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Mitiureva D, Sysoeva O, Proshina E, Portnova G, Khayrullina G, Martynova O. Comparative analysis of resting-state EEG functional connectivity in depression and obsessive-compulsive disorder. Psychiatry Res Neuroimaging 2024; 342:111828. [PMID: 38833944 DOI: 10.1016/j.pscychresns.2024.111828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/09/2024] [Accepted: 05/19/2024] [Indexed: 06/06/2024]
Abstract
Major depressive disorder (MDD) and obsessive-compulsive disorder (OCD) are psychiatric disorders that often co-occur. We aimed to investigate whether their high comorbidity could be traced not only by clinical manifestations, but also at the level of functional brain activity. In this paper, we examined the differences in functional connectivity (FC) at the whole-brain level and within the default mode network (DMN). Resting-state EEG was obtained from 43 controls, 26 OCD patients, and 34 MDD patients. FC was analyzed between 68 cortical sources, and between-group differences in the 4-30 Hz range were assessed via the Network Based Statistic method. The strength of DMN intra-connectivity was compared between groups in the theta, alpha and beta frequency bands. A cluster of 67 connections distinguished the OCD, MDD and control groups. The majority of the connections, 8 of which correlated with depressive symptom severity, were found to be weaker in the clinical groups. Only 3 connections differed between the clinical groups, and one of them correlated with OCD severity. The DMN strength was reduced in the clinical groups in the alpha and beta bands. It can be concluded that the high comorbidity of OCD and MDD can be traced at the level of FC.
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Affiliation(s)
- Dina Mitiureva
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia; Centre for Cognition & Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Olga Sysoeva
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia; Sirius Center for Cognitive Sciences, Sirius University of Science and Technology, Sochi, Russia
| | - Ekaterina Proshina
- Centre for Cognition & Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia.
| | - Galina Portnova
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia
| | - Guzal Khayrullina
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia; Centre for Cognition & Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Olga Martynova
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia; Department of Biology and Biotechnology, National Research University Higher School of Economics, Moscow, Russia
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Amoruso L, García AM, Pusil S, Timofeeva P, Quiñones I, Carreiras M. Decoding bilingualism from resting-state oscillatory network organization. Ann N Y Acad Sci 2024; 1534:106-117. [PMID: 38419368 DOI: 10.1111/nyas.15113] [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: 03/02/2024]
Abstract
Can lifelong bilingualism be robustly decoded from intrinsic brain connectivity? Can we determine, using a spectrally resolved approach, the oscillatory networks that better predict dual-language experience? We recorded resting-state magnetoencephalographic activity in highly proficient Spanish-Basque bilinguals and Spanish monolinguals, calculated functional connectivity at canonical frequency bands, and derived topological network properties using graph analysis. These features were fed into a machine learning classifier to establish how robustly they discriminated between the groups. The model showed excellent classification (AUC: 0.91 ± 0.12) between individuals in each group. The key drivers of classification were network strength in beta (15-30 Hz) and delta (2-4 Hz) rhythms. Further characterization of these networks revealed the involvement of temporal, cingulate, and fronto-parietal hubs likely underpinning the language and default-mode networks (DMNs). Complementary evidence from a correlation analysis showed that the top-ranked features that better discriminated individuals during rest also explained interindividual variability in second language (L2) proficiency within bilinguals, further supporting the robustness of the machine learning model in capturing trait-like markers of bilingualism. Overall, our results show that long-term experience with an L2 can be "brain-read" at a fine-grained level from resting-state oscillatory network organization, highlighting its pervasive impact, particularly within language and DMN networks.
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Affiliation(s)
- Lucia Amoruso
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
| | - Adolfo M García
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, USA
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - Sandra Pusil
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
| | - Polina Timofeeva
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Universidad del País Vasco (UPV/EHU), San Sebastian, Spain
| | - Ileana Quiñones
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Manuel Carreiras
- Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Universidad del País Vasco (UPV/EHU), San Sebastian, Spain
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Hong JK, Yoon IY. Efficacy of cranial electrotherapy stimulation on mood and sense of well-being in people with subclinical insomnia. J Sleep Res 2024; 33:e13978. [PMID: 37366366 DOI: 10.1111/jsr.13978] [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] [Received: 01/12/2023] [Revised: 04/27/2023] [Accepted: 06/12/2023] [Indexed: 06/28/2023]
Abstract
Cranial electrotherapy stimulation is a non-invasive brain stimulation method characterised by using a microcurrent. The objective of the study was to investigate whether a novel device with a stable supplement of electronic stimulation would improve sleep and the accompanying mood symptoms in people with subclinical insomnia. People who had insomnia symptoms without meeting the criteria for chronic insomnia disorder were recruited and randomly assigned to an active or a sham device group. They were required to use the provided device for 30 min each time, twice a day for 2 weeks. Outcome measures included questionnaires for sleep, depression, anxiety, and quality of life, 4 day actigraphy, and 64-channel electroencephalography. Fifty-nine participants (male 35.6%) with a mean age of 41.1 ± 12.0 years were randomised. Improvement of depression (p = 0.032) and physical well-being (p = 0.041) were significant in the active device group compared with the sham device group. Anxiety was also improved in the active device group, although the improvement was not statistically significant (p = 0.090). Regarding sleep, both groups showed a significant improvement in subjective rating, showing no significant group difference. The change in electroencephalography after the 2 week intervention was significantly different between the two groups, especially for occipital delta (p = 0.008) and beta power (p = 0.012), and temporo-parieto-occipital theta (p = 0.022). In conclusion, cranial electrotherapy stimulation can serve as an adjunctive therapy to ameliorate psychological symptoms and to alter brain activity. The effects of the device in a clinical population and an optimal set of parameters of stimulation should be further investigated.
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Affiliation(s)
- Jung Kyung Hong
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - In-Young Yoon
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
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Khayretdinova M, Zakharov I, Pshonkovskaya P, Adamovich T, Kiryasov A, Zhdanov A, Shovkun A. Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model. Neuroimage 2024; 285:120495. [PMID: 38092156 DOI: 10.1016/j.neuroimage.2023.120495] [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: 07/13/2023] [Revised: 11/29/2023] [Accepted: 12/10/2023] [Indexed: 12/17/2023] Open
Abstract
This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG study, affirming that gender prediction can be attained with noteworthy accuracy. The best performing model achieved an accuracy of 85% and an ROC AUC of 89%, surpassing all prior benchmarks set using EEG data and rivaling the top-tier results derived from fMRI studies. A comparative analysis of LightGBM and Deep Convolutional Neural Network (DCNN) models revealed DCNN's superior performance, attributed to its ability to learn complex spatial-temporal patterns in the EEG data and handle large volumes of data effectively. Despite this, interpretability remained a challenge for the DCNN model. The LightGBM interpretability analysis revealed that the most important EEG features for accurate sex prediction were related to left fronto-central and parietal EEG connectivity. We also showed the role of both low (delta and theta) and high (beta and gamma) activity in the accurate sex prediction. These results, however, have to be approached with caution, because it was obtained from a dataset comprised largely of participants with various mental health conditions, which limits the generalizability of the results and necessitates further validation in future studies. . Overall, the study illuminates the potential of interpretable machine learning for sex prediction, alongside highlighting the importance of considering individual differences in prediction sex from brain activity.
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Süß AM, Hug M, Conradi N, Kienitz R, Rosenow F, Rampp S, Merkel N. Lateralization of delta band power in magnetoencephalography (MEG) in patients with unilateral focal epilepsy and its relation to verbal fluency. Brain Behav 2023; 13:e3257. [PMID: 37752097 PMCID: PMC10636394 DOI: 10.1002/brb3.3257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 08/22/2023] [Accepted: 08/31/2023] [Indexed: 09/28/2023] Open
Abstract
INTRODUCTION Delta power is a clinically established biomarker for abnormal brain processes. However, in patients with unilateral focal epilepsy (FE) it is still not well understood, how it relates to the epileptogenic zone and to neurocognitive functioning. The aim of the present study was thus to assess how delta power relates to the affected hemisphere, whether lateralization strength differs between the patients, and how changes in delta power correlate with cognitive functioning. METHOD We retrospectively studied patients with left (LFE) and right FE (RFE) who had undergone a resting-state magnetoencephalography measurement. We computed global and hemispheric delta power and lateralization indices and examined whether delta power correlates with semantic and letter verbal fluency (former being a marker for language and verbal memory, latter for executive functions) in 26 FE patients (15 LFE, 11 RFE) and 10 healthy controls. RESULTS Delta power was increased in FE patients compared to healthy controls. However, the increase across hemispheres was related to the site of the epileptic focus: On group level, LFE patients showed higher delta power in both hemispheres, whereas RFE patients primarily exhibited higher delta power in the ipsilateral right hemisphere. Both groups showed co-fluctuations of delta power between the hemispheres. Besides, delta power correlated negatively only with letter verbal fluency. CONCLUSION The findings confirm and provide further evidence that delta power is a marker of pathological activity and abnormal brain processes in FE. Delta power dynamics differ between patient groups, indicating that delta power could offer additional diagnostic value. The negative association of delta power and letter verbal fluency suggests that executive dysfunctions are related to low frequency abnormalities.
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Affiliation(s)
- Annika Melissa Süß
- Epilepsy Center Frankfurt Rhine‐MainCenter of Neurology and NeurosurgeryUniversity Hospital FrankfurtFrankfurt am MainGermany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe University FrankfurtFrankfurt am MainGermany
| | - Marion Hug
- Department of NeurologyUniversity Hospital Frankfurt and Goethe UniversityFrankfurt am MainGermany
| | - Nadine Conradi
- Epilepsy Center Frankfurt Rhine‐MainCenter of Neurology and NeurosurgeryUniversity Hospital FrankfurtFrankfurt am MainGermany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe University FrankfurtFrankfurt am MainGermany
| | - Ricardo Kienitz
- Epilepsy Center Frankfurt Rhine‐MainCenter of Neurology and NeurosurgeryUniversity Hospital FrankfurtFrankfurt am MainGermany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe University FrankfurtFrankfurt am MainGermany
| | - Felix Rosenow
- Epilepsy Center Frankfurt Rhine‐MainCenter of Neurology and NeurosurgeryUniversity Hospital FrankfurtFrankfurt am MainGermany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe University FrankfurtFrankfurt am MainGermany
| | - Stefan Rampp
- Department of NeurosurgeryUniversity Hospital ErlangenErlangenGermany
- Department of NeurosurgeryUniversity Hospital Halle (Saale)Halle (Saale)Germany
| | - Nina Merkel
- Epilepsy Center Frankfurt Rhine‐MainCenter of Neurology and NeurosurgeryUniversity Hospital FrankfurtFrankfurt am MainGermany
- Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck SocietyFrankfurt am MainGermany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe University FrankfurtFrankfurt am MainGermany
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Ding R, Tang H, Liu Y, Yin Y, Yan B, Jiang Y, Toussaint PJ, Xia Y, Evans AC, Zhou D, Hao X, Lu J, Yao D. Therapeutic effect of tempo in Mozart's "Sonata for two pianos" (K. 448) in patients with epilepsy: An electroencephalographic study. Epilepsy Behav 2023; 145:109323. [PMID: 37356223 DOI: 10.1016/j.yebeh.2023.109323] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND Mozart's "Sonata for two pianos" (Köchel listing 448) has proven effective as music therapy for patients with epilepsy, but little is understood about the mechanism of which feature in it impacted therapeutic effect. This study explored whether tempo in that piece is important for its therapeutic effect. METHODS We measured the effects of tempo in Mozart's sonata on clinical and electroencephalographic parameters of 147 patients with epilepsy who listened to the music at slow, original, or accelerated speed. As a control, patients listened to Haydn's Symphony no. 94 at original speed. RESULTS Listening to Mozart's piece at original speed significantly reduced the number of interictal epileptic discharges. It decreased beta power in the frontal, parietal, and occipital regions, suggesting increased auditory attention and reduced visual attention. It also decreased functional connectivity among frontal, parietal, temporal, and occipital brain regions, also suggesting increased auditory attention and reduced visual attention. No such effects were observed after patients listened to the slow or fast version of Mozart's piece, or to Haydn's symphony at normal speed. CONCLUSIONS These results suggest that Mozart's "Sonata for two pianos" may exert therapeutic effects by regulating attention when played at its original tempo, but not slower or faster. These findings may help guide the design and optimization of music therapy against epilepsy.
