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, 2023).
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. Brodmann graphic © sciencepics/Shutterstock.com.
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.
In the aftermath of traumatic brain injury (TBI), neuronal connections across the entire brain change. For example, surviving long axonal projections no longer target inhibitory neurons (Frankowski et al., 2022).
Current neurofeedback protocols allow us to train networks involved in cognitive functions like attention or psychological disorders like depression using multiple electrodes simultaneously. Connectivity training enables us to increase or decrease communication between brain locations to treat symptoms and improve performance. Activations and deactivations are both important.
Quantitative EEG (qEEG) normative databases can reveal the key parts of a network that need training and the required direction.
Graphic courtesy of NeuroNavigator’s swLORETA Effective Connectivity Normative Database.
Brain Organization and Dynamics
The brain is organized into interactive functional, distributed networks with spatial, temporal, and content-based relationships. These networks interact through feedback loops and transiently organized aggregates of neurons, all mediated by rhythmic, oscillatory electrical discharges that ultimately produce the EEG. This process is further controlled/informed by selective attention to specific interest categories. The graphic below shows the default, salience, and central executive networks.
Nekovarova, Fajnerova, Horacek, Spaniel, CC BY 3.0, via Wikimedia Commons.
Each type of local cognitive, sensory processing, or emotional network produces oscillatory activity and contains internal stabilizing characteristics. These local networks exist within a global dynamic network system that links and provides an interactive capacity to the smaller networks, also operating within an oscillatory framework. A densely-connected lateral prefrontal and posterior parietal cortical network orchestrates responses to novel cognitive tasks using flexible hubs. The frontoparietal network assigns tasks to the most appropriate brain regions and shares information among these regions to master new skills (Cole et al., 2013).
The central nervous system processes incoming content. Separate regions process specialized content (e.g., auditory, kinesthetic, tactile, visual). Content is shared, integrated, compared to previous content, and analyzed. Decisions are made regarding memory and responses. All of this central nervous system activity occurs within interacting networks linked by electrical/chemical signals. Electrical discharges from network activity are recorded from the scalp surface as the EEG.
Network Overview
The networks most relevant to attention include the oculomotor, motor, affective, central autonomic, social, and executive circuits.
Oculomotor Network
The frontal eye field (FEF), in concert with the dorsolateral prefrontal cortex, posterior parietal cortex, basal ganglia, and thalamus, programs and initiates voluntary eye movements, inhibits eye movements toward distracting stimuli, and allows us to return our focus to locations we've experienced in the past (Thompson & Thompson, 2016). Simone Vossel, Joy J. Geng, Gereon R. Fink, CC BY 3.0, via Wikimedia Commons.
Motor Network
The supplementary motor area (SMA), in concert with the premotor cortex, primary motor cortex, sensorimotor cortex, and cerebellum, plans, initiates, and inhibits voluntary movements and muscle contractions (Breedlove & Watson, 2023; Thompson & Thompson, 2016). Chouinard PA and Paus T, CC BY 3.0, via Wikimedia Commons.
Caption: Motor areas in the frontal lobe. The premotor cortex on the lateral surface of the brain can be divided into the dorsal and ventral premotor areas (PMd and PMv) and the supplementary motor cortex on the medial wall of the brain can be divided into the supplementary motor and pre-supplementary motor areas (SMA and pre-SMA). Premotor cortex below the superior frontal sulcus is typically considered PMv whereas premotor cortex above this anatomical landmark is typically considered PMd. The vertical anterior-commissural line is often used to denote the boundary between SMA and pre-SMA. One can further divide PMd according to a rostral subdivision located along the superior frontal gyrus and a caudal subdivision located along the precentral gyrus. However, one cannot dissociate these two subdivisions with TMS easily and we therefore do not discuss them separately. There also exists two cingulate motor areas (RCZa and RZp) anterior to the vertical anterior-commissural line and one cingulate motor area (CCZ) posterior to the vertical anterior-commissural line.
Somato-Cognitive Action Network (SCAN)
Gordon and colleagues (2023), using precision fMRI from seven participants and fMRI datasets from the Adolescent Brain Cognitive Development Study, Human Connectome Project, and UK Biobank from 50,000 individuals, found three interconnected primary motor cortex (M1) regions that participate in the integrated movement of multiple body parts.
The somato-cognitive action network (SCAN) consists of M1, the SMA, Centromedian nucleus (CM) and Ventral Intermediate nucleus (VIM) of the thalamus, posterior putamen, and vermis and the flocculonodular lobe of the cerebellum, which mediate posture and balance. Connectivity analysis revealed that the SCAN communicates with the cingulo-opercular network (CON) or salience network, which mediates cognitive control and maintains task focus over extended periods. SCAN graphic by Johnson et al. (2024).
Caption: Somato-cognitive action network (SCAN). (A) Resting state functional connectivity (RSFC) seeded from the middle inter-effector node in primary motor cortex (bilaterally) in a representative individual (P1; 356 min resting-state fMRI). In cortex the SCAN includes three inter-effector nodes (superior, middle, inferior) that alternate with effector-specific foot, hand and mouth primary motor regions, as well as two nodes on the dorsal midline in the SMA (supplementary motor area) and dACC (dorsal anterior cingulate cortex) that are interleaved with the effector-specific regions of the SMA/pre-SMA.
M1's two interlacing systems establish a pattern of integration and isolation: regions specific to effectors, such as the foot, hand, and mouth, are responsible for the isolation of fine motor control, while the SCAN integrates goals, body movement, and physiology.
