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Fresnoza S, Ischebeck A. Probing Our Built-in Calculator: A Systematic Narrative Review of Noninvasive Brain Stimulation Studies on Arithmetic Operation-Related Brain Areas. eNeuro 2024; 11:ENEURO.0318-23.2024. [PMID: 38580452 PMCID: PMC10999731 DOI: 10.1523/eneuro.0318-23.2024] [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: 08/25/2023] [Revised: 02/06/2024] [Accepted: 02/26/2024] [Indexed: 04/07/2024] Open
Abstract
This systematic review presented a comprehensive survey of studies that applied transcranial magnetic stimulation and transcranial electrical stimulation to parietal and nonparietal areas to examine the neural basis of symbolic arithmetic processing. All findings were compiled with regard to the three assumptions of the triple-code model (TCM) of number processing. Thirty-seven eligible manuscripts were identified for review (33 with healthy participants and 4 with patients). Their results are broadly consistent with the first assumption of the TCM that intraparietal sulcus both hold a magnitude code and engage in operations requiring numerical manipulations such as subtraction. However, largely heterogeneous results conflicted with the second assumption of the TCM that the left angular gyrus subserves arithmetic fact retrieval, such as the retrieval of rote-learned multiplication results. Support is also limited for the third assumption of the TCM, namely, that the posterior superior parietal lobule engages in spatial operations on the mental number line. Furthermore, results from the stimulation of brain areas outside of those postulated by the TCM show that the bilateral supramarginal gyrus is involved in online calculation and retrieval, the left temporal cortex in retrieval, and the bilateral dorsolateral prefrontal cortex and cerebellum in online calculation of cognitively demanding arithmetic problems. The overall results indicate that multiple cortical areas subserve arithmetic skills.
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Affiliation(s)
- Shane Fresnoza
- Department of Psychology, University of Graz, 8010 Graz, Austria
- BioTechMed, 8010 Graz, Austria
| | - Anja Ischebeck
- Department of Psychology, University of Graz, 8010 Graz, Austria
- BioTechMed, 8010 Graz, Austria
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2
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Fassi L, Hochman S, Daskalakis ZJ, Blumberger DM, Cohen Kadosh R. The importance of individual beliefs in assessing treatment efficacy. eLife 2024; 12:RP88889. [PMID: 38547008 PMCID: PMC10977967 DOI: 10.7554/elife.88889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024] Open
Abstract
In recent years, there has been debate about the effectiveness of treatments from different fields, such as neurostimulation, neurofeedback, brain training, and pharmacotherapy. This debate has been fuelled by contradictory and nuanced experimental findings. Notably, the effectiveness of a given treatment is commonly evaluated by comparing the effect of the active treatment versus the placebo on human health and/or behaviour. However, this approach neglects the individual's subjective experience of the type of treatment she or he received in establishing treatment efficacy. Here, we show that individual differences in subjective treatment - the thought of receiving the active or placebo condition during an experiment - can explain variability in outcomes better than the actual treatment. We analysed four independent datasets (N = 387 participants), including clinical patients and healthy adults from different age groups who were exposed to different neurostimulation treatments (transcranial magnetic stimulation: Studies 1 and 2; transcranial direct current stimulation: Studies 3 and 4). Our findings show that the inclusion of subjective treatment can provide a better model fit either alone or in interaction with objective treatment (defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive, and behavioural outcomes. We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants' subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.
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Affiliation(s)
- Luisa Fassi
- MRC Cognition and Brain Sciences Unit, University of CambridgeCambridgeUnited Kingdom
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
- Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
| | - Shachar Hochman
- School of Psychology, University of SurreySurreyUnited Kingdom
| | - Zafiris J Daskalakis
- Department of Psychiatry, University of California, San DiegoSan DiegoUnited States
| | - Daniel M Blumberger
- Temerty Centre for Therapeutic Brain Intervention at the Centre for Addiction and Mental Health and Department of Psychiatry, Temerty Faculty of Medicine, University of TorontoTorontoCanada
| | - Roi Cohen Kadosh
- Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- School of Psychology, University of SurreySurreyUnited Kingdom
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3
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Alipour M, Seok S, Mednick SC, Malerba P. A classification-based generative approach to selective targeting of global slow oscillations during sleep. Front Hum Neurosci 2024; 18:1342975. [PMID: 38415278 PMCID: PMC10896842 DOI: 10.3389/fnhum.2024.1342975] [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: 11/22/2023] [Accepted: 01/30/2024] [Indexed: 02/29/2024] Open
Abstract
Background Given sleep's crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability. Objective/Hypothesis This study targets a specific type of SOs, widespread on the electrode manifold in a short delay ("global SOs"), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile. Methods We introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance. Results Our study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization. Conclusion Our research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.