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Affiliation(s)
- Rui Ding
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China; Montreal Neurological Institute, McGill University, Montreal, QC, Canada, H3A 2B4.
| | - Huajuan Tang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; Department of Neurology, 363 Hospital, Chengdu 610041, Sichuan, China.
| | - Ying Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.
| | - Yitian Yin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.
| | - Bo Yan
- Department of Neurology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| | - Yingqi Jiang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| | - Paule-J Toussaint
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada, H3A 2B4.
| | - Yang Xia
- Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada, H3A 2B4.
| | - Dong Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| | - Xiaoting Hao
- Department of Neurology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| | - Jing Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.
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Ray KL, Griffin NR, Shumake J, Alario A, Allen JJB, Beevers CG, Schnyer DM. Altered electroencephalography resting state network coherence in remitted MDD. Brain Res 2023; 1806:148282. [PMID: 36792002 DOI: 10.1016/j.brainres.2023.148282] [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: 10/10/2022] [Revised: 02/10/2023] [Accepted: 02/11/2023] [Indexed: 02/16/2023]
Abstract
Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.
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Affiliation(s)
| | | | | | - Alexandra Alario
- University of Texas, Austin, United States; University of Iowa, United States
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Mo Z, Grennan G, Kulkarni A, Ramanathan D, Balasubramani PP, Mishra J. Parietal alpha underlies slower cognitive responses during interference processing in adolescents. Behav Brain Res 2023; 443:114356. [PMID: 36801472 DOI: 10.1016/j.bbr.2023.114356] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/03/2023] [Accepted: 02/17/2023] [Indexed: 02/21/2023]
Abstract
Adolescence is a critical period when cognitive control is rapidly maturing across several core dimensions. Here, we evaluated how healthy adolescents (13-17 years of age, n = 44) versus young adults (18-25 years of age, n = 49) differ across a series of cognitive assessments with simultaneous electroencephalography (EEG) recordings. Cognitive tasks included selective attention, inhibitory control, working memory, as well as both non-emotional and emotional interference processing. We found that adolescents displayed significantly slower responses than young adults specifically on the interference processing tasks. Evaluation of EEG event-related spectral perturbations (ERSPs) on the interference tasks showed that adolescents consistently had greater event-related desynchronization in alpha/beta frequencies in parietal regions. Midline frontal theta activity was also greater in the flanker interference task in adolescents, suggesting greater cognitive effort. Parietal alpha activity predicted age-related speed differences during non-emotional flanker interference processing, and frontoparietal connectivity, specifically midfrontal theta - parietal alpha functional connectivity predicted speed effects during emotional interference. Overall, our neuro-cognitive results illustrate developing cognitive control in adolescents particularly for interference processing, predicted by differential alpha band activity and connectivity in parietal brain regions.
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Affiliation(s)
- Zihao Mo
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Gillian Grennan
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Atharv Kulkarni
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Dhakshin Ramanathan
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Department of Mental Health, VA San Diego Medical Center, San Diego, CA, USA
| | | | - Jyoti Mishra
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.
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Seghier ML. Multiple functions of the angular gyrus at high temporal resolution. Brain Struct Funct 2023; 228:7-46. [PMID: 35674917 DOI: 10.1007/s00429-022-02512-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/22/2022] [Indexed: 02/07/2023]
Abstract
Here, the functions of the angular gyrus (AG) are evaluated in the light of current evidence from transcranial magnetic/electric stimulation (TMS/TES) and EEG/MEG studies. 65 TMS/TES and 52 EEG/MEG studies were examined in this review. TMS/TES literature points to a causal role in semantic processing, word and number processing, attention and visual search, self-guided movement, memory, and self-processing. EEG/MEG studies reported AG effects at latencies varying between 32 and 800 ms in a wide range of domains, with a high probability to detect an effect at 300-350 ms post-stimulus onset. A three-phase unifying model revolving around the process of sensemaking is then suggested: (1) early AG involvement in defining the current context, within the first 200 ms, with a bias toward the right hemisphere; (2) attention re-orientation and retrieval of relevant information within 200-500 ms; and (3) cross-modal integration at late latencies with a bias toward the left hemisphere. This sensemaking process can favour accuracy (e.g. for word and number processing) or plausibility (e.g. for comprehension and social cognition). Such functions of the AG depend on the status of other connected regions. The much-debated semantic role is also discussed as follows: (1) there is a strong TMS/TES evidence for a causal semantic role, (2) current EEG/MEG evidence is however weak, but (3) the existing arguments against a semantic role for the AG are not strong. Some outstanding questions for future research are proposed. This review recognizes that cracking the role(s) of the AG in cognition is possible only when its exact contributions within the default mode network are teased apart.
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Affiliation(s)
- Mohamed L Seghier
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE. .,Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, UAE.
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Alhajri N, Boudreau SA, Graven-Nielsen T. Decreased Default Mode Network Connectivity Following 24 Hours of Capsaicin-induced Pain Persists During Immediate Pain Relief and Facilitation. THE JOURNAL OF PAIN 2022; 24:796-811. [PMID: 36521671 DOI: 10.1016/j.jpain.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/30/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022]
Abstract
Prolonged experimental pain models can help assess cortical mechanisms underlying the transition from acute to chronic pain such as resting-state functional connectivity (rsFC), especially in early stages. This crossover study determined the effects of 24-hour-capsaicin-induced pain on the default mode network rsFC, a major network in the dynamic pain connectome. Electroencephalographic rsFC measured by Granger causality was acquired from 24 healthy volunteers (12 women) at baseline, 1hour, and 24hours following the application of a control or capsaicin patch on the right forearm. The control patch was received maximum 1 week before the capsaicin patch. Following 24hours, the patch was cooled and later heated to assess rsFC changes in response to pain relief and facilitation, respectively. Compared to baseline, decreased rsFC at alpha oscillations (8-10Hz) was found following 1hour and 24hours of capsaicin application for connections projecting from medial prefrontal cortex (mPFC) and right angular gyrus (rAG) but not left angular gyrus (lAG) or posterior cingulate cortex (PCC): mPFC-PCC (1hour:P < .001, 24hours:P = .002), mPFC-rAG (1hour:P < .001, 24hours:P = .001), rAG-mPFC (1hour:P < .001, 24hours:P = .001), rAG-PCC (1hour:P < .001, 24hours:P = .004). Comparable decreased rsFC following 1hour and 24hours (P≤0.008) was found at beta oscillations, however, decreased projections from PCC were also found: PCC-rAG (P≤0.005) and PCC-lAG (P≤0.006). Pain NRS scores following 24hours (3.7±0.4) was reduced by cooling (0.3±0.1, P = .004) and increased by heating (4.8±0.6, P = .016). However, neither cooling nor heating altered rsFC. This study shows that 24hours of experimental pain induces a robust decrease in DMN connectivity that persists during pain relief or facilitation suggesting a possible shift to attentional and emotional processing in persistent pain. PERSPECTIVE: This article shows decreased DMN connectivity that might reflect possible attentional and emotional changes during acute and prolonged pain. Understanding these changes could potentially help clinicians in developing therapeutic methods that can better target these attentional and emotional processes before developing into more persistent states.
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Affiliation(s)
- Najah Alhajri
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Shellie Ann Boudreau
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Thomas Graven-Nielsen
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
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Oliaee A, Mohebbi M, Shirani S, Rostami R. Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment. Cogn Neurodyn 2022; 16:1249-1259. [PMID: 36408072 PMCID: PMC9666605 DOI: 10.1007/s11571-022-09794-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 02/16/2022] [Accepted: 02/20/2022] [Indexed: 11/25/2022] Open
Abstract
Dyslexia is a neurological disorder manifested as difficulty reading and writing. It can occur despite adequate instruction, intelligence, and intact sensory abilities. Different electroencephalogram (EEG) patterns have been demonstrated between dyslexic and healthy subjects in previous studies. This study focuses on the difference between patients before and after treatment. The main goal is to identify the subset of features that adequately discriminate subjects before and after a specific treatment plan. The treatment consists of Transcranial Direct Current Stimulation (tDCS) and occupational therapy using the BrainWare SAFARI software. The EEG signals of sixteen dyslexic children were recorded during the eyes-closed resting state before and after treatment. The preprocessing step was followed by the extraction of a wide range of features to investigate the differences related to the treatment. An optimal subset of features extracted from recorded EEG signals was determined using Principal Component Analysis (PCA) in conjunction with the Sequential Floating Forward Selection (SFFS) algorithm. The results showed that treatment leads to significant changes in EEG features like spectral and phase-related EEG features, in various regions. It has been demonstrated that the extracted subset of discriminative features can be useful for classification applications in treatment assessment. The most discriminative subset of features could classify the data with an accuracy of 92% with SVM classifier. The above result confirms the efficacy of the treatment plans in improving dyslexic children's cognitive skills.
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Affiliation(s)
- Anahita Oliaee
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
| | - Maryam Mohebbi
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
| | - Sepehr Shirani
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
| | - Reza Rostami
- Department of Psychology, Faculty of Psychology, University of Tehran, Tehran, Iran
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12
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Kazemi R, Rostami R, Nasiri Z, Hadipour AL, Kiaee N, Coetzee JP, Philips A, Brown R, Seenivasan S, Adamson MM. Electrophysiological and behavioral effects of unilateral and bilateral rTMS; A randomized clinical trial on rumination and depression. J Affect Disord 2022; 317:360-372. [PMID: 36055535 DOI: 10.1016/j.jad.2022.08.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Rumination is significantly frequent in major depressive disorder (MDD). However, not a lot of studies have investigated the effects of repetitive transcranial magnetic stimulation (rTMS) on rumination. METHODS 61 participants with a minimum Hamilton Depression Rating Scale (HAM-D) score of 20 were randomly assigned to sham, bilateral stimulation (BS) or unilateral stimulation (US) groups. EEG, The Ruminative Response Scale (RRS), and HAM-D were administered before and after the 20 sessions of rTMS. Phase locked values (PLV) were calculated as a measure of connectivity. RESULTS There was a significant decrease in HAM-D scores in both BS and US. In responders, BS and US differed significantly in RRS total scores, with greater reduction in BS. PLV significantly changed in the default mode network (DMN) in delta, theta, alpha, and beta in BS, in responders of which PLV decreased in the DMN in beta and gamma. Positive correlations between PLV and brooding in delta and theta, and negative correlations between PLV and reflection were found in theta, alpha, and beta. In US, connectivity in the DMN increased in beta, and PLV increased in theta and beta, and decreased in alpha and beta in its responders. Positive correlations between PLV and brooding in the delta and theta, as well as negative correlations between PLV and reflection in theta were observed in the DMN. CONCLUSION US and BS resulted in different modulations in the DMN, however, both could alleviate both rumination and depression. Reductions in the beta and alpha frequency bands in the DMN can be considered as potential EEG-based markers of response to bilateral and unilateral rTMS, respectively.