The apparent relative expansion of SCAN regions in humans could suggest a role in complex actions specific to humans, such as coordinating breathing for speech, and integrating hand, body and eye movement for tool use. A common factor across this wide range of processes is that they must be integrated if an organism is to achieve its goals through movement while avoiding injury and maintaining physiological allostasis. The SCAN provides a substrate for this integration, enabling pre-action anticipatory postural, breathing, cardiovascular and arousal changes (such as shoulder tension, increased heart rate or ‘butterflies in the stomach’). The finding that action and body control are melded in a common circuit could help explain why mind and body states so often interact.
Mesa (2023) placed these findings in context:
The dominant paradigm states that the motor cortex is simplistic. Planning, cognition, and conscious initiation of movements happen elsewhere in the brain; the motor cortex just receives these signals, relaying them directly to muscles.
The SCAN, in concert with the salience network, is responsible for complex adaptations (e.g., allostasis). These findings are consistent with primate studies showing that more M1 neurons are responsible for movements independent of the muscles used than for the contraction of specific muscles (Griffin et al., 2015; Kaufman et al., 2014). Together, these findings challenge the 1870 cortical homunculus model, a distorted human figure with body parts' size reflecting the amount of cortical area dedicated to them. Graphic by Mpj29. Mpj29, CC BY-SA 4.0, via Wikimedia Commons.
Affective Network
The pre- and subgenual areas of the anterior cingulate cortex (ACC) participate in affective circuits triggered when we make mistakes (Arnsten, 2009). The dorsal rostral cingulate zone monitors cognitive activity to predict when errors are likely, and greater executive control may be needed (Thompson & Thompson, 2016). The ventromedial prefrontal cortex projects to the amygdala, basal ganglia, hypothalamus, and brainstem arousal and reward pathways. Graphic by Gonzalez-Madruga et al. (2022). The affective network is designated by AN.
Caption: Illustration of six resting-state networks: The default mode (DMN), affective (AN), frontoparietal control (FPCN), dorsal attention (DAN), salience (SN) and the somatomotor/sensory networks (SMN). Arrows reflect findings of within and between network hypo-and hyperconnectivity in individuals with ADHD as identified by Gao et al. (2019).
Central Autonomic Network
The central autonomic network (CAN) consists of forebrain, limbic, and brainstem regions (Bennaroch, 1993). Neuroimaging studies reveal a cortico-limbic network responsible for autonomic control, consisting of the ventromedial prefrontal cortex (VMPFC), cingulate cortex, insula, mediodorsal thalamus, hypothalamus, and amygdala (Beissner et al., 2013; Shoemaker et al., 2015; Schuman et al., 2021; Thayer et al., 2012). Thayer and Lane (2000) proposed a neurovisceral integration model. Their model places the prefrontal cortex at the top of a hierarchical structure, with direct functional links with the insula and cingulate (Thayer et al., 2012). The limbic system extends these connections through the amygdala to downstream subcortical regions, such as the hypothalamus and brainstem nuclei, which are integral for the parasympathetic and sympathetic heart rate modulation at the lowest level of this model. The neurovisceral integration model highlights the crucial role of the prefrontal cortex and its superior control over subcortical structures in heart rate regulation and in linking sympathovagal balance with cognitive and emotional processes. Neurovisceral model graphic by Nikolin et al. (2017).
Caption: Neurovisceral integration model. (A) Simplified depiction of the neurovisceral integration model described by Thayer and Sternberg [1]. (B) Brain regions relevant to the neurovisceral integration model. PC, prefrontal cortex; CC, cingulate cortex; Hyp, hypothalamus; Ins, insula; Amy, amygdala; BS, brainstem.
Magnetic resonance imaging (MRI) and resting-state functional connectivity (RSFC) studies have reinforced the critical role of interplay between the medial prefrontal cortex and limbic regions in controlling heart rate (Kumral et al., 2019; Sakaki et al., 2016). Individuals with slower heart rates exhibited significantly heightened RSFC within a functional network comprising various central autonomic and sensorimotor system regions compared to those with faster heart rates (de la Cruz et al., 2019). Slower heart rates were associated with an elevated RSFC between the prefrontal cortex (VMPFC) and the anterior insula.
Social Network
The orbitofrontal cortex (OFC), along with the basal ganglia and thalamus, orchestrates the highest level of emotional processing in the nervous system. The social network is responsible for socially responsible behavior, empathy, behavioral inhibition, emotional regulation, and sound judgment (Thompson & Thompson, 2016). Graphic courtesy of Han and colleagues (2021).
The blue nodes represent the mentalizing network, including the vmPFC (ventromedial prefrontal cortex), OFC (orbitofrontal cortex), dlPFC (dorsolateral prefrontal cortex), and dmPFC (dorsomedial prefrontal cortex). This network enables us to think about our own and others' mental states (Hoskinson et al., 2019). The orange dots are the mirror network (superior temporal sulcus, STS), which is active during our performance and observations of others' actions. The mirror network supports observational learning and social cognition (Sadeghi et al., 2022). The green dot is the amygdala, which detects salient stimuli. The yellow dot is the entorhinal cortex. Finally, the red dot is the anterior insular cortex, AIC.
Executive Network
The dorsolateral prefrontal cortex plays a critical role in executive functions, which Kropotov (2009) described as "the coordination and control of motor and cognitive actions to attain specific goals." Executive functions include allocation of attention, cognitive inhibition, behavioral inhibition, working memory, and cognitive flexibility. The executive network focuses and maintains continuous attention (Faraone et al., 2015). This network shows reduced activation and connectivity in ADHD. Seven-network graphic by Feirrera et al. (2022). The Executive Network is shown in blue.