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Affiliation(s)
- Mahmoud Alipour
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United States
- The Ohio State University School of Medicine, Columbus, OH, United States
| | - SangCheol Seok
- Center for Gene Therapy, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Sara C. Mednick
- Department of Cognitive Sciences, University of California, Irvine, Irvine CA, United States
| | - Paola Malerba
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United States
- The Ohio State University School of Medicine, Columbus, OH, United States
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4
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Mao X, Chang YC, Zanos S, Lajoie G. Personalized inference for neurostimulation with meta-learning: a case study of vagus nerve stimulation. J Neural Eng 2024; 21:016004. [PMID: 38131193 DOI: 10.1088/1741-2552/ad17f4] [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: 07/21/2023] [Accepted: 12/21/2023] [Indexed: 12/23/2023]
Abstract
Objective. Neurostimulation is emerging as treatment for several diseases of the brain and peripheral organs. Due to variability arising from placement of stimulation devices, underlying neuroanatomy and physiological responses to stimulation, it is essential that neurostimulation protocols are personalized to maximize efficacy and safety. Building such personalized protocols would benefit from accumulated information in increasingly large datasets of other individuals' responses.Approach. To address that need, we propose a meta-learning family of algorithms to conduct few-shot optimization of key fitting parameters of physiological and neural responses in new individuals. While our method is agnostic to neurostimulation setting, here we demonstrate its effectiveness on the problem of physiological modeling of fiber recruitment during vagus nerve stimulation (VNS). Using data from acute VNS experiments, the mapping between amplitudes of stimulus-evoked compound action potentials (eCAPs) and physiological responses, such as heart rate and breathing interval modulation, is inferred.Main results. Using additional synthetic data sets to complement experimental results, we demonstrate that our meta-learning framework is capable of directly modeling the physiology-eCAP relationship for individual subjects with much fewer individually queried data points than standard methods.Significance. Our meta-learning framework is general and can be adapted to many input-response neurostimulation mapping problems. Moreover, this method leverages information from growing data sets of past patients, as a treatment is deployed. It can also be combined with several model types, including regression, Gaussian processes with Bayesian optimization, and beyond.
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Affiliation(s)
- Ximeng Mao
- Mila-Quebec Artificial Intelligence Institute, 6666 St-Urbain, Montréal, QC H2S 3H1, Canada
- Department of Computer Science and Operations Research, University of Montréal, 2920 chemin de la Tour, Montréal, QC H3T 1J4, Canada
| | - Yao-Chuan Chang
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States of America
- Medtronic, 710 Medtronic Parkway, Minneapolis, MN 55432, United States of America
| | - Stavros Zanos
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States of America
| | - Guillaume Lajoie
- Mila-Quebec Artificial Intelligence Institute, 6666 St-Urbain, Montréal, QC H2S 3H1, Canada
- Department of Mathematics and Statistics, University of Montréal, 2920 chemin de la Tour, Montréal, QC H3T 1J4, Canada
- Canada CIFAR AI Chair, Toronto, ON M5G 1M1, Canada
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5
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Lee S, Shirinpour S, Alekseichuk I, Perera N, Linn G, Schroeder CE, Falchier AY, Opitz A. Predicting the phase distribution during multi-channel transcranial alternating current stimulation in silico and in vivo. Comput Biol Med 2023; 166:107516. [PMID: 37769460 PMCID: PMC10955626 DOI: 10.1016/j.compbiomed.2023.107516] [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: 06/15/2023] [Revised: 08/22/2023] [Accepted: 09/19/2023] [Indexed: 09/30/2023]
Abstract
BACKGROUND Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation (NIBS) technique to affect neural activity. TACS experiments have been coupled with computational simulations to predict the electromagnetic fields within the brain. However, existing simulations are focused on the magnitude of the field. As the possibility of inducing the phase gradient in the brain using multiple tACS electrodes arises, a simulation framework is necessary to investigate and predict the phase gradient of electric fields during multi-channel tACS. OBJECTIVE Here, we develop such a framework for phasor simulation using phasor algebra and evaluate its accuracy using in vivo recordings in monkeys. METHODS We extract the phase and amplitude of electric fields from intracranial recordings in two monkeys during multi-channel tACS and compare them to those calculated by phasor analysis using finite element models. RESULTS Our findings demonstrate that simulated phases correspond well to measured phases (r = 0.9). Further, we systematically evaluated the impact of accurate electrode placement on modeling and data agreement. Finally, our framework can predict the amplitude distribution in measurements given calibrated tissues' conductivity. CONCLUSIONS Our validated general framework for simulating multi-phase, multi-electrode tACS provides a streamlined tool for principled planning of multi-channel tACS experiments.
