Compensatory Training With Neurofeedback

By Richard Soutar, Ph.D. BCN

Copyright 2012 by Richard Soutar

Downtrain Beta Only Why do the other frequencies respond?

Complex vs Simple Map What Should I Train?

Cathy K vs Kathy T

Pre Post ADHD & Depression Z score & Asymmetry Training

Jasmine M

Laplacian O1-O2? 5Hz? 6Hz?

ROI?-Fz? T3-T4?

5Hz (X= 32 , Y= -32 , Z= -13)

1st Best Match (d= 4 mm) Brodmann area 36 Parahippocampal Gyrus Limbic Lobe

6Hz (X= -3 , Y= -11 , Z= -6) 1st Best Match (d= 14 mm) Brodmann area 25 Anterior Cingulate Limbic Lobe

MiniQ Version

MiniQ Analysis- Network Analysis Multivariate Nonparametric Weighting

• Primary Networks match Laplacian O1-O2

• Networks 7, 11, 12

Pre-Post Map Evaluation

Complex vs Simple Map

Cathy K vs Ariel G

ROI Selection & Cell Columns

Limitations With Spatial Resolution • ROIs are dependent on

the generation of current from large Dipole Layers for detection.

• ROIs may contain millions of cells and network components. Which one and how do we select it and train it with Limited Resolution?

• Lets review the characteristics of these networks . . .

Axonal inputs and outputs to cell column networks: Oberlaender, Computational Neuroanatomy

The network reconstruction pipeline. a) Boundary surfaces defining cell type locations. b) Soma distribution inside one column. c) Cell type assignment. d) Dendrite reconstructions, colored by cell type. e) Thalamocortical axon reconstructions

The network reconstruction pipeline. a) Boundary surfaces defining cell type locations. b) Soma distribution inside one column. c) Cell type assignment. d) Dendrite reconstructions, colored by cell type. e) Thalamocortical axon reconstructions

Spatial Scale Limitations of EEG We Measure Primarily Dipole Layers - Nunez, 2006

• We cannot distinguish specific events at the microcolumn scale with EEG (pg182)

• We can measure dipole layers at the mesosource level.

• Dipole layer size is a function of the amount of synchronous activity, the size of the area of activity, and synchronous cell distribution in the dipole layer.

• Net potential at any sensor location is composed of contributions from all brain sources ie local, global and regional (pg61).

• Mesosource layer size must be at least 6cm squared in size to be recorded at the electrode (pg21)

• This area contains about 600,000 macrocolumns or 60,000,000 neurons forming a dipole layer.

• They must be synchronously active to produce recordable scalp potentials.

• Broca’s area = 12cmsq.

www.newmindmaps.com

Cells Assemblies: Temporal Scale Limitations

“The spatiotemporal orchestration of neurons”- Buzsaki

• Vary in size and distribution and overlap in terms of local, global and regional distribution.

• The same microcolumns may participate in regionally distributed cell assemblies and global networks that overlap.

• Cells may participate between minicolumns shared by macrocolumns in local cell assemblies that overlap. (Nunez, 39)

• Cell assemblies = momentary cohorts of cells networking together for 30ms or more.

• Synaptic action fields = the

number of excitatory and inhibitory synapses per unit volume -independent of participation in cell assemblies.

Frequency & Networks Resonance between Networks

• “frequency may code the channel of communication in neural circuits, and phase modulations may transmit information in specific channels (Bassett,2006).

• Frequency specific circuits. • Correlated oscillations in the

gamma frequencies is the substrate for temporal binding and also state-related changes in spatial configuration of brain (functional) networks. (Bassett, 2006). (Find theta arguement)

• Similar correlations between 2 and 73hz occur in the process.

Freeman: Network Spatial Characteristics

• “The most important insight is that the neural correlates of cognitive load are likely to be found in the large-scale, widely distributed, spatially coherent neural activities at the mesoscopic and macroscopic levels . . . (Freeman, 2009).”

• “It is apparent that networks and modules are functional entities with variable size and adaptive boundaries being redefined by the mesoscopic and macroscopic fields of brain activity with each new cognitive task and within tasks from each tenth of a second to the next” (Freeman: 4).