Caption: Brain networks: One atlas with seven networks. Seven brain networks derived from resting‐state fMRI data were adapted from Schaefer et al. (2018).
Attentional Processes
Attention is the selection of sensory information or cognition for enhanced processing. We can overtly or covertly attend to stimuli. In overt attention, our attentional focus and sensory orientation coincide. For example, you parse this sentence as you focus your gaze on it. In covert attention, we shift our attentional focus from our sensory orientation. For example, you attend to a reminder on the corner of your screen while you gaze at this sentence. While the midbrain superior colliculus is mainly implicated in overt attention, it may also regulate covert attention (Breedlove & Watson, 2023).
Cortical Regions That Guide Attention
The dorsal attention network (dorsal frontoparietal system), which is comprised of the intraparietal sulcus and frontal eye field, is responsible for the top-down direction of attention (Breedlove & Watson, 2020). This system is responsible for endogenous attention (voluntary attention), directing the attentional spotlight to support cognitive system priorities. The intraparietal sulcus (IPS), located in the parietal lobe, provides voluntary top-down attention steering (Corbetta & Shulman, 1998). The frontal eye field (FEF), found in the premotor region of the frontal lobes, directs our gaze toward targets selected by the IPS (Paus et al., 1991). Target selection is guided by cognitive goals (top-down processing) rather than stimulus characteristics (bottom-up processing). Graphic by Aboitiz et al. (2014). The dorsal attention network is shown below in gold.
Caption: The dorsal attentional network (DAN) and the ventral attention network (VAN). DAN (yellow/orange): FEF, frontal eye fields; IPS, inferior parietal sulcus; SPL, superior parietal lobe. VAN (blue): IFG, inferior frontal gyrus; IPL, inferior parietal lobe (posterior aspect); MFG, middle frontal gyrus; TPJ, temporo-parietal junction; STG, superior temporal gyrus. Data from Corbetta and Shulman (2002), with permission.
In contrast, the ventral attention network (ventral frontoparietal network), where the superior temporal gyrus and inferior parietal lobe intersect, mediates bottom-up shifts in attention in response to stimulus attributes (Corbetta & Shulman, 2002). It controls involuntary reflexive attention, redirecting attention based on the novelty or importance of incoming stimuli. The TPJ functions like a circuit breaker by overruling immediate attentional priorities and reallocating attentional resources to a new target (Breedlove & Watson, 2023).
Extensive interconnections between the dorsal and ventral attention systems allow us to fluidly redirect attention from stimuli that are forebrain priorities (IPS) to those that are unexpected.
Attention system graphic by deBoer et al. (2020). The dorsal attention network (blue) is responsible for top-down, and the ventral attention network (gold) mediates bottom-up attention.
Caption: Humans can perceive the world from different perspectives: an endogenous (Self) versus exogenous (Other) focus of attention. Self-produced signals are predicted in the brain (e.g., efference copies), whereas external signals are not. These predictions allow the brain to early discriminate self-produced signals from external stimulation and keep track of its own spatiotemporal location (perspective). Endogenous attention: the dorsal system is goal-directed and processes self-produced signals. ‘Cognitive control’ enables the top-down voluntary selection of stimuli and responses, resp.: dorsolateral prefrontal cortex (dlPFC), frontal eye field (FEF), intraparietal sulcus (IPS) and visual cortex. Exogenous attention * lateralized to the right hemisphere: the ventral system is stimulus-driven and processes external signals. ‘Stimulus control’ enables the automatic orientation and bottom-up detection of behaviorally relevant stimuli, resp.: visual cortex, angular gyrus (AG; part of the temporoparietal junction), and the ventral frontal cortex. These attention systems keep external and self-produced signal processing separated and interconnect at the right AG (double arrow).
Extensive interconnections between the dorsal and ventral attention systems allow us to fluidly redirect attention from stimuli that are forebrain priorities (IPS) to those that are unexpected.
Two Cortical Networks Regulate Attention
Salience Network
The salience network (midcingulo-insular network; M-CIN) comprises structures that monitor our external and internal environments to determine which of these inputs are essential and require further processing and attention. The insula, primarily the anterior insula, is a crucial component of this network because it facilitates bottom-up access to the brain’s attentional and working memory resources (Menon & Uddin, 2010). The cingulate gyrus is another crucial component, particularly the right dorsal anterior cingulate cortex (Thompson & Thompson, 2016). Graphic by van Ettinger-Veenstra et al. (2019).
Caption: Node connections of the default mode network and the salience network. Cartoon of node connections used in this study as seen on a see-through brain. Green shows the connections between the nodes of the DMN and orange the connection between the nodes of the SN.
The clinical literature on ADHD, depression, and schizophrenia has explored the role of the anterior cingulate gyrus (ACC) in these disorders. The ACC's deep brain stimulation has successfully improved treatment-resistant depression (Mayberg et al., 2005).
The insula, which is a cortical region located within the lateral sulcus, functions as an integrative and organization hub for the salience network. The insula integrates interoceptive awareness, emotional experience, and external perception to facilitate an individual's global perception of the world and its relationship. The insula directs specific networks in processing salient stimuli and generating appropriate responses to stimuli (Wiebking & Northoff, 2014).
Yasmine Y. Fathy, Susanne E. HoogersHenk W. BerendseYsbrand D. van der WerfPieter J. VisserFrank J. de JongWilma D.J. van de Berg, CC BY 4.0, via Wikimedia Commons.