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Affiliation(s)
- Sangjun Lee
- Department of Biomedical Engineering, University of Minnesota, MN, USA.
| | - Sina Shirinpour
- Department of Biomedical Engineering, University of Minnesota, MN, USA
| | - Ivan Alekseichuk
- Department of Biomedical Engineering, University of Minnesota, MN, USA
| | - Nipun Perera
- Department of Biomedical Engineering, University of Minnesota, MN, USA
| | - Gary Linn
- Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry NYU Grossman School of Medicine, New York City, NY, USA
| | - Charles E Schroeder
- Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Departments of Neurological Surgery and Psychiatry, Columbia University College of Physicians and Surgeons, NY, USA
| | - Arnaud Y Falchier
- Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry NYU Grossman School of Medicine, New York City, NY, USA
| | - Alexander Opitz
- Department of Biomedical Engineering, University of Minnesota, MN, USA.
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6
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Zacharopoulos G, Sella F, Emir U, Cohen Kadosh R. Dissecting the chain of information processing and its interplay with neurochemicals and fluid intelligence across development. eLife 2023; 12:e84086. [PMID: 37772958 PMCID: PMC10541179 DOI: 10.7554/elife.84086] [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: 10/10/2022] [Accepted: 08/23/2023] [Indexed: 09/30/2023] Open
Abstract
Previous research has highlighted the role of glutamate and gamma-aminobutyric acid (GABA) in perceptual, cognitive, and motor tasks. However, the exact involvement of these neurochemical mechanisms in the chain of information processing, and across human development, is unclear. In a cross-sectional longitudinal design, we used a computational approach to dissociate cognitive, decision, and visuomotor processing in 293 individuals spanning early childhood to adulthood. We found that glutamate and GABA within the intraparietal sulcus (IPS) explained unique variance in visuomotor processing, with higher glutamate predicting poorer visuomotor processing in younger participants but better visuomotor processing in mature participants, while GABA showed the opposite pattern. These findings, which were neurochemically, neuroanatomically and functionally specific, were replicated ~21 mo later and were generalized in two further different behavioral tasks. Using resting functional MRI, we revealed that the relationship between IPS neurochemicals and visuomotor processing is mediated by functional connectivity in the visuomotor network. We then extended our findings to high-level cognitive behavior by predicting fluid intelligence performance. We present evidence that fluid intelligence performance is explained by IPS GABA and glutamate and is mediated by visuomotor processing. However, this evidence was obtained using an uncorrected alpha and needs to be replicated in future studies. These results provide an integrative biological and psychological mechanistic explanation that links cognitive processes and neurotransmitters across human development and establishes their potential involvement in intelligent behavior.