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Affiliation(s)
- Reza Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran.
| | - Reza Rostami
- Department of Psychology, University of Tehran, Tehran, Iran; Atieh Clinical Neuroscience Center, Tehran, Iran
| | - Zahra Nasiri
- Atieh Clinical Neuroscience Center, Tehran, Iran
| | - Abed L Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
| | - Nasim Kiaee
- Atieh Clinical Neuroscience Center, Tehran, Iran
| | - John P Coetzee
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Angela Philips
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Randi Brown
- Department of Psychology, Palo Alto University, Palo Alto, CA, USA
| | - Srija Seenivasan
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Maheen M Adamson
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
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13
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Buján A, Sampaio A, Pinal D. Resting-state electroencephalographic correlates of cognitive reserve: Moderating the age-related worsening in cognitive function. Front Aging Neurosci 2022; 14:854928. [PMID: 36185469 PMCID: PMC9521492 DOI: 10.3389/fnagi.2022.854928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
This exploratory study aimed to investigate the resting-state electroencephalographic (rsEEG) correlates of the cognitive reserve from a life span perspective. Current source density (CSD) and lagged-linear connectivity (LLC) measures were assessed to this aim. We firstly explored the relationship between rsEEG measures for the different frequency bands and a socio-behavioral proxy of cognitive reserve, the Cognitive Reserve Index (CRI). Secondly, we applied moderation analyses to assess whether any of the correlated rsEEG measures showed a moderating role in the relationship between age and cognitive function. Moderate negative correlations were found between the CRI and occipital CSD of delta and beta 2. Moreover, inter- and intrahemispheric LLC measures were correlated with the CRI, showing a negative association with delta and positive associations with alpha 1, beta 1, and beta 2. Among those correlated measures, just two rsEEG variables were significant moderators of the relationship between age and cognition: occipital delta CSD and right hemispheric beta 2 LLC between occipital and limbic regions. The effect of age on cognitive performance was stronger for higher values of both measures. Therefore, lower values of occipital delta CSD and lower beta 2 LLC between right occipital and limbic regions might protect or compensate for the effects of age on cognition. Results of this exploratory study might be helpful to allocate more preventive efforts to curb the progression of cognitive decline in adults with less CR, possibly characterized by these rsEEG parameters at a neural level. However, given the exploratory nature of this study, more conclusive work on these rsEEG measures is needed to firmly establish their role in the cognition–age relationship, for example, verifying if these measures moderate the relationship between brain structure and cognition.
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14
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Yang H, Paller KA, van Vugt M. The steady state visual evoked potential (SSVEP) tracks "sticky" thinking, but not more general mind-wandering. Front Hum Neurosci 2022; 16:892863. [PMID: 36034124 PMCID: PMC9402933 DOI: 10.3389/fnhum.2022.892863] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
For a large proportion of our daily lives, spontaneously occurring thoughts tend to disengage our minds from goal-directed thinking. Previous studies showed that EEG features such as the P3 and alpha oscillations can predict mind-wandering to some extent, but only with accuracies of around 60%. A potential candidate for improving prediction accuracy is the Steady-State Visual Evoked Potential (SSVEP), which is used frequently in single-trial contexts such as brain-computer interfaces as a marker of the direction of attention. In this study, we modified the sustained attention to response task (SART) that is usually employed to measure spontaneous thought to incorporate the SSVEP elicited by a 12.5-Hz flicker. We then examined whether the SSVEP could track and allow for the prediction of the stickiness and task-relatedness dimensions of spontaneous thought. Our results show that the SSVEP evoked by flickering words was able to distinguish between more and less sticky thinking but not between whether a participant was on- or off-task. This suggests that the SSVEP is able to track spontaneous thinking when it is strongly disengaged from the task (as in the sticky form of off-task thinking) but not off-task thought in general. Future research should determine the exact dimensions of spontaneous thought to which the SSVEP is most sensitive.
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Affiliation(s)
- Hang Yang
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
| | - Ken A. Paller
- Department of Psychology, Northwestern University, Evanston, IL, United States
| | - Marieke van Vugt
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
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15
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Ebrahimzadeh E, Saharkhiz S, Rajabion L, Oskouei HB, Seraji M, Fayaz F, Saliminia S, Sadjadi SM, Soltanian-Zadeh H. Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function. Front Syst Neurosci 2022; 16:934266. [PMID: 35966000 PMCID: PMC9371554 DOI: 10.3389/fnsys.2022.934266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 07/08/2022] [Indexed: 02/01/2023] Open
Abstract
Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions.
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Affiliation(s)
- Elias Ebrahimzadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- *Correspondence: Elias Ebrahimzadeh, ,
| | - Saber Saharkhiz
- Department of Pharmacology-Physiology, Faculty of Medicine, University of Sherbrooke, Sherbrooke, Canada
| | - Lila Rajabion
- School of Graduate Studies, State University of New York Empire State College, Manhattan, NY, United States
| | | | - Masoud Seraji
- Department of Psychology, University of Texas at Austin, Austin, TX, United States
| | - Farahnaz Fayaz
- Department of Biomedical Engineering, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Sarah Saliminia
- Department of Biomedical Engineering, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Seyyed Mostafa Sadjadi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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16
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Alteration of cortical functional networks in mood disorders with resting-state electroencephalography. Sci Rep 2022; 12:5920. [PMID: 35396563 PMCID: PMC8993886 DOI: 10.1038/s41598-022-10038-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/24/2022] [Indexed: 01/10/2023] Open
Abstract
Studies comparing bipolar disorder (BD) and major depressive disorder (MDD) are scarce, and the neuropathology of these disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with BD and MDD. EEG was recorded in 35 patients with BD, 39 patients with MDD, and 42 healthy controls (HCs). Graph theory-based source-level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands. At the global level, patients with BD and MDD showed higher strength and CC, and lower PL in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus, left isthmus cingulate, bilateral paracentral, and left superior frontal; however, patients with MDD showed higher nodal CC only in the right precuneus compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes compared to HCs. Our findings might suggest more altered cortical functional network in patients with BD than in those with MDD.
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17
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Polychroni N, Herrojo Ruiz M, Terhune DB. Introspection confidence predicts EEG decoding of self-generated thoughts and meta-awareness. Hum Brain Mapp 2022; 43:2311-2327. [PMID: 35122359 PMCID: PMC8996352 DOI: 10.1002/hbm.25789] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 12/17/2021] [Accepted: 12/29/2021] [Indexed: 01/22/2023] Open
Abstract
The neurophysiological bases of mind wandering (MW)-an experiential state wherein attention is disengaged from the external environment in favour of internal thoughts-and state meta-awareness are poorly understood. In parallel, the relationship between introspection confidence in experiential state judgements and neural representations remains unclear. Here, we recorded EEG while participants completed a listening task within which they made experiential state judgements and rated their confidence. Alpha power was reliably greater during MW episodes, with unaware MW further associated with greater delta and theta power. Multivariate pattern classification analysis revealed that MW and meta-awareness can be decoded from the distribution of power in these three frequency bands. Critically, we show that individual decoding accuracies positively correlate with introspection confidence. Our results reaffirm the role of alpha oscillations in MW, implicate lower frequencies in meta-awareness, and are consistent with the proposal that introspection confidence indexes neurophysiological discriminability of representational states.
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Affiliation(s)
- Naya Polychroni
- Department of Psychology, Goldsmiths, University of London, London, UK
| | - Maria Herrojo Ruiz
- Department of Psychology, Goldsmiths, University of London, London, UK.,Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
| | - Devin B Terhune
- Department of Psychology, Goldsmiths, University of London, London, UK
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18
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Griffa A, Legdeur N, Badissi M, van den Heuvel MP, Stam CJ, Visser PJ, Hillebrand A. Magnetoencephalography Brain Signatures Relate to Cognition and Cognitive Reserve in the Oldest-Old: The EMIF-AD 90 + Study. Front Aging Neurosci 2021; 13:746373. [PMID: 34899269 PMCID: PMC8656941 DOI: 10.3389/fnagi.2021.746373] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/01/2021] [Indexed: 11/25/2022] Open
Abstract
The oldest-old subjects represent the fastest growing segment of society and are at high risk for dementia with a prevalence of up to 40%. Lifestyle factors, such as lifelong participation in cognitive and leisure activities, may contribute to individual cognitive reserve and reduce the risk for cognitive impairments. However, the neural bases underlying cognitive functioning and cognitive reserve in this age range are still poorly understood. Here, we investigate spectral and functional connectivity features obtained from resting-state MEG recordings in a cohort of 35 cognitively normal (92.2 ± 1.8 years old, 19 women) and 11 cognitively impaired (90.9 ± 1.9 years old, 1 woman) oldest-old participants, in relation to cognitive traits and cognitive reserve. The latter was approximated with a self-reported scale on lifelong engagement in cognitively demanding activities. Cognitively impaired oldest-old participants had slower cortical rhythms in frontal, parietal and default mode network regions compared to the cognitively normal subjects. These alterations mainly concerned the theta and beta band and partially explained inter-subject variability of episodic memory scores. Moreover, a distinct spectral pattern characterized by higher relative power in the alpha band was specifically associated with higher cognitive reserve while taking into account the effect of age and education level. Finally, stronger functional connectivity in the alpha and beta band were weakly associated with better cognitive performances in the whole group of subjects, although functional connectivity effects were less prominent than the spectral ones. Our results shed new light on the neural underpinnings of cognitive functioning in the oldest-old population and indicate that cognitive performance and cognitive reserve may have distinct spectral electrophysiological substrates.