Caption: Insular Cortex Sub-regions in MRI. The central sulcus generally divides the insular cortex into anterior and posterior subdivisions. The anterior insular cortex is further divided into ventral (red) and dorsal sub-regions (green) and the dorsal insula can be divided into an anterior and mid-region. The left figure shows the sub-regions of the insular cortex based on the Hammers_mith probabilistic atlas (Faillenot et al., 2017). Surface rendering of the insular sub-regions in 3D is shown on the right. A: anterior, dAI: dorsal anterior insula, I: inferior, P: posterior, PI: posterior insula, S: superior, vAI: ventral anterior insula.
The insula interfaces with the human brain's cognitive, homeostatic, and affective systems. It links the areas of the brain that monitor internal signals and those engaged in watching incoming external sensory streams. The insula detects salient events via afferent pathways and switches between other large-scale networks when these events are recognized to guide attention and working memory.
The anterior and posterior insula interact to regulate autonomic responses to salient stimuli. Interactive communication between the insula and anterior cingulate cortex facilitates access to the motor system (Menon & Uddin, 2010).
This network appears to help us switch between task-oriented (executive) and default mode (attention) networks (Seeley et al., 2007; Shirer et al., 2012).
Default Mode Network (DMN)
Brain regions are selectively active when we are conscious (Breedlove & Watson, 2020). The default mode network (DMN) consists of frontal, temporal, and parietal lobe circuits active during spontaneous cognition like introspection, daydreaming, and streams of consciousness. The DMN appears to contribute to flexible memory retrieval and idea generation, critical creativity elements. The DMN is relatively inactive when pursuing external goals (Andrews-Hanna et al., 2010). Shim G, Oh JS, Jung WH, et al., CC BY 3.0, via Wikimedia Commons.
Caption: Default mode and task-related maps for healthy subjects. On a green background, the default mode network is highlighted in warm colors (red and yellow) and the task-related network is highlighted in cold colors (blue and light blue) depending on the p-value of one-sample t-test.
ADHD has been linked to irregular connectivity among brain regions, including within the DMN (Cao et al., 2014). Notably, the strength of specific brain connections can accurately forecast variations in one's capacity to maintain attention (Rosenberg et al., 2016, 2017). This holds true even in a resting state when the individual is not actively engaged in any specific task. There has been increasing discussion about whether some of the DMN regions are actually part of a distinctively separate Parietal Memory Network (PMN) that includes the precuneus (PCU), the mid-cingulate cortex (MCC), and the posterior inferior parietal lobule/dorsal angular gyrus (pIPL/dAG; Gilmore, Nelson, & McDermott, 2015; Hu et al., 2016). Deactivations can be as important as activations. For example, the degree of DMN deactivation seems to be critically important for aiding attentional control – people who can suppress it more can learn new material more easily (e.g., Nelson et al., 2016; Zerr et al., 2018). The DMN also helps to synthesize details into single coherent events and is important for envisioning the future (e.g., Gilmore, Nelson, Chen, & McDermott, 2018).
The DMN may contribute to creative fluency, generating innovative ideas like alternative uses for everyday objects. A study of neurosurgical patients showed that left DMN stimulation reduced the number of uses but not their originality (Shofty et al., 2022).
Understanding Ourselves
The posterior cingulate cortex (PCC) and precuneus combine bottom-up attention with information from memory and perception. The ventral (lower) part of the PCC activates in all tasks which involve the DMN, including those related to the self or others, remembering the past, thinking about the future, processing concepts, and spatial navigation. The dorsal (upper) part of PCC mediates involuntary awareness and arousal. The precuneus is concerned with visual, sensorimotor, and attentional information.
The medial prefrontal cortex (mPFC) participates in decisions about the self, such as personal information, autobiographical memories, future goals and events, and decision-making regarding those close to us, like family members. The ventral (lower) part involves positive emotional information and reward.
The angular gyrus connects perception, attention, spatial cognition, and action and helps us recall episodic memories.
Understanding Others
The major functional hubs include the PCC, mPFC, and angular gyrus. The dorsal medial prefrontal cortex (dmPFC) analyzes others' objectives. The temporoparietal junction (TPJ) constructs theories of mind, which are models of others' cognitive processes, emotions, knowledge, and motivation. The lateral temporal cortex concerns short-term verbal memory, naming, and reading. Finally, the anterior temporal pole is part of a bilateral semantic system representing object concepts and a left hemisphere-dominant network concerned with naming and understanding object names.
Autobiography and Future Simulations
The major functional hubs include the PCC, mPFC, and angular gyrus. The hippocampus forms new declarative memories. The parahippocampal cortex (PHC) mediates spatial memory, navigation, and high-level visual processing like facial recognition. The retrosplenial cortex (RSC) participates in episodic memory, navigation, predicting future events, and analyzing visual scenes. Finally, the posterior inferior parietal lobe (pIPL) integrates sensory information and participates in top-down attentional orienting. Graphic courtesy of the New Scientist (Fox, 2008).
The Pulvinar Mediates Attentional Shifts
The pulvinar nucleus, which comprises the posterior quarter of the human thalamus, processes visual information and directs attention. The pulvinar plays a pivotal role in processing visual information and shares widespread connections with the cingulate, parietal cortex, and superior colliculus. The pulvinar is crucial for orienting, shifting attention, and filtering out irrelevant stimuli. Tasks that present subjects with more distracting stimuli increase pulvinar activation, as shown by the functional MRI (fMRI) (Buchsbaum et al., 2006). Thalamus graphic © Songkram Chotik-anuchit/Shutterstock.com.
Overall, the pulvinar guides the processing of relevant information in wide-ranging cortical networks based on dynamically changing attentional priorities (Breedlove & Watson, 2023; Saalmann et al., 2012).