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Affiliation(s)
- George Zacharopoulos
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- School of Psychology, Swansea UniversitySwanseaUnited Kingdom
| | - Francesco Sella
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- Centre for Mathematical Cognition, Loughborough UniversityLoughboroughUnited Kingdom
| | - Uzay Emir
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- School of Health Sciences, College of Health and Human Sciences, Purdue UniversityWest LafayetteUnited States
| | - Roi Cohen Kadosh
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- School of Psychology, University of SurreyGuildfordUnited Kingdom
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7
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Zheng Y, Tang S, Zheng H, Wang X, Liu L, Yang Y, Zhen Y, Zheng Z. Noise improves the association between effects of local stimulation and structural degree of brain networks. PLoS Comput Biol 2023; 19:e1010866. [PMID: 37167331 PMCID: PMC10205011 DOI: 10.1371/journal.pcbi.1010866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/23/2023] [Accepted: 04/20/2023] [Indexed: 05/13/2023] Open
Abstract
Stimulation to local areas remarkably affects brain activity patterns, which can be exploited to investigate neural bases of cognitive function and modify pathological brain statuses. There has been growing interest in exploring the fundamental action mechanisms of local stimulation. Nevertheless, how noise amplitude, an essential element in neural dynamics, influences stimulation-induced brain states remains unknown. Here, we systematically examine the effects of local stimulation by using a large-scale biophysical model under different combinations of noise amplitudes and stimulation sites. We demonstrate that noise amplitude nonlinearly and heterogeneously tunes the stimulation effects from both regional and network perspectives. Furthermore, by incorporating the role of the anatomical network, we show that the peak frequencies of unstimulated areas at different stimulation sites averaged across noise amplitudes are highly positively related to structural connectivity. Crucially, the association between the overall changes in functional connectivity as well as the alterations in the constraints imposed by structural connectivity with the structural degree of stimulation sites is nonmonotonically influenced by the noise amplitude, with the association increasing in specific noise amplitude ranges. Moreover, the impacts of local stimulation of cognitive systems depend on the complex interplay between the noise amplitude and average structural degree. Overall, this work provides theoretical insights into how noise amplitude and network structure jointly modulate brain dynamics during stimulation and introduces possibilities for better predicting and controlling stimulation outcomes.
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Affiliation(s)
- Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Shaoting Tang
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Hongwei Zheng
- Beijing Academy of Blockchain and Edge Computing (BABEC), Beijing, China
| | - Xin Wang
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
| | - Longzhao Liu
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
| | - Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Zhiming Zheng
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
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8
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Ayanampudi V, Kumar V, Krishnan A, Walker MP, Ivry RB, Knight RT, Gurumoorthy R. Personalized transcranial alternating current stimulation improves sleep quality: Initial findings. Front Hum Neurosci 2023; 16:1066453. [PMID: 36704097 PMCID: PMC9872012 DOI: 10.3389/fnhum.2022.1066453] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/16/2022] [Indexed: 01/11/2023] Open
Abstract
Insufficient sleep is a major health issue. Inadequate sleep is associated with an array of poor health outcomes, including cardiovascular disease, diabetes, obesity, certain forms of cancer, Alzheimer's disease, depression, anxiety, and suicidality. Given concerns with typical sedative hypnotic drugs for treating sleep difficulties, there is a compelling need for alternative interventions. Here, we report results of a non-invasive electrical brain stimulation approach to optimizing sleep involving transcranial alternating current stimulation (tACS). A total of 25 participants (mean age: 46.3, S.D. ± 12.4, 15 females) were recruited for a null-stimulation controlled (Control condition), within subjects, randomized crossed design, that included two variants of an active condition involving 15 min pre-sleep tACS stimulation. To evaluate the impact on sleep quality, the two active tACS stimulation conditions were designed to modulate sleep-dependent neural activity in the theta/alpha frequency bands, with both stimulation types applied to all subjects in separate sessions. The first tACS condition used a fixed stimulation pattern across all participants, a pattern composed of stimulation at 5 and 10 Hz. The second tACS condition used a personalized stimulation approach with the stimulation frequencies determined by each individual's peak EEG frequencies in the 4-6 Hz and 9-11 Hz bands. Personalized tACS stimulation increased sleep quantity (duration) by 22 min compared to a Control condition (p = 0.04), and 19 min compared to Fixed tACS stimulation (p = 0.03). Fixed stimulation did not significantly increase sleep duration compared to Control (mean: 3 min; p = 0.75). For sleep onset, the Personalized tACS stimulation resulted in reducing the onset by 28% compared to the Fixed tACS stimulation (6 min faster, p = 0.02). For a Poor Sleep sub-group (n = 13) categorized with Clinical Insomnia and a high insomnia severity, Personalized tACS stimulation improved sleep duration by 33 min compared to Fixed stimulation (p = 0.02), and 30 min compared to Control condition (p < 0.1). Together, these results suggest that Personalized stimulation improves sleep quantity and time taken to fall asleep relative to Control and Fixed stimulation providing motivation for larger-scale trials for Personalized tACS as a sleep therapeutic, including for those with insomnia.
