms at the basis of neurodegenerative disorders. The main aim of the present paper is to study the effectiveness of HV test in modifying the functional connectivity from the EEG signals that can be typical of a prodromal state of Alzheimer's disease (AD), the Mild Cognitive Impairment prodromal to Alzheimer condition. MCI subjects and a group of age-matched healthy elderly (Ctrl) were enrolled and subjected to EEG recording during HV, eyes-closed (EC), and eyes-open (EO) conditions. Since the cognitive decline in MCI seems to be a progressive disconnection syndrome, the approach we used in the present study is the graph theory, which allows to describe brain networks with a series of different parameters. Small world (SW), modularity (M), and global efficiency (GE) indexes were computed among the EC, EO, and HV conditions comparing the MCI group to the Ctrl one. All the three graph parameters, computed in the typical EEG frequency bands, showed significant changes among the three conditions, and more interestingly, a significant difference in the GE values between the MCI group and the Ctrl one was obtained, suggesting that the combination of HV test and graph theory parameters should be a powerful tool for the detection of possible cerebral dysfunction and alteration.
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Affiliation(s)
- Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy.
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy
| | - Valeria Guglielmi
- Dipartimento Neuroscienze, Organi di Senso e Torace, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Alessia Cacciotti
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy
| | - Rosa Manenti
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Elda Judica
- Department of Neurorehabilitation Sciences, Casa di Cura IGEA, Milano, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
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Ferreri F, Francesca M, Fabrizio V, Manzo N, Maria C, Elda J, Rossini PM. EEG, ERPs, and EROs in patients with neurodegenerative dementing disorders: A window into the cortical neurophysiology of cognition and behavior. Int J Psychophysiol 2022; 181:85-94. [PMID: 36055410 DOI: 10.1016/j.ijpsycho.2022.08.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/20/2022] [Accepted: 08/18/2022] [Indexed: 10/31/2022]
Abstract
In the human brain, physiological aging is characterized by progressive neuronal loss, leading to disruption of synapses and to a degree of failure in neurotransmission and information flow. However, there is increasing evidence to support the notion that the aged brain has a remarkable level of resilience (i.s. ability to reorganize itself), with the aim of preserving its physiological activity. It is therefore of paramount interest to develop objective markers able to characterize the biological processes underlying brain aging in the intact human, and to distinguish them from brain degeneration associated to age-related neurological progressive diseases like Alzheimer's disease. EEG, alone and combined with transcranial magnetic stimulation (TMS-EEG), is particularly suited to this aim, due to the functional nature of the information provided, and thanks to the ease with which it can be integrated in ecological scenarios including behavioral tasks. In this review, we aimed to provide the reader with updated information about the role of modern methods of EEG and TMS-EEG analysis in the investigation of physiological brain aging and Alzheimer's disease. In particular, we focused on data about cortical connectivity obtained by using readouts such graph theory network brain organization and architecture, and transcranial evoked potentials (TEPs) during TMS-EEG. Overall, findings in the literature support an important potential contribution of such neurophysiological techniques to the understanding of the mechanisms underlying normal brain aging and the early (prodromal/pre-symptomatic) stages of dementia.
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Affiliation(s)
- Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology and Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy; Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Miraglia Francesca
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy; Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
| | - Vecchio Fabrizio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy; Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Nicoletta Manzo
- IRCCS San Camillo Hospital, Via Alberoni 70, 30126 Lido di Venezia, Venice, Italy
| | - Cotelli Maria
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di DioFatebenefratelli, Brescia, Italy
| | - Judica Elda
- Department of Neurorehabilitation Sciences, Casa Cura Policlinico, Milano, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
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Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy. GeroScience 2022; 44:1599-1607. [PMID: 35344121 DOI: 10.1007/s11357-022-00552-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/19/2022] [Indexed: 11/04/2022] Open
Abstract
The objective of the present study is to explore the brain resting state differences between Parkinson's disease (PD) patients and age- and gender-matched healthy controls (elderly) in terms of complexity of electroencephalographic (EEG) signals. One non-linear approach to determine the complexity of EEG is the entropy. In this pilot study, 28 resting state EEGs were analyzed from 13 PD patients and 15 elderly subjects, applying approximate entropy (ApEn) analysis to EEGs in ten regions of interest (ROIs), five for each brain hemisphere (frontal, central, parietal, occipital, temporal). Results showed that PD patients presented statistically higher ApEn values than elderly confirming the hypothesis that PD is characterized by a remarkable modification of brain complexity and globally modifies the underlying organization of the brain. The higher-than-normal entropy of PD patients may describe a condition of low order and consequently low information flow due to an alteration of cortical functioning and processing of information. Understanding the dynamics of brain applying ApEn could be a useful tool to help in diagnosis, follow the progression of Parkinson's disease, and set up personalized rehabilitation programs.