Neurofeedback training increasingly monitors and trains network activity using qEEG normative databases. Neurofeedback professionals must thoroughly understand Brodmann areas and the functional and structural networks in which they participate to employ these protocols effectively.
Appreciation
Christopher L. Zerr, Postdoctoral Research Associate in Dr. Henry Roediger’s memory lab at Washington University in St. Louis, contributed extensively to this post.
Glossary
affective network: a network that is triggered when we make mistakes and that monitors cognitive activity to predict when errors are likely, and greater executive control may be needed. The affective network includes the anterior cingulate cortex, hippocampal cortex, entorhinal cortex, superior temporal gyrus, inferior temporal gyrus, posterior parietal cortex, globus pallidus internal segment, substantia nigra, pars reticulata, and medial dorsal nucleus of the thalamus.
amygdala: limbic system structure that participates in evaluating whether stimuli are salient (rewarding or threatening), establishing unconscious emotional memories, learning conditioned emotional responses, and producing anxiety and fear responses.
attention: the selection of sensory information or cognition for enhanced processing. attentional spotlight: a shift of selective attention to choose stimuli for enhanced processing. central autonomic network (CAN): a complex system of brain regions that is involved in the regulation of the autonomic nervous system. This network includes several brain structures like the prefrontal cortex, anterior cingulate cortex, insula, amygdala, hypothalamus, periaqueductal gray, parabrachial complex, nucleus of the solitary tract, and the medulla oblongata. These structures work together to regulate the body's physiological states, such as heart rate, blood pressure, respiration, digestion, and thermoregulation. central executive network: structures including the dorsolateral prefrontal cortex, anterior cingulate cortex, and orbitofrontal cortex responsible for cognitive regulation of behavior. cingulate cortex: cortex that lies above the corpus callosum that is responsible for the motivation dimension of attention, like pain due to physical injury and social rejection. covert attention: an attentional focus independent of sensory orientation. creative fluency: generating creative ideas like alternative uses for everyday objects.
default mode network: frontal, temporal, and parietal lobe circuits that are active during introspection and daydreaming and relatively inactive when we pursue external goals.
dorsal frontoparietal system: the network comprised of the intraparietal sulcus and frontal eye field responsible for the top-down direction of attention.
dorsolateral prefrontal cortex: the left dorsolateral prefrontal cortex is concerned with approach behavior and positive affect. It helps us select positive goals and organizes and implements behavior to achieve these goals. The right dorsolateral prefrontal cortex organizes withdrawal-related behavior and negative affect and mediates threat-related vigilance. It plays a role in working memory for object location. early selection: filtering out lower-priority competing stimuli before preliminary perceptual and semantic analysis. endogenous attention: voluntary attention that directs the attentional spotlight to support cognitive system priorities. executive network: a network responsible for allocating attention, cognitive inhibition, behavioral inhibition, working memory, and cognitive flexibility. The executive network includes the dorsolateral prefrontal cortex, posterior parietal cortex, arcuate premotor area, globus pallidus internal segment, substantia nigra, pars reticulata, ventral anterior nucleus of the thalamus, and medial dorsal nucleus of the thalamus. exogenous attention: involuntary reflexive attention that redirects attention based on the novelty or importance of incoming stimuli. frontal eye field (FEF): region of the premotor cortex that directs gaze towards targets selected by the IPS. functional networks: correlated activity between regions over time. insula: the cortical region located within the lateral sulcus of the frontal, parietal, and temporal lobes that functions as an integrative and organization hub for the salience network.
intraparietal sulcus (IPS): the region of the parietal lobe that provides voluntary top-down steering of attention. late selection: filtering out competing stimuli after performing extensive analysis. magnetic resonance imaging (MRI): a non-invasive imaging technology that uses a strong magnetic field and radio waves to produce detailed images of the inside of the body. It's especially useful for imaging soft tissues and organs like the brain, spinal cord, muscles, and heart. It provides high-resolution, 3D images that can be viewed from different angles, making it a valuable tool in medical diagnosis and research. motor network: the network that plans, initiates and inhibits voluntary movements and muscle contractions. The motor network includes the supplementary motor area, premotor cortex, primary motor cortex, primary somatosensory cortex, cerebellum, arcuate premotor area, globus pallidus internal segment, substantia nigra, pars reticulata, and ventral lateral nucleus of the thalamus. neurovisceral integration model: a theoretical framework that suggests the heart, brain, and other bodily systems are interconnected and communicate with each other to maintain overall health and well-being. It posits that autonomic, attentional, and affective systems are integrated within the central autonomic network and that imbalances within this network may underlie the associations between stress, disease, and cognitive function. oculomotor network: a network that programs and initiates voluntary eye movements, inhibits eye movements toward distracting stimuli, and allows us to return our focus to locations we've previously experienced. The oculomotor network includes the frontal eye field, dorsolateral prefrontal cortex, posterior parietal cortex, caudate, globus pallidus internal segment, substantia nigra, pars reticulata, ventral anterior nucleus of the thalamus, and medial dorsal nucleus of the thalamus.
