Exploiting Functionally-Connected Networks

Optode layouts are highly configurable, and other networks and regions can be interrogated for other operational and clinical applications, if they are known to be relevant, and are accessible to fNIRS. For this study, the attentional and resting state networks of the brain have been selected as sensor locations for this work to exploit known anti-correlation of the ATN and DMN activations. The DMN, which is one of many resting state networks of the brain, activates during resting or task-free states (Greicius et al., 2003; Mantini et al., 2007; Raichle et al., 2001). During focused attention and task engagement, relative to during a task-free or rest state, the ATN is expected to activate and the DMN is expected to deactivate. This simple model is depicted in Figure 2. Whether this anti-correlation originates due to competition for resources (Kelly et al., 2009) or is a manifestation of intrinsic interactions in the brain (Fox et al., 2005), it has been shown and quantified with fMRI studies. Also, less deactivation of the DMN has been shown to be associated with worse behavioral performance (such as stimulus reaction time) and attentional lapses (Weissman et al., 2006; Kelly et al., 2008; Kineses et al., 2008). Activity differences have been linked to autism (Weng et al., 2010), attention deficit hyperactivity disorder, and Alzheimer’s disease (Uddin et al, 2009). Importantly, errors due to lapses in attention have been predicted in less than one minute prior to the error by detecting neural activity in networks (Eichele et al., 2008; O'Connell et al., 2009; Weissman et al., 2006).

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Figure 2. Expected network activations depending on task condition

ATN activations (blue) are expected to be high while DMN activations (red) are low during task engagement. With less task engagement, the opposite response is expected. The vector w, introduced in section 2.4, is shown normal to a dashed line separating the two conditions, or classes. A vector instance is illustrated for each class. Each vector instance has two components, or two classifier input feature instances, where component x1w represents the DMN activity in the ‘work’ or more engaged state, and component x1r represents the DMN activity in the ‘rest’ or less-engaged state. In this simple example, note that x1w is less than x1r. Similarly, component x2w represents the ATN activity in the ‘work’ or more engaged state, and component x2r represents the ATN activity in the ‘rest’ or less-engaged state. Note that x2w is greater than x2r.

The functional connectivity of these networks is an important aspect of the proposed monitoring system. The brain is regionally organized, but functional specificity of particular regions is not absolute. Particular regions of the brain are specialized for certain functional tasks, but can be active as well for a variety of different functional tasks and respond to a variety of different stimuli (Gazzaniga, 1989; Kanwisher, 2010). Thus, as introduced in 2.1.2, activity in one region indicated by a single point measurement cannot be assumed to be completely and only due to the functional task of interest. In this way, such measurements can be sensitive to a state or condition, but not specific and thus not predictive. Multiple point measurements allow for the detection of a functional network and its level of activation. Further, some spatially- separate regions of the brain undergo temporally correlated activation in response to a functional task or stimulus. These regions are said to be functionally connected, and this connectivity can

w

[x1w, x2w]

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be quantified by measuring across-region temporal correlation of the hemodynamic activations in response to stimuli. Additionally, functionally-connected resting state networks exhibit activity at low frequencies (below 0.1Hz) during resting conditions (Biswal et al., 1995).

The detection of activity in multiple regions (for example at least one node each in two networks) should provide more confidence that what has been detected actually indicates the functional state that is intended to be measured. Therefore, we proposed that attentional state may be monitored through the measurement of neural hemodynamic activity in cortical layers of functionally-connected attentional and default mode network regions of the brain. If functionally-connected network activations as measured by fNIRS are correlated with behavioral measures indicating performance decrement due to lapses in attention, then sustained attention can be monitored with fNIRS. A first step toward attaining this goal is detecting and predicting task engagement relative to rest, or a lack of task engagement.

Notably no cognitive strategy is required on the part of the participant or user to employ these networks, allowing passive monitoring of the brain without hindering task performance. Further supporting the suitability of these networks for attentional state monitoring, activity is increased in the TPN (and decreased in the TNN) as task demand increases (Fox et al., 2005; McKiernan et al., 2003; Wojciulik and Kanwisher, 1999). This aspect may allow for the reduction of false alarms in non-extreme states, as the extremes of engagement foster hazardous disengagement due to a lack of arousal, or potentially dangerous performance decrement due to task overload (Yerkes and Dodson, 1908).

Additionally, the capability of detecting such network activation might prove useful in future applications of fNIRS that are aimed at discriminating between optimal behavioral performance (where a negative correlation is expected) and internally-guided thought (where co- activation and, hence, a positive correlation is expected) (Christoff et al., 2009; Smallwood et al., 2012).