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Affiliation(s)
- Alessandra Griffa
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Center of Neuroprosthetics, Institute of Bioengineering, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.,Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Nienke Legdeur
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Maryam Badissi
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Martijn P van den Heuvel
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neuroscience and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Pieter Jelle Visser
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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19
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Morawska MM, Moreira CG, Ginde VR, Valko PO, Weiss T, Büchele F, Imbach LL, Masneuf S, Kollarik S, Prymaczok N, Gerez JA, Riek R, Baumann CR, Noain D. Slow-wave sleep affects synucleinopathy and regulates proteostatic processes in mouse models of Parkinson's disease. Sci Transl Med 2021; 13:eabe7099. [PMID: 34878820 DOI: 10.1126/scitranslmed.abe7099] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Marta M Morawska
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland.,University of Zurich (UZH), Neuroscience Center Zurich (ZNZ), Winterthurerstrasse 190, Zurich 8057, Switzerland
| | - Carlos G Moreira
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland.,ETH Zurich, Neuroscience Center Zurich (ZNZ), Winterthurerstrasse 190, Zurich 8057, Switzerland
| | - Varun R Ginde
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland
| | - Philipp O Valko
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland
| | - Tobias Weiss
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland
| | - Fabian Büchele
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland
| | - Lukas L Imbach
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland
| | - Sophie Masneuf
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland
| | - Sedef Kollarik
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland.,University of Zurich (UZH), Neuroscience Center Zurich (ZNZ), Winterthurerstrasse 190, Zurich 8057, Switzerland
| | - Natalia Prymaczok
- ETH Zurich, Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, Zurich 8093, Switzerland
| | - Juan A Gerez
- ETH Zurich, Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, Zurich 8093, Switzerland
| | - Roland Riek
- ETH Zurich, Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, Zurich 8093, Switzerland
| | - Christian R Baumann
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland.,University of Zurich (UZH), Neuroscience Center Zurich (ZNZ), Winterthurerstrasse 190, Zurich 8057, Switzerland.,Center of Competence Sleep and Health Zurich, University of Zurich, Frauenklinikstrasse 26, Zurich 8091, Switzerland
| | - Daniela Noain
- Department of Neurology, University Hospital Zurich (USZ), Frauenklinikstrasse 26, Zurich 8091, Switzerland.,University of Zurich (UZH), Neuroscience Center Zurich (ZNZ), Winterthurerstrasse 190, Zurich 8057, Switzerland.,Center of Competence Sleep and Health Zurich, University of Zurich, Frauenklinikstrasse 26, Zurich 8091, Switzerland
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20
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Jin S, Shin C, Han C, Kim YK, Lee J, Jeon SW, Lee SH, Ko YH. Changes in Brain Electrical Activity According to Post-traumatic Stress Symptoms in Survivors of the Sewol Ferry Disaster: A 1-year Longitudinal Study. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE 2021; 19:537-544. [PMID: 34294623 PMCID: PMC8316658 DOI: 10.9758/cpn.2021.19.3.537] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/03/2020] [Accepted: 11/04/2020] [Indexed: 11/25/2022]
Abstract
Objective The pathology of post-traumatic stress disorder (PTSD) is associated with changes in brain structure and function, especially in the amygdala, medial prefrontal cortex, hippocampus, and insula. Survivors of tragic accidents often experience psychological stress and develop post-traumatic stress symptoms (PTSS), regardless of the diagnosis of PTSD. This study aimed to evaluate electroencephalographic changes according to PTSS in victims of a single traumatic event. Methods This study enrolled 60 survivors of the Sewol ferry disaster that occurred in 2014 from Danwon High School and collected electroencephalographic data through 19 channels twice for each person in 2014 and 2015 (mean 451.88 [standard deviation 25.77] days of follow-up). PTSS was assessed using the PTSD Checklist-Civilian Version (PCL-C) and the participants were divided into two groups according to the differences in PCL-C scores between 2014 and 2015. Electroencephalographic data were converted to three-dimensional data to perform low-resolution electrical tomographic analysis. Results Significant electroencephalographic changes over time were observed. The group of participants with worsened PCL-C score showed an increased change of delta slow waves in Brodmann areas 13 and 44, with the largest difference in the insula region, compared to those with improved PCL-C scores. Conclusion Our findings suggests that the electrophysiological changes in the insula are associated with PTSS changes.
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Affiliation(s)
- Sehee Jin
- Department of Psychiatry, Korea University College of Medicine, Seoul, Korea
| | - Cheolmin Shin
- Department of Psychiatry, Korea University College of Medicine, Seoul, Korea
| | - Changsu Han
- Department of Psychiatry, Korea University College of Medicine, Seoul, Korea
| | - Yong-Ku Kim
- Department of Psychiatry, Korea University College of Medicine, Seoul, Korea
| | - Jongha Lee
- Department of Psychiatry, Korea University College of Medicine, Seoul, Korea
| | - Sang Won Jeon
- Department of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seung-Hoon Lee
- Department of Psychiatry, Veterans Health Service Medical Center, Seoul, Korea
| | - Young-Hoon Ko
- Department of Psychiatry, Korea University College of Medicine, Seoul, Korea
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21
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Shan X, Huo S, Yang L, Cao J, Zou J, Chen L, Sarrigiannis PG, Zhao Y. A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals. IEEE Trans Neural Syst Rehabil Eng 2021; 29:841-851. [PMID: 33909567 DOI: 10.1109/tnsre.2021.3076311] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based cross-spectrum is one of the most widely implemented methods, but it is limited by the spectral leakage caused by the finite length of the basic function that impacts the time and frequency resolutions. This paper proposes a new time-frequency brain functional connectivity analysis framework to track the non-stationary association of two EEG signals based on a Revised Hilbert-Huang Transform (RHHT). The framework can estimate the cross-spectrum of decomposed components of EEG, followed by a surrogate significance test. The results of two simulation examples demonstrate that, within a certain statistical confidence level, the proposed framework outperforms the wavelet-based method in terms of accuracy and time-frequency resolution. A case study on classifying epileptic patients and healthy controls using interictal seizure-free EEG data is also presented. The result suggests that the proposed method has the potential to better differentiate these two groups benefiting from the enhanced measure of dynamic time-frequency association.
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22
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Fakhraei L, Francoeur M, Balasubramani PP, Tang T, Hulyalkar S, Buscher N, Mishra J, Ramanathan DS. Electrophysiological Correlates of Rodent Default-Mode Network Suppression Revealed by Large-Scale Local Field Potential Recordings. Cereb Cortex Commun 2021; 2:tgab034. [PMID: 34296178 PMCID: PMC8166125 DOI: 10.1093/texcom/tgab034] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/13/2021] [Accepted: 04/14/2021] [Indexed: 11/13/2022] Open
Abstract
The default-mode network (DMN) in humans consists of a set of brain regions that, as measured with functional magnetic resonance imaging (fMRI), show both intrinsic correlations with each other and suppression during externally oriented tasks. Resting-state fMRI studies have previously identified similar patterns of intrinsic correlations in overlapping brain regions in rodents (A29C/posterior cingulate cortex, parietal cortex, and medial temporal lobe structures). However, due to challenges with performing rodent behavior in an MRI machine, it is still unclear whether activity in rodent DMN regions are suppressed during externally oriented visual tasks. Using distributed local field potential measurements in rats, we have discovered that activity in DMN brain regions noted above show task-related suppression during an externally oriented visual task at alpha and low beta-frequencies. Interestingly, this suppression (particularly in posterior cingulate cortex) was linked with improved performance on the task. Using electroencephalography recordings from a similar task in humans, we identified a similar suppression of activity in posterior cingulate cortex at alpha/low beta-frequencies. Thus, we have identified a common electrophysiological marker of DMN suppression in both rodents and humans. This observation paves the way for future studies using rodents to probe circuit-level functioning of DMN function. SIGNIFICANCE Here we show that alpha/beta frequency oscillations in rats show key features of DMN activity, including intrinsic correlations between DMN brain regions, task-related suppression, and interference with attention/decision-making. We found similar task-related suppression at alpha/low beta-frequencies of DMN activity in humans.
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Affiliation(s)
- Leila Fakhraei
- Mental Health Service, VA San Diego Healthcare System., La Jolla, CA 92161, USA
- Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
| | - Miranda Francoeur
- Mental Health Service, VA San Diego Healthcare System., La Jolla, CA 92161, USA
- Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
| | | | - Tianzhi Tang
- Mental Health Service, VA San Diego Healthcare System., La Jolla, CA 92161, USA
- Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
| | - Sidharth Hulyalkar
- Mental Health Service, VA San Diego Healthcare System., La Jolla, CA 92161, USA
- Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
| | - Nathalie Buscher
- Mental Health Service, VA San Diego Healthcare System., La Jolla, CA 92161, USA
- Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
| | - Jyoti Mishra
- Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
| | - Dhakshin S Ramanathan
- Mental Health Service, VA San Diego Healthcare System., La Jolla, CA 92161, USA
- Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
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Benschop L, Poppa T, Medani T, Shahabi H, Baeken C, Leahy RM, Pizzagalli DA, Vanderhasselt MA. Electrophysiological scarring in remitted depressed patients: Elevated EEG functional connectivity between the posterior cingulate cortex and the subgenual prefrontal cortex as a neural marker for rumination. J Affect Disord 2021; 281:493-501. [PMID: 33385828 DOI: 10.1016/j.jad.2020.12.081] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 12/18/2020] [Accepted: 12/21/2020] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Prior resting state fMRI studies have revealed that elevated connectivity between the default mode network (DMN) and subgenual prefrontal cortex (sgPFC) connectivity may underly maladaptive rumination, which is a major risk factor for depression. To further evaluate such relationship, we investigated whether posterior regions of the DMN, showed elevated connectivity with the sgPFC in remitted depressed patients (rMDD) and whether this connectivity was related to maladaptive rumination. METHODS We examined whether rMDD (N = 20) had elevated EEG posterior DMN - sgPFC functional connectivity when compared to age and sex matched healthy controls (N = 17), and whether this posterior DMN - sgPFC connectivity positively correlated with rumination. Using minimum norm as the source estimation method, we extracted current density maps from six regions of interest (ROIs) within the posterior DMN. EEG source-space functional connectivity was calculated using the Amplitude Envelope Correlation method. RESULTS Relative to controls, rMDD showed increased posterior cingulate cortex (PCC) - sgPFC connectivity in the beta-3 (25-30 Hz) band. As hypothesized, PCC - sgPFC connectivity was positively associated with rumination for rMDD, even after controlling for depression and anxiety. LIMITATIONS The absence of an MDD patient group and the relatively small sample size can limit the generalizability of the results. CONCLUSIONS EEG resting state PCC - sgPFC functional connectivity is significantly elevated in rMDD and is associated with rumination, suggesting that EEG PCC - sgPFC connectivity may be useful as a neural marker to identify individuals at risk for depression.
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Affiliation(s)
- Lars Benschop
- Ghent University, Department of Psychiatry and Medical Psychology, Corneel Heymanslaan 10, 9000 Ghent Belgium.
| | - Tasha Poppa
- Ghent University, Department of Psychiatry and Medical Psychology, Corneel Heymanslaan 10, 9000 Ghent Belgium
| | | | | | - Chris Baeken
- Ghent University, Department of Psychiatry and Medical Psychology, Corneel Heymanslaan 10, 9000 Ghent Belgium; Free University of Brussels; Eindhoven University of Technology
| | | | | | - Marie-Anne Vanderhasselt
- Ghent University, Department of Psychiatry and Medical Psychology, Corneel Heymanslaan 10, 9000 Ghent Belgium
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Uyulan C, Ergüzel TT, Unubol H, Cebi M, Sayar GH, Nezhad Asad M, Tarhan N. Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach. Clin EEG Neurosci 2021; 52:38-51. [PMID: 32491928 DOI: 10.1177/1550059420916634] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.
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Affiliation(s)
- Caglar Uyulan
- Department of Mechatronics, Faculty of Engineering, Bulent Ecevit University, Zonguldak, Turkey
| | - Türker Tekin Ergüzel
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Huseyin Unubol
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Merve Cebi
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Gokben Hizli Sayar
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | | | - Nevzat Tarhan
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
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25
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Jiang H, Dai Z, Lu Q, Yao Z. Magnetoencephalography resting-state spectral fingerprints distinguish bipolar depression and unipolar depression. Bipolar Disord 2020; 22:612-620. [PMID: 31729112 DOI: 10.1111/bdi.12871] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVES In clinical practice, bipolar depression (BD) and unipolar depression (UD) appear to have similar symptoms, causing BD being frequently misdiagnosed as UD, leading to improper treatment decision and outcome. Therefore, it is in urgent need of distinguishing BD from UD based on clinical objective biomarkers as early as possible. Here, we aimed to integrate brain neuroimaging data and an advanced machine learning technique to predict different types of mood disorder patients at the individual level. METHODS Eyes closed resting-state magnetoencephalography (MEG) data were collected from 23 BD, 30 UD, and 31 healthy controls (HC). Individual power spectra were estimated by Fourier transform, and statistic spectral differences were assessed via a cluster permutation test. A support vector machine classifier was further applied to predict different mood disorder types based on discriminative oscillatory power. RESULTS Both BD and UD showed decreased frontal-central gamma/beta ratios comparing to HC, in which gamma power (30-75 Hz) was decreased in BD while beta power (14-30 Hz) was increased in UD vs HC. The support vector machine model obtained significant high classification accuracies distinguishing three groups based on mean gamma and beta power (BD: 79.9%, UD: 81.1%, HC: 76.3%, P < .01). CONCLUSIONS In combination with resting-state MEG data and machine learning technique, it is possible to make an individual and objective prediction for mode disorder types, which in turn has implications for diagnosis precision and treatment decision of mood disorder patients.