overt attention: the agreement between attentional focus and sensory orientation. prefrontal cortex: the most anterior region of the frontal lobes divided into orbitofrontal and ventromedial, dorsolateral prefrontal cortex, and anterior and ventral cingulate cortex subdivisions, and is responsible for the brain’s executive functions. pulvinar nucleus: the posterior region of the thalamus that processes visual information and directs attention. resting-state functional connectivity (RSFC): a neuroimaging method used to investigate brain networks that are active when a person is not focused on the outside world, often referred to as "at rest". These brain networks show synchronous activity when the person is not performing an explicit task. It's used in functional magnetic resonance imaging (fMRI) studies to assess connectivity and coordination between different parts of the brain. It helps in understanding brain organization and the baseline level of neural activity. salience network: structures including the insula and anterior cingulate cortex that seek to monitor our external and internal environments to determine which of these inputs are salient and require further processing and attention. social network: the network that mediates socially responsible behavior, empathy, behavioral inhibition, emotional regulation, and sound judgment. The social network includes the orbitofrontal cortex, superior temporal gyrus, inferior temporal gyrus, anterior cingulate cortex, caudate, globus pallidus internal segment, substantia nigra, pars reticulata, ventral anterior nucleus of the thalamus, and medial dorsal nucleus of the thalamus.
structural networks: axonal projections and pathways. temporoparietal junction (TPJ): the intersection of the superior temporal gyrus and inferior parietal lobe that mediates bottom-up shifts in attention in response to stimulus attributes. ventromedial prefrontal cortex: a region of the prefrontal cortex that may play a role in calculating risk and the emotional responses of anxiety and fear. Cortisol binding to this structure increases anxiety and fear and disrupts and kills neurons.
Andrews-Hanna, J. R., Reidler, J. S., Huang, C., & Buckner, R. L. (2010). Evidence for the default network's role in spontaneous cognition. J Neurophysiol., 104, 1664-1671. https://doi.org/10.1152/jn.00830.2009 Arnsten A. F. (2009). Stress signaling pathways that impair prefrontal cortex structure and function. Nature Reviews. Neuroscience, 10(6), 410–422. https://doi.org/10.1038/nrn2648 Beissner, F., Meissner, K., Bär, K. J., & Napadow, V. (2013). The autonomic brain: An activation likelihood estimation meta-analysis for central processing of autonomic function. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 33(25), 10503–10511. https://doi.org/10.1523/JNEUROSCI.1103-13.2013 Benarroch E. E. (1993). The central autonomic network: Functional organization, dysfunction, and perspective. Mayo Clinic Proceedings, 68(10), 988–1001. https://doi.org/10.1016/s0025-6196(12)62272-1 Breedlove, S., M., & Watson, N. V. (2023). Behavioral neuroscience (10th ed.). Sinauer Associates, Inc. Buchsbaum, M. S., Buchsbaum, B. R., Chokron, S., Tang, C., Wei, T. C., & Byne, W. (2006). Thalamocortical circuits: fMRI assessment of the pulvinar and medial dorsal nucleus in normal volunteers. Neuroscience Letters, 404(3), 282-287. https://doi.org/10.1016/j.neulet.2006.05.063
Cao, M., Shu, N., Cao, Q., Wang, Y., & He, Y. (2014). Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder. Molecular Neurobiology, 50(3), 1111–1123. https://doi.org/10.1007/s12035-014-8685-x
Catani, M., & Thiebault de Schotten, M. (2012). Atlas of human brain connections. Oxford University Press.
Chau, W., & McIntosh, A. R. (2005). The Talairach coordinate of a point in the MNI space: How to interpret it. NeuroImage, 25(2), 408–416. https://doi.org/10.1016/j.neuroimage.2004.12.007
Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16, 1348–1355. https://doi.org/10.1038/nn.3470
Corbetta, M., & Shulman, G. I. (1998). Human cortical mechanisms of visual attention during orienting and search. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 353(1373), 1353-1362. https://doi.org/10.1098/rstb.1998.0289
Critchley H. D. (2005). Neural mechanisms of autonomic, affective, and cognitive integration. The Journal of Comparative Neurology, 493(1), 154–166. https://doi.org/10.1002/cne.20749
Critchley H. D. (2009). Psychophysiology of neural, cognitive and affective integration: fMRI and autonomic indicants. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 73(2), 88–94. https://doi.org/10.1016/j.ijpsycho.2009.01.012
