Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity

Or-Borichev, Ayelet; Gurevitch, Guy; Klovatch, Ilana; Greental, Ayam; Lerner, Yulia; Levy, Dino J.; Hendler, Talma

doi:10.1016/j.neuroimage.2022.119822

Cited by 4 publications

(4 citation statements)

References 88 publications

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“…Results from a series of validation experiments of this method with both healthy participants ( Keynan et al., 2019 , 2016 ) and clinical populations (PTSD; Fruchtman-Steinbok et al., 2021 ; Fibromyalgia; Goldway et al., 2019 ) indicated that individuals are able to learn how to down-regulate the amygdala-EFP, and that such training is associated with marked neurobehavioral changes related to amygdala down-regulation, with significant clinical outcomes. Most recently, an electrical FingerPrint of the inferior Frontal Gyrus ( Or-Borichev et al., 2023 ) with a similar performance magnitude has also been successfully applied in a neurofeedback design. It was demonstrated that successful learning to up-regulate the IFG-EFP was associated with reduced tendency for risk taking behavior.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, this observation of a moderate relationship between VS-BOLD and VS-related-EFP should be taken into account in the development of future applications, where it would be important to validate via fMRI that the target of the electrical Fingerprint is modulated by the neurofeedback task. Such validation step has been successfully done for other EFP models (which have a comparable effect size as the VS-EFP) when they were implemented in a neurofeedback design in both healthy ( Keynan et al., 2019 , 2016 ; Or-Borichev et al., 2023 ) and patient populations ( Fruchtman-Steinbok et al., 2021 ; Goldway et al., 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…This amygdala-BOLD informed model has been validated using simultaneous EEG/fMRI data of the same individuals from different sessions ( Meir-Hasson et al., 2016 ), and from a different group of individuals, revealing it to be a reliable predictor of the BOLD activity in the amygdala and a connected network ( Keynan et al., 2016 ). The algorithmic development of such cross-modal prediction inspired the construction of predictive models of additional regions by others, such as the Inferior Frontal Gyrus ( Or-Borichev et al., 2023 ), the motor cortex ( Rudnev et al., 2021 ), and groups of regions such as the facial expression processing network ( Simões et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation

et al. 2023

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation

et al. 2023

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“…However, the neuroanatomical precision of EEG is questionable, resulting in poor target localization, particularly in deep brain region as the amygdala. To advance beyond the state of the art, we have established an analytic way to benefit from both technologies, resulting in fMRI informed EEG models [42][43][44]. Such a one-class prediction model is based on simultaneous acquisition of fMRI and EEG in an independent dataset, and the prediction of well localized fMRI activity in the amygdala from the EEG signal; termed herby Amygdala Electrical Fingerprint (Amyg-EFP).…”

Section: Introductionmentioning

confidence: 99%

Amygdala Self-Neuromodulation Capacity as a Window for Process-Related Network Recruitment

Gurevitch,

Lubianiker,

Markovits

et al. 2024

Preprint

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Neurofeedback (NF) has emerged as a promising avenue for demonstrating process-related neuroplasticity, enabling self-regulation of brain function. NF targeting the amygdala has drawn attention for therapeutic potential in psychiatry, by potentially harnessing emotion-regulation processes. However, not all individuals respond equally to NF training, possibly due to varying self-regulation abilities. This underscores the importance of understanding the mechanisms behind successful neuromodulation (i.e. capacity). This study aimed to investigate the establishment and neural correlates of neuromodulation capacity by using data from repeated sessions of Amygdala Electrical Finger Print (EFP)-NF and post-training fMRI-NF session.Results from 97 psychiatric patients and healthy participants revealed increased amygdala-EFP neuromodulation capacity over training, associated with post-training amygdala fMRI modulation-capacity and improvements in alexithymia. Individual differences in this capacity were associated with pre-training amygdala reactivity and initial neuromodulation success. Additionally, amygdala down-regulation during fMRI-NF co-modulated with other regions such as the posterior-insula and parahippocampal gyrus. This combined modulation better explained EFP-modulation capacity and improvement in alexithymia than the amygdala modulation alone, suggesting the relevance of this broader network to the gained capacity. These findings support a network-based approach for NF and highlight the need to consider individual differences in brain function and modulation capacity to optimize NF interventions.

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Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review

Fleury,

Figueiredo,

Vourvopoulos

et al. 2023

J. Neural Eng.

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Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain- computer interfaces (BCI). While EEG has high temporal resolution and low spatial resolution, fMRI has high spatial resolution and low temporal resolution. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities in order to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals in order to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges. We will also identify some of the remaining challenges in this field.

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Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity

Cited by 4 publications

References 88 publications

Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation

Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation

Amygdala Self-Neuromodulation Capacity as a Window for Process-Related Network Recruitment

Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review

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