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Affiliation(s)
| | - V. Kumar
- StimScience Inc., Berkeley, CA, United States
| | - A. Krishnan
- StimScience Inc., Berkeley, CA, United States
| | - M. P. Walker
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - R. B. Ivry
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - R. T. Knight
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - R. Gurumoorthy
- StimScience Inc., Berkeley, CA, United States,*Correspondence: R. Gurumoorthy,
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9
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Ziegler DA, Anguera JA, Gallen CL, Hsu WY, Wais PE, Gazzaley A. Leveraging technology to personalize cognitive enhancement methods in aging. NATURE AGING 2022; 2:475-483. [PMID: 35873177 PMCID: PMC9302894 DOI: 10.1038/s43587-022-00237-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
As population aging advances at an increasing rate, efforts to help people maintain or improve cognitive function late in life are critical. Although some studies have shown promise, the question of whether cognitive training is an effective tool for improving general cognitive ability remains incompletely explored, and study results to date have been inconsistent. Most approaches to cognitive enhancement in older adults have taken a 'one size fits all' tack, as opposed to tailoring interventions to the specific needs of individuals. In this Perspective, we argue that modern technology has the potential to enable large-scale trials of public health interventions to enhance cognition in older adults in a personalized manner. Technology-based cognitive interventions that rely on closed-loop systems can be tailored to individuals in real time and have the potential for global testing, extending their reach to large and diverse populations of older adults. We propose that the future of cognitive enhancement in older adults will rely on harnessing new technologies in scientifically informed ways.
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Affiliation(s)
- David A. Ziegler
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Neuroscape, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Correspondence should be addressed to David A. Ziegler or Adam Gazzaley. ;
| | - Joaquin A. Anguera
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Neuroscape, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
| | - Courtney L. Gallen
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Neuroscape, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Wan-Yu Hsu
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Peter E. Wais
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Neuroscape, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Adam Gazzaley
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Neuroscape, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- Department of Physiology, University of California San Francisco, San Francisco, CA, USA
- Correspondence should be addressed to David A. Ziegler or Adam Gazzaley. ;
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10
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Antal A, Luber B, Brem AK, Bikson M, Brunoni AR, Cohen Kadosh R, Dubljević V, Fecteau S, Ferreri F, Flöel A, Hallett M, Hamilton RH, Herrmann CS, Lavidor M, Loo C, Lustenberger C, Machado S, Miniussi C, Moliadze V, Nitsche MA, Rossi S, Rossini PM, Santarnecchi E, Seeck M, Thut G, Turi Z, Ugawa Y, Venkatasubramanian G, Wenderoth N, Wexler A, Ziemann U, Paulus W. Non-invasive brain stimulation and neuroenhancement. Clin Neurophysiol Pract 2022; 7:146-165. [PMID: 35734582 PMCID: PMC9207555 DOI: 10.1016/j.cnp.2022.05.002] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/19/2022] [Accepted: 05/18/2022] [Indexed: 12/15/2022] Open
Abstract
The available data frame with a wide parameter space of tES does not allow an overarching protocol recommendation. Established engineering risk-management procedures with regard to manufacturing should be followed. Consensus among experts is that tES for neuroenhancement is safe as long as tested protocols are followed.
Attempts to enhance human memory and learning ability have a long tradition in science. This topic has recently gained substantial attention because of the increasing percentage of older individuals worldwide and the predicted rise of age-associated cognitive decline in brain functions. Transcranial brain stimulation methods, such as transcranial magnetic (TMS) and transcranial electric (tES) stimulation, have been extensively used in an effort to improve cognitive functions in humans. Here we summarize the available data on low-intensity tES for this purpose, in comparison to repetitive TMS and some pharmacological agents, such as caffeine and nicotine. There is no single area in the brain stimulation field in which only positive outcomes have been reported. For self-directed tES devices, how to restrict variability with regard to efficacy is an essential aspect of device design and function. As with any technique, reproducible outcomes depend on the equipment and how well this is matched to the experience and skill of the operator. For self-administered non-invasive brain stimulation, this requires device designs that rigorously incorporate human operator factors. The wide parameter space of non-invasive brain stimulation, including dose (e.g., duration, intensity (current density), number of repetitions), inclusion/exclusion (e.g., subject’s age), and homeostatic effects, administration of tasks before and during stimulation, and, most importantly, placebo or nocebo effects, have to be taken into account. The outcomes of stimulation are expected to depend on these parameters and should be strictly controlled. The consensus among experts is that low-intensity tES is safe as long as tested and accepted protocols (including, for example, dose, inclusion/exclusion) are followed and devices are used which follow established engineering risk-management procedures. Devices and protocols that allow stimulation outside these parameters cannot claim to be “safe” where they are applying stimulation beyond that examined in published studies that also investigated potential side effects. Brain stimulation devices marketed for consumer use are distinct from medical devices because they do not make medical claims and are therefore not necessarily subject to the same level of regulation as medical devices (i.e., by government agencies tasked with regulating medical devices). Manufacturers must follow ethical and best practices in marketing tES stimulators, including not misleading users by referencing effects from human trials using devices and protocols not similar to theirs.