Davidson, R. J. (2000). The functional neuroanatomy of affective style. Cognitive neuroscience of emotion. Oxford University Press, 371–388. de la Cruz, F., Schumann, A., Köhler, S., Reichenbach, J. R., Wagner, G., & Bär, K. J. (2019). The relationship between heart rate and functional connectivity of brain regions involved in autonomic control. NeuroImage, 196, 318–328. https://doi.org/10.1016/j.neuroimage.2019.04.014 Faraone, S. V., Biederman, J., & Mick, E. (2006). The age-dependent decline of attention deficit hyperactivity disorder: A meta-analysis of follow-up studies. Psychological medicine, 36(2), 159–165. https://doi.org/10.1017/S003329170500471X A. Fornito, A. Zalesky, & E. T. Bullmore (Eds.) (2016). Fundamentals of brain network analysis. Elsevier. https://doi.org/10.1016/C2012-0-06036-X Fox, D. (2008). The secret life of the brain. New Scientist, 2681. Frankowski, J. C., Tierno, A., Pavani, S., Cao, Q., Lyon, D. C., & Hunt, R. F. (2022). Brain-wide reconstruction of inhibitory circuits after traumatic brain injury. Nat Commun, 13, 3417. https://doi.org/10.1038/s41467-022-31072-2 Gilmore, A. W., Nelson, S. M., Chen, H. Y., & McDermott, K. B. (2018). Task-related and resting-state fMRI identify distinct networks that preferentially support remembering the past and imagining the future. Neuropsychologia, 110, 180–189. https://doi.org/10.1016/j.neuropsychologia.2017.06.016
Gilmore, A. W., Nelson, S. M., & McDermott, K. B. (2015). A parietal memory network revealed by multiple MRI methods. Trends in Cognitive Sciences, 19(9), 534–543. https://doi.org/10.1016/j.tics.2015.07.004 Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., Smith, S. M., & Van Essen, D. C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178. https://doi.org/10.1038/nature18933 Gordon, E. M., Chauvin, R. J., Van, A. N., Rajesh, A., Nielsen, A., Newbold, D. J., Lynch, C. J., Seider, N. A., Krimmel, S. R., Scheidter, K. M., Monk, J., Miller, R. L., Metoki, A., Montez, D. F., Zheng, A., Elbau, I., Madison, T., Nishino, T., Myers, M. J., Kaplan, S., … Dosenbach, N. U. F. (2023). A somato-cognitive action network alternates with effector regions in motor cortex. Nature, 617(7960), 351–359. https://doi.org/10.1038/s41586-023-05964-2 Gorges, M., Müller, H. P., & Kassubek, J. (2018). Structural and functional brain mapping correlates of impaired eye movement control in Parkinsonian Syndromes: A systems-based concept. Frontiers in Neurology, 9, 319. https://doi.org/10.3389/fneur.2018.00319 Griffin, D. M., Hoffman, D. S., & Strick, P. L. (2015). Corticomotoneuronal cells are "functionally tuned." Science, 350(6261), 667–670. https://doi.org/10.1126/science.aaa8035 Gross J. J. (2002). Emotion regulation: Affective, cognitive, and social consequences. Psychophysiology, 39(3), 281–291. https://doi.org/10.1017/s0048577201393198 Han, M., Jiang, G., Luo, H., & Shao, Y. (2021). Neurobiological bases of social networks. Frontiers in Psychology, 12, 626337. https://doi.org/10.3389/fpsyg.2021.626337 Holmes, A. J., MacDonald, A., 3rd, Carter, C. S., Barch, D. M., Andrew Stenger, V., & Cohen, J. D. (2005). Prefrontal functioning during context processing in schizophrenia and major depression: An event-related fMRI study. Schizophrenia Research, 76(2-3), 199–206. https://doi.org/10.1016/j.schres.2005.01.021 Hoskinson, K. R., Bigler, E. D., Abildskov, T. J., Dennis, M., Taylor, H. G., Rubin, K., Gerhardt, C. A., Vannatta, K., Stancin, T., & Yeates, K. O. The mentalizing network and theory of mind mediate adjustment after childhood traumatic brain injury. Soc Cogn Affect Neurosci., 14(12), 1285-1295. https://doi.org/10.1093/scan/nsaa006. PMID: 31993655; PMCID: PMC7137721. Hu, Y., Wang, J., Li, C., Wang, Y. S., Yang, Z., & Zuo, X. N. (2016). Segregation between the parietal memory network and the default mode network: Effects of spatial smoothing and model order in ICA. Science Bulletin, 61(24), 1844–1854. https://doi.org/10.1007/s11434-016-1202-z Jackson, D. C., Malmstadt, J. R., Larson, C. L., & Davidson, R. J. (2000). Suppression and enhancement of emotional responses to unpleasant pictures. Psychophysiology, 37(4), 515–522. Kaufman, M. T., Churchland, M. M., Ryu, S. I., & Shenoy, K. V. (2014). Cortical activity in the null space: Permitting preparation without movement. Nature Neuroscience, 17(3), 440–448. https://doi.org/10.1038/nn.3643 Kroptov, J. D. (2009). Quantitative EEG, event-related potentials and neurotherapy. Academic Press, Elsevier.
Kumral, D., Schaare, H. L., Beyer, F., Reinelt, J., Uhlig, M., Liem, F., Lampe, L., Babayan, A., Reiter, A., Erbey, M., Roebbig, J., Loeffler, M., Schroeter, M. L., Husser, D., Witte, A. V., Villringer, A., & Gaebler, M. (2019). The age-dependent relationship between resting heart rate variability and functional brain connectivity. NeuroImage, 185, 521–533. https://doi.org/10.1016/j.neuroimage.2018.10.027
Makovac, E., Meeten, F., Watson, D. R., Herman, A., Garfinkel, S. N., D Critchley, H., & Ottaviani, C. (2016). Alterations in amygdala-prefrontal functional connectivity account for excessive worry and autonomic dysregulation in Generalized Anxiety Disorder. Biological Psychiatry, 80(10), 786–795. https://doi.org/10.1016/j.biopsych.2015.10.013
Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C., Schwalb, J. M., & Kennedy, S. H. (2005). Deep brain stimulation for treatment-resistant depression. Neuron, 45(5), 651–660. https://doi.org/10.1016/j.neuron.2005.02.014
Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Struct Function, 214(5-6), 655-667. https://doi.org/10.1007/s00429-010-0262-0
Mesa, N. (2023). New brain network connecting mind and body discovered. Retrieved from The Scientist.