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Affiliation(s)
- Haiteng Jiang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Medical College of Nanjing University, Nanjing, China
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26
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Dynamic Properties of Human Default Mode Network in Eyes-Closed and Eyes-Open. Brain Topogr 2020; 33:720-732. [PMID: 32803623 DOI: 10.1007/s10548-020-00792-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 08/08/2020] [Indexed: 10/23/2022]
Abstract
The default mode network (DMN) reflects spontaneous activity in the resting human brain. Previous studies examined the difference in static functional connectivity (sFC) of the DMN between eyes-closed (EC) and eyes-open (EO) using the resting-state functional magnetic resonance imaging (rs-fMRI) data. However, it remains unclear about the difference in dynamic FC (dFC) of the DMN between EC and EO. To this end, we acquired rs-fMRI data from 19 subjects in two different statues (EC and EO) and selected a seed region-of-interest (ROI) at the posterior cingulate cortex (PCC) to generate the sFC map. We identified the DMN consisting of ten clusters that were significantly correlated with the PCC. By using a sliding-window approach, we analyzed the dFC of the DMN. Then, the Newman's modularity algorithm was applied to identify dFC states based on nodal total connectivity strength in each sliding-window. In addition, graph-theory based network analysis was applied to detect dynamic topological properties of the DMN. We identified three group-level dFC states (State1, 2 and 3) that reflects the strength of dFC within the DMN between EC and EO in different time. The following results were reached: (1) no significant difference in sFC between EC and EO, (2) dFC was lower in State2 but higher in State3 in EC than in EO, (3) lower clustering coefficient, local efficiency, and global efficiency, but higher characteristic path length in State2 in EC than in EO, and (4) lower nodal strength in the precuneus (PCUN), PCC, angular gyrus (ANG), middle temporal gyrus (MTG) and medial prefrontal cortex (MPFC) in State3 in EC. These results suggested different resting statuses, EC and EO, may induce different time-varying neural activity in the DMN.
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27
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Wada M, Nakajima S, Tarumi R, Masuda F, Miyazaki T, Tsugawa S, Ogyu K, Honda S, Matsushita K, Kikuchi Y, Fujii S, Blumberger DM, Daskalakis ZJ, Mimura M, Noda Y. Resting-State Isolated Effective Connectivity of the Cingulate Cortex as a Neurophysiological Biomarker in Patients with Severe Treatment-Resistant Schizophrenia. J Pers Med 2020; 10:jpm10030089. [PMID: 32823914 PMCID: PMC7564631 DOI: 10.3390/jpm10030089] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 08/09/2020] [Accepted: 08/12/2020] [Indexed: 11/18/2022] Open
Abstract
Background: The neural basis of treatment-resistant schizophrenia (TRS) remains unclear. Previous neuroimaging studies suggest that aberrant connectivity between the anterior cingulate cortex (ACC) and default mode network (DMN) may play a key role in the pathophysiology of TRS. Thus, we aimed to examine the connectivity between the ACC and posterior cingulate cortex (PCC), a hub of the DMN, computing isolated effective coherence (iCoh), which represents causal effective connectivity. Methods: Resting-state electroencephalogram with 19 channels was acquired from seventeen patients with TRS and thirty patients with non-TRS (nTRS). The iCoh values between the PCC and ACC were calculated using sLORETA software. We conducted four-way analyses of variance (ANOVAs) for iCoh values with group as a between-subject factor and frequency, directionality, and laterality as within-subject factors and post-hoc independent t-tests. Results: The ANOVA and post-hoc t-tests for the iCoh ratio of directionality from PCC to ACC showed significant findings in delta (t45 = 7.659, p = 0.008) and theta (t45 = 8.066, p = 0.007) bands in the left side (TRS
< nTRS). Conclusion: Left delta and theta PCC and ACC iCoh ratio may represent a neurophysiological basis of TRS. Given the preliminary nature of this study, these results warrant further study to confirm the importance of iCoh as a clinical indicator for treatment-resistance.
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Affiliation(s)
- Masataka Wada
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan; (M.W.); (R.T.); (F.M.); (T.M.); (S.T.); (K.O.); (M.M.)
| | - Shinichiro Nakajima
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan; (M.W.); (R.T.); (F.M.); (T.M.); (S.T.); (K.O.); (M.M.)
- Correspondence: (S.N.); (Y.N.); Tel.: +81-3-3353-1211 (ext. 62454) (S.N.); +81-3-3353-1211 (ext. 61857) (Y.N.); Fax: +81-3-5379-0187 (S.N. & Y.N.)
| | - Ryosuke Tarumi
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan; (M.W.); (R.T.); (F.M.); (T.M.); (S.T.); (K.O.); (M.M.)
- Department of Psychiatry, Komagino Hospital, Tokyo 193-8505, Japan
| | - Fumi Masuda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan; (M.W.); (R.T.); (F.M.); (T.M.); (S.T.); (K.O.); (M.M.)
| | - Takahiro Miyazaki
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan; (M.W.); (R.T.); (F.M.); (T.M.); (S.T.); (K.O.); (M.M.)
| | - Sakiko Tsugawa
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan; (M.W.); (R.T.); (F.M.); (T.M.); (S.T.); (K.O.); (M.M.)
| | - Kamiyu Ogyu
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan; (M.W.); (R.T.); (F.M.); (T.M.); (S.T.); (K.O.); (M.M.)
| | - Shiori Honda
- Graduate School of Media and Governance, Keio University, Kanagawa, Tokyo 252-0882, Japan;
| | - Karin Matsushita
- Faculty of Environment and Information Studies, Keio University, Kanagawa, Tokyo 252-0882, Japan; (K.M.); (Y.K.); (S.F.)
| | - Yudai Kikuchi
- Faculty of Environment and Information Studies, Keio University, Kanagawa, Tokyo 252-0882, Japan; (K.M.); (Y.K.); (S.F.)
| | - Shinya Fujii
- Faculty of Environment and Information Studies, Keio University, Kanagawa, Tokyo 252-0882, Japan; (K.M.); (Y.K.); (S.F.)
| | - Daniel M. Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON M6J 1H4, Canada; (D.M.B.); (Z.J.D.)
| | - Zafiris J. Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON M6J 1H4, Canada; (D.M.B.); (Z.J.D.)
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan; (M.W.); (R.T.); (F.M.); (T.M.); (S.T.); (K.O.); (M.M.)
| | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan; (M.W.); (R.T.); (F.M.); (T.M.); (S.T.); (K.O.); (M.M.)
- Correspondence: (S.N.); (Y.N.); Tel.: +81-3-3353-1211 (ext. 62454) (S.N.); +81-3-3353-1211 (ext. 61857) (Y.N.); Fax: +81-3-5379-0187 (S.N. & Y.N.)
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28
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Dual-process contributions to creativity in jazz improvisations: An SPM-EEG study. Neuroimage 2020; 213:116632. [DOI: 10.1016/j.neuroimage.2020.116632] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 02/05/2020] [Accepted: 02/10/2020] [Indexed: 12/19/2022] Open
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Wu L, Wang XQ, Yang Y, Dong TF, Lei L, Cheng QQ, Li SX. Spatio-temporal dynamics of EEG features during sleep in major depressive disorder after treatment with escitalopram: a pilot study. BMC Psychiatry 2020; 20:124. [PMID: 32171290 PMCID: PMC7071588 DOI: 10.1186/s12888-020-02519-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 02/26/2020] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Previous studies have shown escitalopram is related to sleep quality. However, effects of escitalopram on dynamics of electroencephalogram (EEG) features especially during different sleep stages have not been reported. This study may help to reveal pharmacological mechanism underlying escitalopram treatment. METHODS The spatial and temporal responses of patients with major depressive disorder (MDD) to escitalopram treatment were analyzed in this study. Eleven MDD patients and eleven healthy control subjects who completed eight weeks' treatment of escitalopram were included in the final statistics. Six-channel sleep EEG signals were acquired during sleep. Power spectrum and nonlinear dynamics were used to analyze the spatio-temporal dynamics features of the sleep EEG after escitalopram treatment. RESULTS For temporal dynamics: after treatment, there was a significant increase in the relative energy (RE) of δ1 band (0.5 - 2 Hz), accompanied by a significant decrease in the RE of β2 band (20 - 30 Hz). Lempel-Ziv complexity and Co - complexity values were significantly lower. EEG changes at different sleep stages also showed the same regulation as throughout the night sleep. For spatio dynamics: after treatment, the EEG response of the left and right hemisphere showed asymmetry. Regarding band-specific EEG complexity estimations, δ1 and β2 in stage-1 and δ1 in stage-2 sleep stage in frontal cortex is found to be much more sensitive to escitalopram treatment in comparison to central and occipital cortices. CONCLUSIONS The sleep quality of MDD patients improved, EEG response occurred asymmetry in left and right hemispheres due to escitalopram treatment, and frontal cortex is found to be much more sensitive to escitalopram treatment. These findings may contribute to a comprehensive understanding of the pharmacological mechanism of escitalopram in the treatment of depression.
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Affiliation(s)
- Li Wu
- School of automation Hangzhou Dianzi University, HangZhou Economic Development Zone, 1158, 2# Road, BaiYang Street, Hangzhou, 310018 Zhejiang China
| | - Xue-Qin Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Yong Yang
- School of automation Hangzhou Dianzi University, HangZhou Economic Development Zone, 1158, 2# Road, BaiYang Street, Hangzhou, 310018 Zhejiang China
| | - Teng-Fei Dong
- School of automation Hangzhou Dianzi University, HangZhou Economic Development Zone, 1158, 2# Road, BaiYang Street, Hangzhou, 310018 Zhejiang China
| | - Ling Lei
- School of automation Hangzhou Dianzi University, HangZhou Economic Development Zone, 1158, 2# Road, BaiYang Street, Hangzhou, 310018 Zhejiang China
| | - Qi-Qi Cheng
- School of automation Hangzhou Dianzi University, HangZhou Economic Development Zone, 1158, 2# Road, BaiYang Street, Hangzhou, 310018 Zhejiang China
| | - Su-Xia Li
- National Institute on Drug Dependence, Peking University, Beijing, 100191 China
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Privodnova EY, Volf NV, Knyazev GG. The Evaluation of Creative Ideas in Older and Younger Adults. J PSYCHOPHYSIOL 2020. [DOI: 10.1027/0269-8803/a000232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract. The ability to solve problems of divergent type is one of the most intact functions in successful aging. However, neurophysiologic mechanisms that support the efficiency of creative thinking remain largely unknown. This study was aimed to investigate age-related difference in localized induced electroencephalogram (EEG) changes during creative idea evaluation stage of divergent problem-solving (Alternate Uses Task), using standardized low-resolution brain electromagnetic tomography. Younger (45 women, 44 men, Mage = 22.1 years, age range: 18–30 years) and older adults (46 women, 43 men, Mage = 64.9 years, age range: 55–75 years) participated in the study. Higher synchronization in individually adjusted theta frequency band [from (individual alpha peak frequency −6 Hz) to (individual alpha peak frequency −4 Hz)] in anterior areas with the maximum values in anterior cingulate gyrus was revealed in older as compared with younger participants by group contrast. Higher desynchronization in wide beta range [from (individual alpha peak frequency +2 Hz) to 30 Hz] was localized in posterior brain regions with the highest values in posterior cingulate gyrus, precuneus, and parietal lobule in older adults. Induced beta 2 synchronization was positively correlated with originality (as measured by the mean frequency of ideas) in younger and years of education in older subjects. Based on the data, it was supposed that controlling the decision-making processes is more important for older adults while maintenance of the internal image of elements’ recombination may play essential role for younger subjects.