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Affiliation(s)
- Andrea Antal
- Department of Neurology, University Medical Center, Göttingen, Germany
- Corresponding author at: Department of Neurology, University Medical Center, Göttingen, Robert Koch Str. 40, 37075 Göttingen, Germany.
| | - Bruce Luber
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Anna-Katharine Brem
- University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Marom Bikson
- Biomedical Engineering at the City College of New York (CCNY) of the City University of New York (CUNY), NY, USA
| | - Andre R. Brunoni
- Departamento de Clínica Médica e de Psiquiatria, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Service of Interdisciplinary Neuromodulation (SIN), Laboratory of Neurosciences (LIM-27), Institute of Psychiatry, Hospital das Clínicas da Faculdade de Medicina da USP, São Paulo, Brazil
| | - Roi Cohen Kadosh
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Veljko Dubljević
- Science, Technology and Society Program, College of Humanities and Social Sciences, North Carolina State University, Raleigh, NC, USA
| | - Shirley Fecteau
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, CERVO Brain Research Centre, Centre intégré universitaire en santé et services sociaux de la Capitale-Nationale, Quebec City, Quebec, Canada
| | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Agnes Flöel
- Department of Neurology, Universitätsmedizin Greifswald, 17475 Greifswald, Germany
- German Centre for Neurodegenerative Diseases (DZNE) Standort Greifswald, 17475 Greifswald, Germany
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Roy H. Hamilton
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christoph S. Herrmann
- Experimental Psychology Lab, Department of Psychology, Carl von Ossietzky Universität, Oldenburg, Germany
| | - Michal Lavidor
- Department of Psychology and the Gonda Brain Research Center, Bar Ilan University, Israel
| | - Collen Loo
- School of Psychiatry and Black Dog Institute, University of New South Wales; The George Institute; Sydney, Australia
| | - Caroline Lustenberger
- Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
| | - Sergio Machado
- Department of Sports Methods and Techniques, Federal University of Santa Maria, Santa Maria, Brazil
- Laboratory of Physical Activity Neuroscience, Neurodiversity Institute, Queimados-RJ, Brazil
| | - Carlo Miniussi
- Center for Mind/Brain Sciences – CIMeC and Centre for Medical Sciences - CISMed, University of Trento, Rovereto, Italy
| | - Vera Moliadze
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | - Michael A Nitsche
- Department Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors at TU, Dortmund, Germany
- Dept. Neurology, University Medical Hospital Bergmannsheil, Bochum, Germany
| | - Simone Rossi
- Siena Brain Investigation and Neuromodulation Lab (Si-BIN Lab), Unit of Neurology and Clinical Neurophysiology, Department of Medicine, Surgery and Neuroscience, University of Siena, Italy
| | - Paolo M. Rossini
- Department of Neuroscience and Neurorehabilitation, Brain Connectivity Lab, IRCCS-San Raffaele-Pisana, Rome, Italy
| | - Emiliano Santarnecchi
- Precision Neuroscience and Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Margitta Seeck
- Department of Clinical Neurosciences, Hôpitaux Universitaires de Genève, Switzerland
| | - Gregor Thut
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, EEG & Epolepsy Unit, University of Glasgow, United Kingdom
| | - Zsolt Turi
- Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Yoshikazu Ugawa
- Department of Human Neurophysiology, Fukushima Medical University, Fukushima, Japan
| | | | - Nicole Wenderoth
- Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise (CREATE), Singapore
| | - Anna Wexler
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ulf Ziemann
- Department of Neurology and Stroke, University of Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Walter Paulus
- Department of of Neurology, Ludwig Maximilians University Munich, Germany
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11
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Predicting Math Ability Using Working Memory, Number Sense, and Neurophysiology in Children and Adults. Brain Sci 2022; 12:brainsci12050550. [PMID: 35624937 PMCID: PMC9139259 DOI: 10.3390/brainsci12050550] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 11/16/2022] Open
Abstract
Previous work has shown relations between domain-general processes, domain-specific processes, and mathematical ability. However, the underlying neurophysiological effects of mathematical ability are less clear. Recent evidence highlighted the potential role of beta oscillations in mathematical ability. Here we investigate whether domain-general (working memory) and domain-specific (number sense) processes mediate the relation between resting-state beta oscillations and mathematical ability, and how this may differ as a function of development (children vs. adults). We compared a traditional analysis method normally used in EEG studies with a more recently developed parameterization method that separates periodic from aperiodic activity. Regardless of methods chosen, we found no support for mediation of working memory and number sense, neither for children nor for adults. However, we found subtle differences between the methods. Additionally, we showed that the traditional EEG analysis method conflates periodic activity with aperiodic activity; in addition, the latter is strongly related to mathematical ability and this relation differs between children and adults. At the cognitive level, our findings do not support previous suggestions of a mediation of working memory and number sense. At the neurophysiological level our findings suggest that aperiodic, rather than periodic, activity is linked to mathematical ability as a function of development.
