Eti Ben-Simon

Towards a Neuroscience of Mind-Wandering

Mind-wandering (MW) is among the most robust and permanent expressions of human conscious awareness, classically regarded by philosophers, clinicians, and scientists as a core element of an intact sense of self. Nevertheless, the scientific exploration of MW poses unique challenges; MW is by nature a spontaneous, off task, internal mental process which is often unaware and usually difficult to control, document or replicate. Consequently, there is a lack of accepted modus operandi for exploring MW in a laboratory setup, leading to a relatively small amount of studies regarding the neural basis of MW. In order to facilitate scientific examination of MW the current review categorizes recent literature into five suggested strategies. Each strategy represents a different methodology of MW research within functional neuroimaging paradigms. Particular attention is paid to resting-state brain activity and to the “default-mode” network. Since the default network is known to exert high activity levels during off-task conditions, it stands out as a compelling candidate for a neuro-biological account of mind-wandering, in itself a rest-based phenomenon. By summarizing the results within and across strategies we suggest further insights into the neural basis and adaptive value of MW, a truly intriguing and unique human experience.

Never Resting Brain: Simultaneous Representation of Two Alpha Related Processes in Humans

Brain activity is continuously modulated, even at “rest”. The alpha rhythm (8–12 Hz) has been known as the hallmark of the brains idle-state. However, it is still debated if the alpha rhythm reflects synchronization in a distributed network or focal generator and whether it occurs spontaneously or is driven by a stimulus. This EEG/fMRI study aimed to explore the source of alpha modulations and their distribution in the resting brain. By serendipity, while computing the individually defined power modulations of the alpha-band, two simultaneously occurring components of these modulations were found. An ‘induced alpha’ that was correlated with the paradigm (eyes open/ eyes closed), and a ‘spontaneous alpha’ that was on-going and unrelated to the paradigm. These alpha components when used as regressors for BOLD activation revealed two segregated activation maps: the ‘induced map’ included left lateral temporal cortical regions and the hippocampus; the ‘spontaneous map’ included prefrontal cortical regions and the thalamus. Our combined fMRI/EEG approach allowed to computationally untangle two parallel patterns of alpha modulations and underpin their anatomical basis in the human brain. These findings suggest that the human alpha rhythm represents at least two simultaneously occurring processes which characterize the ‘resting brain’; one is related to expected change in sensory information, while the other is endogenous and independent of stimulus change.

The dark side of the alpha rhythm: fMRI evidence for induced alpha modulation during complete darkness

The unique role of the EEG alpha rhythm in different states of cortical activity is still debated. The main theories regarding alpha function posit either sensory processing or attention allocation as the main processes governing its modulation. Closing and opening eyes, a well‐known manipulation of the alpha rhythm, could be regarded as attention allocation from inward to outward focus though during light is also accompanied by visual change. To disentangle the effects of attention allocation and sensory visual input on alpha modulation, 14 healthy subjects were asked to open and close their eyes during conditions of light and of complete darkness while simultaneous recordings of EEG and fMRI were acquired. Thus, during complete darkness the eyes‐open condition is not related to visual input but only to attention allocation, allowing direct examination of its role in alpha modulation. A data‐driven ridge regression classifier was applied to the EEG data in order to ascertain the contribution of the alpha rhythm to eyes‐open/eyes‐closed inference in both lighting conditions. Classifier results revealed significant alpha contribution during both light and dark conditions, suggesting that alpha rhythm modulation is closely linked to the change in the direction of attention regardless of the presence of visual sensory input. Furthermore, fMRI activation maps derived from an alpha modulation time‐course during the complete darkness condition exhibited a right frontal cortical network associated with attention allocation. These findings support the importance of top‐down processes such as attention allocation to alpha rhythm modulation, possibly as a prerequisite to its known bottom‐up processing of sensory input.

Spatio-temporal indications of sub-cortical involvement in leftward bias of spatial attention

A leftward bias is well known in humans and animals, and commonly related to the right hemisphere dominance for spatial attention. Our previous fMRI study suggested that this bias is mediated by faster conduction from the right to left parietal cortices, than the reverse (Siman-Tov et al., 2007). However, the limited temporal resolution of fMRI and evidence on the critical involvement of sub-cortical regions in orienting of spatial attention suggested further investigation of the leftward bias using multi-scale measurement. In this simultaneous EEG-fMRI study, healthy participants were presented with face pictures in either the right or left visual fields while performing a central fixation task. Temporo-occipital event related potentials, time-locked to the stimulus onset, showed an association between faster conduction from the right to the left hemisphere and higher fMRI activation in the left pulvinar nucleus following left visual field stimulation. This combined-modal finding provides original evidence of the involvement of sub-cortical central attention-related regions in the leftward bias. This assertion was further strengthened by a DCM analysis designated at cortical (i.e., inferior parietal sulcus; IPS) and sub-cortical (pulvinar nucleus) attention-related nodes that revealed: 1. Stronger inter-hemispheric connections from the right to left than vice versa, already at the pulvinar level. 2. Stronger connections within the right than the left hemisphere, from the pulvinar to the IPS. This multi-level neural superiority can guide future efforts in alleviating attention deficits by focusing on improving network connectivity.