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Affiliation(s)
- Evgeniya Yu. Privodnova
- Federal State Budgetary Scientific Institution “Scientific Research Institute of Physiology and Basic Medicine”, Novosibirsk, Russian Federation
- Department of Psychology, Novosibirsk State University, Novosibirsk, Russian Federation
| | - Nina V. Volf
- Federal State Budgetary Scientific Institution “Scientific Research Institute of Physiology and Basic Medicine”, Novosibirsk, Russian Federation
- Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russian Federation
| | - Gennady G. Knyazev
- Federal State Budgetary Scientific Institution “Scientific Research Institute of Physiology and Basic Medicine”, Novosibirsk, Russian Federation
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31
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Abnormal EEG Power Spectrum in Individuals with High Autistic Personality Traits: an eLORETA Study. JOURNAL OF PSYCHOPATHOLOGY AND BEHAVIORAL ASSESSMENT 2019. [DOI: 10.1007/s10862-019-09777-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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32
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Zrenner B, Zrenner C, Gordon PC, Belardinelli P, McDermott EJ, Soekadar SR, Fallgatter AJ, Ziemann U, Müller-Dahlhaus F. Brain oscillation-synchronized stimulation of the left dorsolateral prefrontal cortex in depression using real-time EEG-triggered TMS. Brain Stimul 2019; 13:197-205. [PMID: 31631058 DOI: 10.1016/j.brs.2019.10.007] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 10/02/2019] [Accepted: 10/09/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (DLPFC) is an effective treatment for major depressive disorder (MDD), but response rates are low and effect sizes small. Synchronizing TMS pulses with instantaneous brain oscillations can reduce variability and increase efficacy of TMS-induced plasticity. OBJECTIVE To study whether brain oscillation-synchronized rTMS is feasible, safe and has neuromodulatory effects when targeting the DLPFC of patients with MDD. METHODS Using real-time EEG-triggered TMS we conducted a pseudo-randomized controlled single-session crossover trial of brain oscillation-synchronized rTMS of left DLPFC in 17 adult patients with antidepressant-resistant MDD. Stimulation conditions in separate sessions were: (1) rTMS triggered at the negative EEG peak of instantaneous alpha oscillations (alpha-synchronized rTMS), (2) a variation of intermittent theta-burst stimulation (modified iTBS), and (3) a random alpha phase control condition. RESULTS Triggering TMS at the negative peak of instantaneous alpha oscillations by real-time analysis of the electrode F5 EEG signal was successful in 15 subjects. Two subjects reported mild transient discomfort at the site of stimulation during stimulation; no serious adverse events were reported. Alpha-synchronized rTMS, but not modified iTBS or the random alpha phase control condition, reduced resting-state alpha activity in left DLPFC and increased TMS-induced beta oscillations over frontocentral channels. CONCLUSIONS Alpha-synchronized rTMS of left DLPFC is feasible, safe and has specific single-session neuromodulatory effects in patients with antidepressant-resistant MDD. Future studies need to further elucidate the mechanisms, optimize the parameters and investigate the therapeutic potential and efficacy of brain oscillation-synchronized rTMS in MDD.
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Affiliation(s)
- Brigitte Zrenner
- Department of Neurology and Stroke, And Hertie Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Germany
| | - Christoph Zrenner
- Department of Neurology and Stroke, And Hertie Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Germany
| | - Pedro Caldana Gordon
- Department of Neurology and Stroke, And Hertie Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Germany; Service of Interdisciplinary Neuromodulation, Laboratory of Neuroscience (LIM27) and National Institute of Biomarkers in Psychiatry (INBioN), Department and Institute of Psychiatry, Hospital Das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Paolo Belardinelli
- Department of Neurology and Stroke, And Hertie Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Germany
| | - Eric J McDermott
- Department of Neurology and Stroke, And Hertie Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Germany
| | - Surjo R Soekadar
- Department of Psychiatry and Psychotherapy, Eberhard Karls University Tübingen, Germany; Clinical Neurotechnology Laboratory, Neuroscience Research Center (NWFZ) & Department of Psychiatry and Psychotherapy, Charité - University Medicine Berlin, Germany
| | - Andreas J Fallgatter
- Department of Psychiatry and Psychotherapy, Eberhard Karls University Tübingen, Germany
| | - Ulf Ziemann
- Department of Neurology and Stroke, And Hertie Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Germany.
| | - Florian Müller-Dahlhaus
- Department of Neurology and Stroke, And Hertie Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Germany; Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Germany
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Shtark MB, Kozlova LI, Bezmaternykh DD, Mel'nikov MY, Savelov AA, Sokhadze EM. Neuroimaging Study of Alpha and Beta EEG Biofeedback Effects on Neural Networks. Appl Psychophysiol Biofeedback 2019; 43:169-178. [PMID: 29926265 DOI: 10.1007/s10484-018-9396-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Neural networks interaction was studied in healthy men (20-35 years old) who underwent 20 sessions of EEG biofeedback training outside the MRI scanner, with concurrent fMRI-EEG scans at the beginning, middle, and end of the course. The study recruited 35 subjects for EEG biofeedback, but only 18 of them were considered as "successful" in self-regulation of target EEG bands during the whole course of training. Results of fMRI analysis during EEG biofeedback are reported only for these "successful" trainees. The experimental group (N = 23 total, N = 13 "successful") upregulated the power of alpha rhythm, while the control group (N = 12 total, N = 5 "successful") beta rhythm, with the protocol instructions being as for alpha training in both. The acquisition of the stable skills of alpha self-regulation was followed by the weakening of the irrelevant links between the cerebellum and visuospatial network (VSN), as well as between the VSN, the right executive control network (RECN), and the cuneus. It was also found formation of a stable complex based on the interaction of the precuneus, the cuneus, the VSN, and the high level visuospatial network (HVN), along with the strengthening of the interaction of the anterior salience network (ASN) with the precuneus. In the control group, beta enhancement training was accompanied by weakening of interaction between the precuneus and the default mode network, and a decrease in connectivity between the cuneus and the primary visual network (PVN). The differences between the alpha training group and the control group increased successively during training. Alpha training was characterized by a less pronounced interaction of the network formed by the PVN and the HVN, as well as by an increased interaction of the cerebellum with the precuneus and the RECN. The study demonstrated the differences in the structure and interaction of neural networks involved into alpha and beta generating systems forming and functioning, which should be taken into account during planning neurofeedback interventions. Possibility of using fMRI-guided biofeedback organized according to the described neural networks interaction may advance more accurate targeting specific symptoms during neurotherapy.
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Affiliation(s)
- Mark B Shtark
- Research Institute of Molecular Biology and Biophysics, Novosibirsk, Russia, 630117.,Institute of Medicine and Psychology, Novosibirsk State University, Novosibirsk, Russia, 630090
| | - Lyudmila I Kozlova
- Research Institute of Molecular Biology and Biophysics, Novosibirsk, Russia, 630117.,Institute of Medicine and Psychology, Novosibirsk State University, Novosibirsk, Russia, 630090
| | - Dmitriy D Bezmaternykh
- Research Institute of Molecular Biology and Biophysics, Novosibirsk, Russia, 630117.,Institute of Medicine and Psychology, Novosibirsk State University, Novosibirsk, Russia, 630090
| | - Mikhail Ye Mel'nikov
- Research Institute of Molecular Biology and Biophysics, Novosibirsk, Russia, 630117.,Institute of Medicine and Psychology, Novosibirsk State University, Novosibirsk, Russia, 630090
| | - Andrey A Savelov
- International Tomography Center, Siberian Division of Russian Academy of Sciences, Novosibirsk, Russia, 630090
| | - Estate M Sokhadze
- University of South Carolina, School of Medicine-Greenville, Greenville, SC, 29605, USA. .,Department of Biomedical Sciences, University of South Carolina, School of Medicine-Greenville, 200 Patewood Dr. #A200, Greenville, SC, 29615, USA.
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Ghaderi AH, Nazari MA, Darooneh AH. Functional brain segregation changes during demanding mathematical task. Int J Neurosci 2019; 129:904-915. [DOI: 10.1080/00207454.2019.1586688] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Amir Hossein Ghaderi
- Vision: Science to Applications (VISTA) Program, York University, Toronto, ON, Canada
- Iranian Neuro-Wave Lab, Vilashahr, Isfahan, Iran
- Division of Cognitive Neuroscience, University of Tabriz, Tabriz, Iran
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Gao J, Leung HK, Wu BWY, Skouras S, Sik HH. The neurophysiological correlates of religious chanting. Sci Rep 2019; 9:4262. [PMID: 30862790 PMCID: PMC6414545 DOI: 10.1038/s41598-019-40200-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 02/11/2019] [Indexed: 01/19/2023] Open
Abstract
Despite extensive research on various types of meditation, research on the neural correlates of religious chanting is in a nascent stage. Using multi-modal electrophysiological and neuroimaging methods, we illustrate that during religious chanting, the posterior cingulate cortex shows the largest decrease in eigenvector centrality, potentially due to regional endogenous generation of delta oscillations. Our data show that these functional effects are not due to peripheral cardiac or respiratory activity, nor due to implicit language processing. Finally, we suggest that the neurophysiological correlates of religious chanting are likely different from those of meditation and prayer, and would possibly induce distinctive psychotherapeutic effects.
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Affiliation(s)
- Junling Gao
- Buddhism and Science Research Lab, Centre of Buddhist Studies, The University of Hong Kong, Pokfulam, Hong Kong
| | - Hang Kin Leung
- Buddhism and Science Research Lab, Centre of Buddhist Studies, The University of Hong Kong, Pokfulam, Hong Kong
| | - Bonnie Wai Yan Wu
- Buddhism and Science Research Lab, Centre of Buddhist Studies, The University of Hong Kong, Pokfulam, Hong Kong
| | - Stavros Skouras
- Department of Biological and Medical Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway
| | - Hin Hung Sik
- Buddhism and Science Research Lab, Centre of Buddhist Studies, The University of Hong Kong, Pokfulam, Hong Kong.
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Abstract
Altered power of resting-state neurophysiological activity has been associated with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which commonly co-occur. We compared resting-state neurophysiological power in children with ASD, ADHD, co-occurring ASD + ADHD, and typically developing controls. Children with ASD (ASD/ASD + ADHD) showed reduced theta and alpha power compared to children without ASD (controls/ADHD). Children with ADHD (ADHD/ASD + ADHD) displayed decreased delta power compared to children without ADHD (ASD/controls). Children with ASD + ADHD largely presented as an additive co-occurrence with deficits of both disorders, although reduced theta compared to ADHD-only and reduced delta compared to controls suggested some unique markers. Identifying specific neurophysiological profiles in ASD and ADHD may assist in characterising more homogeneous subgroups to inform treatment approaches and aetiological investigations.