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12
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Personalized Frequency Modulated Transcranial Electrical Stimulation for Associative Memory Enhancement. Brain Sci 2022; 12:brainsci12040472. [PMID: 35448003 PMCID: PMC9025454 DOI: 10.3390/brainsci12040472] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/28/2022] [Accepted: 03/28/2022] [Indexed: 12/25/2022] Open
Abstract
Associative memory (AM) is the ability to remember the relationship between previously unrelated items. AM is significantly affected by normal aging and neurodegenerative conditions, thus there is a growing interest in applying non-invasive brain stimulation (NIBS) techniques for AM enhancement. A growing body of studies identifies posterior parietal cortex (PPC) as the most promising cortical target for both transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES) to modulate a cortico-hippocampal network that underlines AM. In that sense, theta frequency oscillatory tES protocols, targeted towards the hallmark oscillatory activity within the cortico-hippocampal network, are increasingly coming to prominence. To increase precision and effectiveness, the need for EEG guided individualization of the tES protocols is proposed. Here, we present the study protocol in which two types of personalized oscillatory tES–transcranial alternating current stimulation (tACS) and oscillatory transcranial direct current stimulation (otDCS), both frequency-modulated to the individual theta-band frequency (ITF), are compared to the non-oscillatory transcranial direct current stimulation (tDCS) and to the sham stimulation. The study has cross-over design with four tES conditions (tACS, otDCS, tDCS, sham), and the comprehensive set of neurophysiological (resting state EEG and AM-evoked EEG) and behavioral outcomes, including AM tasks (short-term associative memory, face–word, face–object, object-location), as well as measures of other cognitive functions (cognitive control, verbal fluency, and working memory).
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13
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Mathematical Model Insights into EEG Origin under Transcranial Direct Current Stimulation (tDCS) in the Context of Psychosis. J Clin Med 2022; 11:jcm11071845. [PMID: 35407453 PMCID: PMC8999473 DOI: 10.3390/jcm11071845] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/12/2022] [Accepted: 03/22/2022] [Indexed: 02/04/2023] Open
Abstract
Schizophrenia is a psychotic disease that develops progressively over years with a transition from prodromal to psychotic state associated with a disruption in brain activity. Transcranial Direct Current Stimulation (tDCS), known to alleviate pharmaco-resistant symptoms in patients suffering from schizophrenia, promises to prevent such a psychotic transition. To understand better how tDCS affects brain activity, we propose a neural cortico-thalamo-cortical (CTC) circuit model involving the Ascending Reticular Arousal System (ARAS) that permits to describe major impact features of tDCS, such as excitability for short-duration stimulation and electroencephalography (EEG) power modulation for long-duration stimulation. To this end, the mathematical model relates stimulus duration and Long-Term Plasticity (LTP) effect, in addition to describing the temporal LTP decay after stimulus offset. This new relation promises to optimize future stimulation protocols. Moreover, we reproduce successfully EEG-power modulation under tDCS in a ketamine-induced psychosis model and confirm the N-methyl-d-aspartate (NMDA) receptor hypofunction hypothesis in the etiopathophysiology of schizophrenia. The model description points to an important role of the ARAS and the δ-rhythm synchronicity in CTC circuit in early-stage psychosis.
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