A Review of the Networks Trained by Neurofeedback

Neurofeedback has evolved from training activity at single discrete scalp sites corresponding to discrete Brodmann areas to modifying network communication. Quantitative EEG (qEEG) normative databases can reveal the key parts of a network that need training and the required direction.

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With neurofeedback, we want to know where to place active electrodes and what brain activity to train. It's normal to think that the sensor should go over the part of the brain corresponding to a Brodmann area that needs to be up-trained or down-trained. That may work for some conditions that respond to single-channel training. But, one site is connected to another site, and so on, in networks. Networks, which can be functional or structural, mediate connectivity. In functional networks, there is correlated activity between regions over time. Structural networks consist of axonal projections and pathways. Although functional and structural networks can overlap, functional connectivity does not automatically imply an underlying anatomical connection. Fornito et al. (2016) explained the importance of time scale in their convergence: ". . . as functional connectivity is averaged over longer time periods, it may converge onto structural connectivity, although it is important to remember that structural and functional connectivity are different measures and may thus yield connectomes with different values of some topological parameters (Zalesky et al., 2012b)" (p. 25). Interventions as different as immobilizing a limb and neurofeedback can change functional connectivity. Newbold and Dosenbach (2021) put a cast on participants' (non-broken) dominant hand for a few weeks and performed multiple fMRI scans to see how this impacted the functional connectivity of their motor cortices. After just a day or two, functional connectivity was nearly non-existent between the left and right motor cortex, which is a faster timescale than many people had predicted. Recovery was also quite rapid in 2 of the 3 people. The premise of connectivity training is that neurofeedback (EEG and functional MRI) can increase or decrease functional connectivity to improve performance.

Commonsense About Functional Brain Areas

Peterson and Fiez (1993) observed: "A functional area of the brain is not a task area; there is no 'tennis forehand area' to be discovered. Likewise, no area of the brain is devoted to a very complex function; 'attention' or 'language' is not localized in a particular Brodmann area or lobe. Any task or 'function' utilizes a complex and distributed set of brain areas.

The areas involved in performing a particular task are distributed in different locations in the brain, but the processing involved in task performance is not diffusely distributed among them. Each area makes a specific contribution to the performance of the task, and the contribution is determined by where the area resides within its richly connected parallel, distributed hierarchy."

Key Anatomical Terms

Long brain region names can be intimidating. These six descriptions can serve as your decoder ring. Anterior or rostral refers to toward the head end, whereas posterior or caudal means toward the tail. Dorsal means toward the top of the brain, while ventral means toward the bottom of the brain. A gyrus is a ridge of convoluted brain tissue, whereas a sulcus is a furrow (Breedlove & Watson, 2020).

Networks have functional and anatomical names. For example, the dorsal attention network (DAN) corresponds to the dorsal frontoparietal network (D-FPN).

Brodmann Areas

The original Brodmann areas consisted of 47 numbered cytoarchitectural zones of the cerebral cortex based on Nissl staining. They require subdividing certain areas and vary in size and shape across individuals.

Gordon et al. (2016) compared the original Brodmann areas and several other brain atlases using fMRI and concluded: "The Brodmann parcellation (Brodmann 1909) [. . .] does successfully represent structure in the data, but is too underparcellated to represent true cortical areas. This perspective agrees with modern attempts to anatomically parcellate human cortex, which frequently observe more fine-grained architectonic divisions than those reported by Brodmann (e.g., Morris et al. 2000, retrosplenial cortex; Öngür et al. 2003, orbitofrontal cortex; Morosan et al. 2005, superior temporal gyrus; Caspers et al. 2006, inferior parietal cortex; Scheperjans et al. 2008, superior parietal cortex; Kujovic et al. 2013, extrastriate visual cortex)." The more than 100-year-old Brodmann areas lack some of the specificity needed for in vivo and functional studies.

The revised Brodmann maps shown above reveal 180 regions, 100 of which were not previously identified (Glasser et al., 2016). The revised maps were contributed by Mark Dow, Research Assistant at the Brain Development Lab at the University of Oregon, to Wikimedia Commons. Cognitive neuroscience tends to use modern brain atlases of defined regions (e.g., Yeo et al., 2011) and exact coordinates like Montreal Neurological Institute (MNI) space and Talairach space (Chau & McIntosh, 2005). Researchers sometimes "convert" these coordinates to corresponding Brodmann areas since they are well-known. Brodmann areas participate in networks. They play an important role in neurofeedback because they help us target functional and structural networks instead of discrete, disconnected scalp sites. Neuroscience reveals the connections between Brodmann areas and how different networks can become active or quiet together during diverse conditions.

For example, attention to an object's location activates sites in four main areas that make up the dorsal attention network (dorsal frontoparietal network) are shown in blue. The five areas comprising the ventral attention network (ventral frontoparietal network) shown in red automatically process unanticipated environmental events. Flexible attention control involves the dynamic interaction of these top-down and bottom-up systems (Vossel et al., 2014).

Psychological disorders can be associated with shifts from normal activity in particular brain areas and their connections. For example, the figure below illustrates networks related to unipolar depression.

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