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EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions. J Neurosci Methods 2019; 318:34-46. [PMID: 30802472 DOI: 10.1016/j.jneumeth.2019.02.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 11/29/2018] [Accepted: 02/17/2019] [Indexed: 11/24/2022]
Abstract
BACKGROUND Spatial and temporal resolution of brain network activity can be improved by combining different modalities. Functional Magnetic Resonance Imaging (fMRI) provides full brain coverage with limited temporal resolution, while electroencephalography (EEG), estimates cortical activity with high temporal resolution. Combining them may provide improved network characterization. NEW METHOD We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 1st and 2nd temporal derivatives in voxel-wise general linear models (GLM). HRF shapes were derived from EEG-fMRI time courses during "resting-state", visual oddball and semantic decision paradigms. RESULTS The resulting GLM F-maps self-organized into several different large-scale brain networks (LSBNs) often with different timing between EEG and fMRI revealed through differences in GLM-derived HRF shapes (e.g., with a lower time to peak than the canonical HRF). We demonstrate that some EEG spatiospectral patterns (related to concurrent fMRI) are weakly task-modulated. COMPARISON WITH EXISTING METHOD(S) Previously, we demonstrated 14 independent EEG spatiospectral patterns within this EEG dataset, stable across the resting-state, visual oddball and semantic decision paradigms. Here, we demonstrate that their time courses are significantly correlated with fMRI dynamics organized into LSBN structures. EEG-fMRI derived HRF peak appears earlier than the canonical HRF peak, which suggests limitations when assuming a canonical HRF shape in EEG-fMRI. CONCLUSIONS This is the first study examining EEG-fMRI relationships among independent EEG spatiospectral patterns over different paradigms. The findings highlight the importance of considering different HRF shapes when spatiotemporally characterizing brain networks using EEG and fMRI.
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Prestel M, Steinfath TP, Tremmel M, Stark R, Ott U. fMRI BOLD Correlates of EEG Independent Components: Spatial Correspondence With the Default Mode Network. Front Hum Neurosci 2018; 12:478. [PMID: 30542275 PMCID: PMC6277921 DOI: 10.3389/fnhum.2018.00478] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 11/14/2018] [Indexed: 01/24/2023] Open
Abstract
Goal: We aimed to identify electroencephalographic (EEG) signal fluctuations within independent components (ICs) that correlate to spontaneous blood oxygenation level dependent (BOLD) activity in regions of the default mode network (DMN) during eyes-closed resting state. Methods: We analyzed simultaneously acquired EEG and functional magnetic resonance imaging (fMRI) eyes-closed resting state data in a convenience sample of 30 participants. IC analysis (ICA) was used to decompose the EEG time-series and common ICs were identified using data-driven IC clustering across subjects. The IC time courses were filtered into seven frequency bands, convolved with a hemeodynamic response function (HRF) and used to model spontaneous fMRI signal fluctuations across the brain. In parallel, group ICA analysis was used to decompose the fMRI signal into ICs from which the DMN was identified. Frequency and IC cluster associated hemeodynamic correlation maps obtained from the regression analysis were spatially correlated with the DMN. To investigate the reliability of our findings, the analyses were repeated with data collected from the same subjects 1 year later. Results: Our results indicate a relationship between power fluctuations in the delta, theta, beta and gamma frequency range and the DMN in different EEG ICs in our sample as shown by small to moderate spatial correlations at the first measurement (0.234 < |r| < 0.346, p < 0.0001). Furthermore, activity within an EEG component commonly identified as eye movements correlates with BOLD activity within regions of the DMN. In addition, we demonstrate that correlations between EEG ICs and the BOLD signal during rest are in part stable across time. Discussion: We show that ICA source separated EEG signals can be used to investigate electrophysiological correlates of the DMN. The relationship between the eye movement component and the DMN points to a behavioral association between DMN activity and the level of eye movement or the presence of neuronal activity in this component. Previous findings of an association between frontal midline theta activity and the DMN were replicated.
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Affiliation(s)
- Marcel Prestel
- Bender Institute of Neuroimaging, Justus Liebig University Giessen, Giessen, Germany
| | - Tim Paul Steinfath
- Bender Institute of Neuroimaging, Justus Liebig University Giessen, Giessen, Germany
| | - Michael Tremmel
- Bender Institute of Neuroimaging, Justus Liebig University Giessen, Giessen, Germany
| | - Rudolf Stark
- Bender Institute of Neuroimaging, Justus Liebig University Giessen, Giessen, Germany
| | - Ulrich Ott
- Bender Institute of Neuroimaging, Justus Liebig University Giessen, Giessen, Germany
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Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI. Sci Rep 2018; 8:8902. [PMID: 29891929 PMCID: PMC5995808 DOI: 10.1038/s41598-018-27187-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 05/30/2018] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.
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Multimodal Functional and Structural Brain Connectivity Analysis in Autism: A Preliminary Integrated Approach With EEG, fMRI, and DTI. IEEE Trans Cogn Dev Syst 2018. [DOI: 10.1109/tcds.2017.2680408] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Mash LE, Reiter MA, Linke AC, Townsend J, Müller RA. Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective. Dev Neurobiol 2018; 78:456-473. [PMID: 29266810 PMCID: PMC5897150 DOI: 10.1002/dneu.22570] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 12/18/2017] [Accepted: 12/18/2017] [Indexed: 12/22/2022]
Abstract
Atypical functional connectivity has been implicated in autism spectrum disorders (ASDs). However, the literature to date has been largely inconsistent, with mixed and conflicting reports of hypo- and hyper-connectivity. These discrepancies are partly due to differences between various neuroimaging modalities. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) measure distinct indices of functional connectivity (e.g., blood-oxygenation level-dependent [BOLD] signal vs. electrical activity). Furthermore, each method has unique benefits and disadvantages with respect to spatial and temporal resolution, vulnerability to specific artifacts, and practical implementation. Thus far, functional connectivity research on ASDs has remained almost exclusively unimodal; therefore, interpreting findings across modalities remains a challenge. Multimodal integration of fMRI, EEG, and MEG data is critical in resolving discrepancies in the literature, and working toward a unifying framework for interpreting past and future findings. This review aims to provide a theoretical foundation for future multimodal research on ASDs. First, we will discuss the merits and shortcomings of several popular theories in ASD functional connectivity research, using examples from the literature to date. Next, the neurophysiological relationships between imaging modalities, including their relationship with invasive neural recordings, will be reviewed. Finally, methodological approaches to multimodal data integration will be presented, and their future application to ASDs will be discussed. Analyses relating transient patterns of neural activity ("states") are particularly promising. This strategy provides a comparable measure across modalities, captures complex spatiotemporal patterns, and is a natural extension of recent dynamic fMRI research in ASDs. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 456-473, 2018.
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Affiliation(s)
- Lisa E. Mash
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Maya A. Reiter
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Annika C. Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Jeanne Townsend
- University of California, San Diego, Department of Neurosciences
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
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Increased hippocampal-prefrontal functional connectivity in insomnia. Neurobiol Learn Mem 2018; 160:144-150. [PMID: 29448003 DOI: 10.1016/j.nlm.2018.02.006] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 01/18/2018] [Accepted: 02/07/2018] [Indexed: 12/20/2022]
Abstract
Insomnia Disorder (ID) is the second-most common mental disorder and has a far-reaching impact on daytime functioning. A meta-analysis indicates that, of all cognitive domains, declarative memory involving the hippocampus is most affected in insomnia. Hippocampal functioning has consistently been shown to be sensitive to experimental sleep deprivation. Insomnia however differs from sleep deprivation in many aspects, and findings on hippocampal structure and function have been equivocal. The present study used both structural and resting-state functional Magnetic Resonance Imaging in a larger sample than previously reported to evaluate hippocampal volume and functional connectivity in ID. Included were 65 ID patients (mean age = 48.3 y ± 14.0, 17 males) and 65 good sleepers (mean age = 44.1 y ± 15.2, 23 males). Insomnia severity was assessed with the Insomnia Severity Index (ISI), subjective sleep with the Consensus Sleep Diary (CSD) and objective sleep by two nights of polysomnography (PSG). Seed-based analysis showed a significantly stronger connectivity of the bilateral hippocampus with the left middle frontal gyrus in ID than in controls (p = .035, cluster based correction for multiple comparisons). Further analyses across all participants moreover showed that individual differences in the strength of this connectivity were associated with insomnia severity (ISI, r = 0.371, p = 9.3e-5) and with subjective sleep quality (CSD sleep efficiency, r = -0.307, p = .009) (all p FDR-corrected). Hippocampal volume did not differ between ID and controls. The findings indicate more severe insomnia and worse sleep quality in people with a stronger functional connectivity between the bilateral hippocampus and the left middle frontal gyrus, part of a circuit that characteristically activates with maladaptive rumination and deactivates with sleep.
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Baskaran A, Farzan F, Milev R, Brenner CA, Alturi S, Pat McAndrews M, Blier P, Evans K, Foster JA, Frey BN, Giacobbe P, Lam RW, Leri F, MacQueen GM, Müller DJ, Parikh SV, Rotzinger S, Soares CN, Strother SC, Turecki G, Kennedy SH. The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: A pilot study. J Affect Disord 2018; 227:542-549. [PMID: 29169123 DOI: 10.1016/j.jad.2017.10.028] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 09/25/2017] [Accepted: 10/16/2017] [Indexed: 01/21/2023]
Abstract
BACKGROUND This study aims to compare the effectiveness of EEG frequency band activity including interhemispheric asymmetry and prefrontal theta cordance in predicting response to escitalopram therapy at 8-weeks post-treatment, in a multi-site initiative. METHODS Resting state 64-channel EEG data were recorded from 44 patients with a diagnosis of major depressive disorder (MDD) as part of a larger, multisite discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). Clinical response was measured at 8-weeks post-treatment as change from baseline Montgomery-Asberg Depression Rating Scale (MADRS) score of 50% or more. EEG measures were analyzed at (1) pre-treatment baseline (2) 2 weeks post-treatment and (3) as an ''early change" variable defined as change in EEG from baseline to 2 weeks post-treatment. RESULTS At baseline, treatment responders showed elevated absolute alpha power in the left hemisphere while non-responders showed the opposite. Responders further exhibited a cortical asymmetry in the parietal region. Groups also differed in pre-treatment relative delta power with responders showing greater power in the right hemisphere over the left while non-responders showed the opposite. At 2 weeks post-treatment, responders exhibited greater absolute beta power in the left hemisphere relative to the right and the opposite was noted for non-responders. A reverse pattern was noted for absolute and relative delta power at 2 weeks post-treatment. Responders exhibited early reductions in relative alpha power and early increments in relative theta power. Non-responders showed a significant early increase in prefrontal theta cordance. CONCLUSIONS Hemispheric asymmetries in the alpha and delta bands at baseline and at 2 weeks post-treatment have moderately strong predictive utility in predicting response to antidepressant treatment.