The dark side of the alpha rhythm: FMRI evidence for alpha-related attention allocation during complete darkness

The unique role of the EEG alpha rhythm in different states of cortical activity is still debated. The main theories regarding alpha function posit either sensory processing or attention allocation as the main processes governing its modulation. Closing and opening eyes, a well‐known manipulation of the alpha rhythm, could be regarded as attention allocation from inward to outward focus though during light is also accompanied by visual change. To disentangle the effects of attention allocation and sensory visual input on alpha modulation, 14 healthy subjects were asked to open and close their eyes during conditions of light and of complete darkness while simultaneous recordings of EEG and fMRI were acquired. Thus, during complete darkness the eyes‐open condition is not related to visual input but only to attention allocation, allowing direct examination of its role in alpha modulation. A data‐driven ridge regression classifier was applied to the EEG data in order to ascertain the contribution of the alpha rhythm to eyes‐open/eyes‐closed inference in both lighting conditions. Classifier results revealed significant alpha contribution during both light and dark conditions, suggesting that alpha rhythm modulation is closely linked to the change in the direction of attention regardless of the presence of visual sensory input. Furthermore, fMRI activation maps derived from an alpha modulation time‐course during the complete darkness condition exhibited a right frontal cortical network associated with attention allocation. These findings support the importance of top‐down processes such as attention allocation to alpha rhythm modulation, possibly as a prerequisite to its known bottom‐up processing of sensory input.

Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes

Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (DEPNA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the DEPNA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the DEPNA correctly captures the network’s hierarchy of influence. Applying DEPNA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that DEPNA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions.

Human mesostriatal response tracks motivational tendencies under naturalistic goal conflict

Goal conflict situations, involving the simultaneous presence of reward and punishment, occur commonly in real life, and reflect well-known individual differences in the behavioral tendency to approach or avoid. However, despite accumulating neural depiction of motivational processing, the investigation of naturalistic approach behavior and its interplay with individual tendencies is remarkably lacking. We developed a novel ecological interactive scenario which triggers motivational behavior under high or low goal conflict conditions. Fifty-five healthy subjects played the game during a functional magnetic resonance imaging scan. A machine-learning approach was applied to classify approach/avoidance behaviors during the game. To achieve an independent measure of individual tendencies, an integrative profile was composed from three established theoretical models. Results demonstrated that approach under high relative to low conflict involved increased activity in the ventral tegmental area (VTA), peri-aquaductal gray, ventral striatum (VS) and precuneus. Notably, only VS and VTA activations during high conflict discriminated between approach/avoidance personality profiles, suggesting that the relationship between individual personality and naturalistic motivational tendencies is uniquely associated with the mesostriatal pathway. VTA-VS further demonstrated stronger coupling during high vs low conflict. These findings are the first to unravel the multilevel relationship among personality profile, approach tendencies in naturalistic set-up and their underlying neural manifestation, thus enabling new avenues for investigating approach-related psychopathologies.

Robust modeling based on optimized EEG bands for functional brain state inference

The need to infer brain states in a data driven approach is crucial for BCI applications as well as for neuroscience research. In this work we present a novel classification framework based on Regularized Linear Regression classifier constructed from time-frequency decomposition of an EEG (electro-encephalography) signal. The regression is then used to derive a model of frequency distributions that identifies brain states. The process of classifier construction, preprocessing and selection of optimal regularization parameter by means of cross-validation is presented and discussed. The framework and the feature selection technique are demonstrated on EEG data recorded from 10 healthy subjects while requested to open and close their eyes every 30 s. This paradigm is well known in inducing Alpha power modulations that differ from low power (during eyes opened) to high (during eyes closed). The classifier was trained to infer eyes opened or eyes closed states and achieved higher than 90% classification accuracy. Furthermore, our findings reveal interesting patterns of relations between experimental conditions, EEG frequencies, regularization parameters and classifier choice. This viable tool enables identification of the most contributing frequency bands to any given brain state and their optimal combination in inferring this state. These features allow for much greater detail than the standard Fourier Transform power analysis, making it an essential method for both BCI proposes and neuroimaging research.

Information flow and coherence of EEG during awake, meditation and drowsiness.

A comparison of coupling (information flow) and coherence (connectedness) of the brain regions between human awake, meditation and drowsiness states was carried out in this study. The Directed Transfer Function (DTF) method was used to estimate the coupling or brains flow of information between different regions during each condition. Welch and Minimum Variance Distortionless Response (MVDR) methods were utilised to estimate the coherence between brain areas. Analysis was conducted using the EEG data of 30 subjects (10 awake, 10 drowsiness and 10 meditating) with 6 EEG electrodes. The EEG data was recorded for each subject during 5 minutes baseline and 15 minutes of three specific conditions (awake, meditation or drowsiness). Statistical analysis was carried out which consisted of the Kruskal-Wallis (KW) non-parametric analysis of variance followed by post-hoc tests with Bonferroni alpha-correction. The results of this study revealed that a change in external awareness led to substantial differences in the spectral profile of the brains information flow as well as its connectedness.