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Neuronavigated Magnetic Stimulation combined with cognitive training for Alzheimer's patients: an EEG graph study. GeroScience 2021; 44:159-172. [PMID: 34970718 DOI: 10.1007/s11357-021-00508-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 12/21/2021] [Indexed: 10/19/2022] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder in elderly subjects. Recent studies verified the effects of cognitive training combined with repetitive transcranial magnetic stimulation (rTMS-COG) in AD patients. Here, we analyzed neuropsychological and neurophysiological data, derived from electroencephalography (EEG), to evaluate the effects of a 6-week protocol of rTMS-COG in 72 AD. We designed a randomized, double-blind, sham-controlled trial to evaluate efficacy of rTMS on 6 brain regions obtained by an individual MRI combined with COG related to brain areas to stimulate (i.e., syntax and grammar tasks, comprehension of lexical meaning and categorization tasks, action naming, object naming, spatial memory, spatial attention). Patients underwent neuropsychological and EEG examination before (T0), after treatment (T1), and after 40 weeks (T2), to evaluate the effects of rehabilitation therapy. "Small World" (SW) graph approach was introduced allowing us to model the architecture of brain connectivity in order to correlate it with cognitive improvements. We found that following 6 weeks of intensive daily treatment the immediate results showed an improvement in cognitive scales among AD patients. SW present no differences before and after the treatment, whereas a crucial SW modulation emerges at 40-week follow-up, emphasizing the importance of rTMS-COG rehabilitation treatment for AD. Additional results demonstrated that the delta and alpha1 SW seem to be diagnostic biomarkers of AD, whereas alpha2 SW might represent a prognostic biomarker of cognitive recovery. Derived EEG parameters can be awarded the role of diagnostic and predictive biomarkers of AD progression, and rTMS-COG can be regarded as a potentially useful treatment for AD.
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Miraglia F, Vecchio F, Pellicciari MC, Cespon J, Rossini PM. Brain Networks Modulation in Young and Old Subjects During Transcranial Direct Current Stimulation Applied on Prefrontal and Parietal Cortex. Int J Neural Syst 2021; 32:2150056. [PMID: 34651550 DOI: 10.1142/s0129065721500568] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Evidence indicates that the transcranial direct current stimulation (tDCS) has the potential to transiently modulate cognitive function, including age-related changes in brain performance. Only a small number of studies have explored the interaction between the stimulation sites on the scalp, task performance, and brain network connectivity within the frame of physiological aging. We aimed to evaluate the spread of brain activation in both young and older adults in response to anodal tDCS applied to two different scalp stimulation sites: Prefrontal cortex (PFC) and posterior parietal cortex (PPC). EEG data were recorded during tDCS stimulation and evaluated using the Small World (SW) index as a graph theory metric. Before and after tDCS, participants performed a behavioral task; a performance accuracy index was computed and correlated with the SW index. Results showed that the SW index increased during tDCS of the PPC compared to the PFC at higher EEG frequencies only in young participants. tDCS at the PPC site did not exert significant effects on the performance, while tDCS at the PFC site appeared to influence task reaction times in the same direction in both young and older participants. In conclusion, studies using tDCS to modulate functional connectivity and influence behavior can help identify suitable protocols for the aging brain.
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Affiliation(s)
- Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma Rome, Italy.,eCampus University, Novedrate (Como), Italy
| | | | - Jesus Cespon
- Basque Center on Cognition, Brain and Language, San Sebastian, Spain
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma Rome, Italy
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