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

The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed "Electrical FingerPrint of rIFG"; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe.

One-Class FMRI-Inspired EEG Model for Self-Regulation Training.

Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations.

Task-specific Aspects of Goal-directed Word Generation Identified via Simultaneous EEG-fMRI.

Generating words according to a given rule relies on retrieval-related search and postretrieval control processes. Using fMRI, we recently characterized neural patterns of word generation in response to episodic, semantic, and phonemic cues by comparing free recall of wordlists, category fluency, and letter fluency [Shapira-Lichter, I., Oren, N., Jacob, Y., Gruberger, M., & Hendler, T. Portraying the unique contribution of the default mode network to internally driven mnemonic processes. Proceedings of the National Academy of Sciences, U.S.A., 110, 4950-4955, 2013]. Distinct selectivity for each condition was evident, representing discrete aspects of word generation-related memory retrieval. For example, the precuneus, implicated in processing spatiotemporal information, emerged as a key contributor to the episodic condition, which uniquely requires this information. Gamma band is known to play a central role in memory, and increased gamma power has been observed before word generation. Yet, gamma modulation in response to task demands has not been investigated. To capture the task-specific modulation of gamma power, we analyzed the EEG data recorded simultaneously with the aforementioned fMRI, focusing on the activity locked to and immediately preceding word articulation. Transient increases in gamma power were identified in a parietal electrode immediately before episodic and semantic word generation, however, within a different time frame relative to articulation. Gamma increases were followed by an alpha-theta decrease in the episodic condition, a gamma decrease in the semantic condition. This pattern indicates a task-specific modulation of the gamma signal corresponding to the specific demands of each word generation task. The gamma power and fMRI signal from the precuneus were correlated during the episodic condition, implying the existence of a common cognitive construct uniquely required for this task, possibly the reactivation or processing of spatiotemporal information.

Limbic Activity Modulation Guided by Functional Magnetic Resonance Imaging-Inspired Electroencephalography Improves Implicit Emotion Regulation.

The amygdala has a pivotal role in processing traumatic stress; hence, gaining control over its activity could facilitate adaptive mechanism and recovery. To date, amygdala volitional regulation could be obtained only via real-time functional magnetic resonance imaging (fMRI), a highly inaccessible procedure. The current article presents high-impact neurobehavioral implications of a novel imaging approach that enables bedside monitoring of amygdala activity using fMRI-inspired electroencephalography (EEG), hereafter termed amygdala-electrical fingerprint (amyg-EFP). Simultaneous EEG/fMRI indicated that the amyg-EFP reliably predicts amygdala-blood oxygen level-dependent activity. Implementing the amyg-EFP in neurofeedback demonstrated that learned downregulation of the amyg-EFP facilitated volitional downregulation of amygdala-blood oxygen level-dependent activity via real-time fMRI and manifested as reduced amygdala reactivity to visual stimuli. Behavioral evidence further emphasized the therapeutic potential of this approach by showing improved implicit emotion regulation following amyg-EFP neurofeedback. Additional EFP models denoting different brain regions could provide a library of localized activity for low-cost and highly accessible brain-based diagnosis and treatment.

Cognitive state following mild stroke: A matter of hippocampal mean diffusivity.

The hippocampus is known to play a vital role in learning and memory and was demonstrated as an early imaging marker for Alzheimer's disease (AD). However, its role as a predictor for mild cognitive impairment and dementia following stroke is unclear. The main purpose of this study was to examine the associations between hippocampal volume, mean diffusivity (MD) and connectivity and cognitive state following stroke. Eighty three consecutive first ever mild to moderate stroke or transient ischemic attack (TIA) survivors from our ongoing prospective TABASCO (Tel Aviv Brain Acute Stroke Cohort) study underwent magnetic resonance imaging scans within 7 days of stroke onset. Hippocampal volume was measured from T1 weighted images, hippocampal mean diffusivity was calculated from diffusion tensor imaging and connectivity was calculated from resting state fMRI. Global cognitive assessments were evaluated during hospitalization and 6 and 12 months later using a computerized neuropsychological battery. Multiple linear regression analysis was used to test which of the hippocampi measurements best predict cognitive state. All three imaging parameters were significantly correlated to each other (|r's| >0.3, P's < 0.005), and with cognitive state 6 and 12 months after the event. Multiple regression analyses demonstrated the predictive role of hippocampal mean diffusivity (ß = -0.382, P = 0.026) on cognitive state, above and beyond that of volume and connectivity of this structure. To our knowledge, the combination of hippocampal volume, mean diffusivity and connectivity in first ever post stroke or TIA patients has not yet been considered in relation to cognitive state. The results demonstrate the predictive role of hippocampal mean diffusivity, suggesting that these changes may precede and contribute to volumetric and connectivity changes in the hippocampi, potentially serving as a marker for early identification of patients at risk of developing cognitive impairment or dementia.