Nelson, S. M., Savalia, N. K., Fishell, A. K., Gilmore, A. W., Zou, F., Balota, D. A., & McDermott, K. B. (2016). Default mode network activity predicts early memory decline in healthy young adults aged 18-31. Cerebral Cortex, 26(8), 3379–3389. https://doi.org/10.1093/cercor/bhv165 Newbold, D. J., & Dosenbach, N. U. F. (2021). Tracking plasticity of individual human brains. Current Opinion in Behavioral Sciences, 40, 161-168. https://doi.org/10.1016/j.cobeha.2021.04.018 Nikolin, S., Boonstra, T. W., Loo, C. K., & Martin, D. (2017). Combined effect of prefrontal transcranial direct current stimulation and a working memory task on heart rate variability. PloS one, 12(8), e0181833. https://doi.org/10.1371/journal.pone.0181833 Ottaviani, C., Watson, D. R., Meeten, F., Makovac, E., Garfinkel, S. N., & Critchley, H. D. (2016). Neurobiological substrates of cognitive rigidity and autonomic inflexibility in generalized anxiety disorder. Biological Psychology, 119, 31–41. https://doi.org/10.1016/j.biopsycho.2016.06.009 Paus, T., Kalina, M., Patocková, L., Angerová, Y., Cerný, R., Mecir, P., Bauer, J., & Krabec, P. (1991). Medial vs lateral frontal lobe lesions and differential impairment of central-gaze fixation maintenance in man. Brain, 114, 2051-2067. https://doi.org/10.1093/brain/114.5.2051 Petersen, S. E., & Fiez, J. A. (1993). The processing of single words studied with positron emission tomography. Annual Review of Neuroscience, 16, 509-530. https://doi.org/10.1146/annurev.ne.16.030193.002453 Rogers, M. A., Kasai, K., Koji, M., Fukuda, R., Iwanami, A., Nakagome, K., Fukuda, M., & Kato, N. (2004). Executive and prefrontal dysfunction in unipolar depression: A review of neuropsychological and imaging evidence. Neuroscience Research, 50(1), 1–11. https://doi.org/10.1016/j.neures.2004.05.003 Rosenberg, M. D., Finn, E. S., Scheinost, D., Constable, R. T., & Chun, M. M. (2017). Characterizing attention with predictive network models. Trends in Cognitive Sciences, 21(4), 290–302. https://doi.org/10.1016/j.tics.2017.01.011 Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience, 19(1), 165–171. https://doi.org/10.1038/nn.4179 Saalmann, Y. B., Pinsk, M. A., Wang, L., Li, X., & Kastner, S. (2012). The pulvinar regulates information transmission between cortical areas based on attention demands. Science, 337(6095), 753-756. https://doi.org/10.1126/science.1223082
Sadeghi, S., Schmidt, S., Mier, D., & Hass, J. (2022). Effective connectivity of the human mirror neuron system during social cognition. Social cognitive and affective neuroscience, 17(8), 732–743. https://doi.org/10.1093/scan/nsab138 Sakaki, M., Yoo, H. J., Nga, L., Lee, T. H., Thayer, J. F., & Mather, M. (2016). Heart rate variability is associated with amygdala functional connectivity with MPFC across younger and older adults. NeuroImage, 139, 44–52. https://doi.org/10.1016/j.neuroimage.2016.05.076 Sanders, R. H., & Levitin, D. J. (2020). Towards an understanding of control of complex rhythmical "wavelike" coordination in humans. Brain Sciences, 10(4), 215. https://doi.org/10.3390/brainsci10040215 Shoemaker, J. K., Norton, K. N., Baker, J., & Luchyshyn, T. (2015). Forebrain organization for autonomic cardiovascular control. Autonomic Neuroscience: Basic & Clinical, 188, 5–9. https://doi.org/10.1016/j.autneu.2014.10.022 Schumann, A., de la Cruz, F., Köhler, S., Brotte, L., & Bär, K. J. (2021). The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity. Frontiers in Neuroscience, 15, 691988. https://doi.org/10.3389/fnins.2021.691988 Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Rice, A. L., & Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci, 27(9), 2349-2356. https://doi.org/10.1523/JNEUROSCI.5587-06.2007 Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., Greicius, M. D. (2012). Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex, 22. https://doi.org/158–165.10.1093/cercor/bhr099 Shofty, B., Gonent, T., Bergmann, E., Mayseless, N., Korn, A., Shamay-Tsoory, S., Grossman, R., Jalon, I., & Ram, Z. (2022). The default network is causally linked to creative thinking. Molecular Psychiatry. https://doi.org/10.1038/s41380-021-01403-8 Thayer, J. F., Hansen, A. L., Saus-Rose, E., & Johnsen, B. H. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: The neurovisceral integration perspective on self-regulation, adaptation, and health. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 37(2), 141–153. https://doi.org/10.1007/s12160-009-9101-z Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61(3), 201–216. https://doi.org/10.1016/s0165-0327(00)00338-4 Thompson, M., & Thompson, L. (2015). The neurofeedback book (2nd ed.). Association for Applied Psychophysiology and Biofeedback. van Ettinger-Veenstra, H., Lundberg, P., Alföldi, P., Södermark, M., Graven-Nielsen, T., Sjörs, A., Engström, M., & Gerdle, B. (2019). Chronic widespread pain patients show disrupted cortical connectivity in default mode and salience networks, modulated by pain sensitivity. Journal of pain research, 12, 1743–1755. https://doi.org/10.2147/JPR.S189443
Vossel, S., Geng, J. J., & Fink, G. R. (2014). Dorsal and ventral attention systems: Distinct neural circuits but collaborative roles. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 20(2), 150–159. https://doi.org/10.1177/1073858413494269 Wiebking, C., & Northoff, G. (2014). Interoceptive awareness and the insula - Application of neuroimaging techniques in psychotherapy. GSTF International Journal of Psychology, 1(1), 53-60. https://doi.org/10.5176/0000-0002_1.1.8
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011
Zerr, C. L., Berg, J. J., Nelson, S. M., Fishell A. K., Savalia, N. K., & McDermott, K. B. (2018). Learning efficiency: Identifying individual differences in learning rate and retention in healthy adults. Psychological Science, 956797618772540. PMID 29953332 https://doi.org/10.1177/0956797618772540
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