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Affiliation(s)
- Anusha Baskaran
- Centre for Neuroscience Studies, Queen's Unviersty, Kingston, Canada; Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.
| | - Faranak Farzan
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, Canada
| | - Roumen Milev
- Centre for Neuroscience Studies, Queen's Unviersty, Kingston, Canada; Department of Psychiatry, Queen's University, Kingston, Canada
| | - Colleen A Brenner
- Department of Psychology, Loma Linda University, Loma Linda, United States
| | - Sravya Alturi
- Department of Psychiatry, Queen's University, Kingston, Canada
| | | | - Pierre Blier
- Brain and Mind Research Institute, University of Ottawa, Ottawa, Canada
| | | | - Jane A Foster
- Krembil Research Institute, University Health Network, Toronto, Canada; Department of Psychiatry & Behavioural Neurosciences, McMaster University, Hamilton, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Benicio N Frey
- Psychiatry & Behavioural Neurosciences, McMaster University, Hamilton, Canada; Mood Disorders Program & Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Canada
| | - Peter Giacobbe
- Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Psychiatry, University Health Network, Toronto, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Francesco Leri
- Department of Psychology, University of Guelph, Guelph, Canada
| | - Glenda M MacQueen
- The Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, Canada; Pharmacogenetics Research Clinic, Centre for Addiction and Mental Health, Toronto, Canada
| | - Sagar V Parikh
- Department of Psychiatry, University of Michigan, Ann Arbor, United States
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Psychiatry, University Health Network, Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Claudio N Soares
- Department of Psychiatry, Queen's University, Kingston, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | | | - Gustavo Turecki
- Department of Psychiatry, McGill University, Montreal, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Psychiatry, University Health Network, Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
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Vieira de Melo BB, Trigueiro MJ, Rodrigues PP. Systematic overview of neuroanatomical differences in ADHD: Definitive evidence. Dev Neuropsychol 2017; 43:52-68. [DOI: 10.1080/87565641.2017.1414821] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Bruno Bastos Vieira de Melo
- Occupational Therapy Department, Higher School of Health, Polytechnic Institute of Porto, Porto, Portugal
- Faculty of Education Sciences, University of Vigo
| | - Maria João Trigueiro
- Occupational Therapy Department, Higher School of Health, Polytechnic Institute of Porto, Porto, Portugal
| | - Pedro Pereira Rodrigues
- CINTESIS & Community Medicine, Information and Health Decision Sciences Department, Faculty of Medicine, University of Porto, Porto, Portugal
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Whitton AE, Deccy S, Ironside ML, Kumar P, Beltzer M, Pizzagalli DA. Electroencephalography Source Functional Connectivity Reveals Abnormal High-Frequency Communication Among Large-Scale Functional Networks in Depression. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 3:50-58. [PMID: 29397079 DOI: 10.1016/j.bpsc.2017.07.001] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 06/13/2017] [Accepted: 07/03/2017] [Indexed: 12/29/2022]
Abstract
BACKGROUND Functional magnetic resonance imaging studies of resting-state functional connectivity have shown that major depressive disorder (MDD) is characterized by increased connectivity within the default mode network (DMN) and between the DMN and the frontoparietal network (FPN). However, much remains unknown about abnormalities in higher frequency (>1 Hz) synchronization. Findings of abnormal synchronization in specific frequencies would contribute to a better understanding of the potential neurophysiological origins of disrupted functional connectivity in MDD. METHODS We used the high temporal resolution of electroencephalography to compare the spectral properties of resting-state functional connectivity in individuals with MDD (n = 65) with healthy control subjects (n = 79) and examined the extent to which connectivity disturbances were evident in a third sample of individuals in remission from depression (n = 30). Exact low resolution electromagnetic tomography was used to compute intracortical activity from regions within the DMN and FPN, and functional connectivity was computed using lagged phase synchronization. RESULTS Compared to control subjects, the MDD group showed greater within-DMN beta 2 band (18.5-21 Hz) connectivity and greater beta 1 band (12.5-18 Hz) connectivity between the DMN and FPN. This hyperconnectivity was not observed in the remitted MDD group. However, greater beta 1 band DMN-FPN connectivity was associated with more frequent depressive episodes since first depression onset, even after controlling for current symptom severity. CONCLUSIONS These findings extend our understanding of the neurophysiological basis of abnormal resting-state functional connectivity in MDD and indicate that elevations in high-frequency DMN-FPN connectivity may be a neural marker linked to a more recurrent illness course.
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Affiliation(s)
- Alexis E Whitton
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Stephanie Deccy
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Manon L Ironside
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Poornima Kumar
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Miranda Beltzer
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
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The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue. IEEE J Biomed Health Inform 2017; 21:743-755. [PMID: 28113875 DOI: 10.1109/jbhi.2016.2544061] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Knyazev GG, Savostyanov AN, Bocharov AV, Slobodskaya HR, Bairova NB, Tamozhnikov SS, Stepanova VV. Effortful control and resting state networks: A longitudinal EEG study. Neuroscience 2017; 346:365-381. [DOI: 10.1016/j.neuroscience.2017.01.031] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Revised: 01/14/2017] [Accepted: 01/17/2017] [Indexed: 10/20/2022]
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Ranzi P, Freund JA, Thiel CM, Herrmann CS. Encephalography Connectivity on Sources in Male Nonsmokers after Nicotine Administration during the Resting State. Neuropsychobiology 2017; 74:48-59. [PMID: 27802427 DOI: 10.1159/000450711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 09/09/2016] [Indexed: 11/19/2022]
Abstract
We present an encephalography (EEG) connectivity study where 30 healthy male nonsmokers were randomly allocated either to a nicotine group (14 subjects, 7 mg of transdermal nicotine) or to a placebo group. EEG activity was recorded in an eyes-open (EO) and eyes-closed (EC) condition before and after drug administration. This is a reanalysis of a previous dataset. Through a source reconstruction procedure, we extracted 13 time series representing 13 sources belonging to a resting-state network. Here, we conducted connectivity analysis (renormalized partial directed coherence; rPDC) on sources, focusing on the frequency range of 8.5-18.4 Hz, subdivided into 3 frequency bands (α1, α2, and β1) with the hypothesis that an increase in vigilance would modulate connectivity. Furthermore, a phase-amplitude coupling (mean resultant vector length; VL) analysis, was performed investigating whether an increase of vigilance would modulate phase-amplitude coupling. In the VL analysis we estimated the coupling of the phases of 3 low frequencies (α1, α2, and β1), respectively, with the amplitude of high-frequency oscillations (30-40 Hz, low γ). With rPDC we found that during the EC condition, nicotine decreased feedback connectivity (from the precentral gyrus to precuneus, angular gyrus, cuneus and superior occipital gyrus) at 10.5-12.4 Hz. The VL analysis showed nicotine-induced increases in coupling at 10.5-18.4 Hz in the precuneus, cuneus and superior occipital gyrus during the EC condition. During the EO condition, no significant results were found in connectivity or phase-amplitude coupling measures at any frequency range. In conclusion, the results suggest that nicotine potentially increases the level of vigilance in the EC condition.
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Affiliation(s)
- Paolo Ranzi
- Experimental Psychology Group, Department of Psychology, Cluster of Excellence 'Hearing4all', European Medical School, Carl von Ossietzky University, Oldenburg, Germany
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Baenninger A, Palzes VA, Roach BJ, Mathalon DH, Ford JM, Koenig T. Abnormal Coupling Between Default Mode Network and Delta and Beta Band Brain Electric Activity in Psychotic Patients. Brain Connect 2017; 7:34-44. [PMID: 27897031 DOI: 10.1089/brain.2016.0456] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Common-phase synchronization of neuronal oscillations is a mechanism by which distributed brain regions can be integrated into transiently stable networks. Based on the hypothesis that schizophrenia is characterized by deficits in functional integration within neuronal networks, this study aimed to explore whether psychotic patients exhibit differences in brain regions involved in integrative mechanisms. We report an electroencephalography (EEG)-informed functional magnetic resonance imaging analysis of eyes-open resting-state data collected from patients and healthy controls at two study sites. Global field synchronization (GFS) was chosen as an EEG measure indicating common-phase synchronization across electrodes. Several brain clusters appeared to be coupled to GFS differently in patients and controls. Activation in brain areas belonging to the default mode network was negatively associated to GFS delta (1-3.5 Hz) and positively to GFS beta (13-30 Hz) bands in patients, whereas controls showed an opposite pattern for both GFS frequency bands in those regions; activation in the extrastriate visual cortex was inversely related to GFS alpha1 (8.5-10.5 Hz) band in healthy controls, while patients had a tendency toward a positive relationship. Taken together, the GFS measure might be useful for detecting additional aspects of deficient functional network integration in psychosis.
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Affiliation(s)
- Anja Baenninger
- 1 Translational Research Center, University Hospital of Psychiatry, University of Bern , Bern, Switzerland .,2 Center for Cognition, Learning and Memory, University of Bern , Bern, Switzerland
| | | | - Brian J Roach
- 3 San Francisco VA Medical Center , San Francisco, California
| | - Daniel H Mathalon
- 3 San Francisco VA Medical Center , San Francisco, California.,4 Department of Psychiatry, University of California San Francisco , San Francisco, California
| | - Judith M Ford
- 3 San Francisco VA Medical Center , San Francisco, California.,4 Department of Psychiatry, University of California San Francisco , San Francisco, California
| | - Thomas Koenig
- 1 Translational Research Center, University Hospital of Psychiatry, University of Bern , Bern, Switzerland .,2 Center for Cognition, Learning and Memory, University of Bern , Bern, Switzerland
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Case M, Zhang H, Mundahl J, Datta Y, Nelson S, Gupta K, He B. Characterization of functional brain activity and connectivity using EEG and fMRI in patients with sickle cell disease. NEUROIMAGE-CLINICAL 2016; 14:1-17. [PMID: 28116239 PMCID: PMC5226854 DOI: 10.1016/j.nicl.2016.12.024] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 12/19/2016] [Indexed: 11/29/2022]
Abstract
Sickle cell disease (SCD) is a red blood cell disorder that causes many complications including life-long pain. Treatment of pain remains challenging due to a poor understanding of the mechanisms and limitations to characterize and quantify pain. In the present study, we examined simultaneously recording functional MRI (fMRI) and electroencephalogram (EEG) to better understand neural connectivity as a consequence of chronic pain in SCD patients. We performed independent component analysis and seed-based connectivity on fMRI data. Spontaneous power and microstate analysis was performed on EEG-fMRI data. ICA analysis showed that patients lacked activity in the default mode network (DMN) and executive control network compared to controls. EEG-fMRI data revealed that the insula cortex's role in salience increases with age in patients. EEG microstate analysis showed patients had increased activity in pain processing regions. The cerebellum in patients showed a stronger connection to the periaqueductal gray matter (involved in pain inhibition), and negative connections to pain processing areas. These results suggest that patients have reduced activity of DMN and increased activity in pain processing regions during rest. The present findings suggest resting state connectivity differences between patients and controls can be used as novel biomarkers of SCD pain. Simultaneous EEG-fMRI recordings revealed altered connectivity in sickle cell patients. Reduced activity observed in default mode network and executive control network. Patients' salience network strength increases with age; opposite seen in controls. EEG-fMRI parameters reflect disease severity in sickle cell patients.
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Key Words
- BOLD, blood-oxygen-level dependent
- CBA, cardioballistic artifact
- DMN, default mode network
- ECN, executive control network
- EEG
- EEG, electroencephalography
- FDR, false discovery rate
- FWHM, full width at half maximum
- Functional MRI
- GLM, general linear model
- HRF, hemodynamic response function
- ICA, independent component analysis
- MNI, montreal neurological institute
- OBS, optimal basis set
- PAG, periaqueductal gray
- PCA, principal component analysis
- PCC, posterior cingulate cortex
- PFC, prefrontal cortex
- Pain
- ROI, region of interest
- RSN, resting state networks
- Resting state networks
- SCD, sickle cell disease
- SMA, supplementary motor area
- Sickle cell disease
- fMRI, functional magnetic resonance imaging
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Affiliation(s)
- Michelle Case
- Department of Biomedical Engineering, University of Minnesota, USA
| | - Huishi Zhang
- Department of Biomedical Engineering, University of Minnesota, USA
| | - John Mundahl
- Department of Biomedical Engineering, University of Minnesota, USA
| | - Yvonne Datta
- Department of Medicine, University of Minnesota, USA
| | | | - Kalpna Gupta
- Department of Medicine, University of Minnesota, USA
| | - Bin He
- Department of Biomedical Engineering, University of Minnesota, USA; Institute for Engineering in Medicine, University of Minnesota, USA
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