• “The network view, by contrast, considers a damaged region to be a node of a network supporting a cognitive function: loss of a network node may have a wide range of consequences. At one extreme, the function may be maintained by the network despite loss of the node. At the other extreme, functional silencing of the network may result from the damaged node, (Meehan & Bressler, 2012).”

It’s the Network not the focal abnormal ROI!

Graph Theory How Networks Connect

• Components of networks cannot yet be identified at the micro level.

• We are training at the meso level.

• We are training Scale Free Networks.

• Training major nodes from a network perspective is likely most effective.

• “. . . We are not generally justified in assuming that scalp EEG primarily records specific neural network activity, perhaps in contrast to hopeful views of some scientists.” (Nunez, 2006: 25)

Hubs and Connectors: Vertices vs Edges Scale Free Small Worlds Network Analysis

Cell columns connect in chains acting as nodes and act as “vertices”. Vertices connect through “edges” or axons connecting distant hubs & nodes.

How Do We Cope With Shifting Networks Issues?

• Identify average abnormal ROIs

• Identify major affiliated meta- networks

Major Networks

• The recent Brain Map Project has done a meta analysis of major networks and clearly identified 18 major functional networks (Meta-Networks) in the brain (Laird et al 2012).

• The majority of these networks are bilateral in nature.

• The majority correlate closely with the 10-20 system.

Bilateral Networks Laird et al, 2012

Network Functions ICN Brodmann Area 10/20 Function Interface

1 28, 34, 35, 36, 38 + Parahippocampal gyrus

Fp1-Fp2 Facial Emotional Discrimination

2 25, 10, 11, 12 + ACC Fpz, Fz Emotion & Reward

3 Bilateral BG & Thalamus Cz Hypothalamic Functions, Emotion

4 13, 16, 24 + Insula & Cingulate

T3-T4 Language, Valence, Pain, Go-NoGo

5 Midbrain C3-C4 Sensorimotor

6 6, 8, 9, SMA C3-C4 Timing, Movement, Visual Focus & Motion

7 46, 7 Mid Frontal Gyri F3-F4 O1-O2

Visual Spatial Reasoning, Counting, Calculation, Card Sort

7 & 4

8 Cerebellum + Central Region

C3-C4 Sensorimotor

9 5 Parietal P3-P4 Motor Learning, Drawing, Reaching

8 & 9

10 MT, MST, V5, 37, 39 Temp-Occipital Junction

T5-T6 Visual Emotional Process, Name Objects, Spatial Location

11 & 12 V1, V2, V3, 17, 18, 19 O1-O2 Visual Process

13 Medial prefrontal + Post Cingulate

Fpz-Pz T5-T6

DMN

14 Cerebellum O1-O2 Timing

15 44, 45, 22, 39, 40 Right FrontoParietal

F7-F8 T5-T6

Attention, memory, reasoning

16 41, 42 Transverse Temporal Gyri

T3-T4 Auditory

17 4, 3, 1, 2 Cerebellum, central region

T3-T4 Motor

18 44, 45, 22, 39, 40 Broca-Wernicke

F7-T3 F8-T4

Verbal, recall, encode, reading

19 Artifact

20 Artifact

Which Network Element(s) Is Defective? Limitations of Connectivity Analysis

• “Although the result of abnormal functional connectivity between two remote regions is integrative and comprehensive, it is difficult to draw a conclusion about which region is abnormal from such a study”

• “Amplitude of signal is more promising for diagnostic purposes.” (Jingming et al, 2007)

• Conclusion: Use Magnitude as primary evaluation tool to identify abnormal networks but include connectivity as a secondary measure.

Weighting The Activation of Networks To identify Greatest Deviance

• Spatial patterns of brain activity can have significantly greater sensitivity and specificity for detecting conscious and unconscious mental processes than activity in individual regions (Haynes & Rees, 2006; Soon et al, 2008).

• “The key point is that the contribution of a particular region to function is best appreciated in the context of other areas that are engaged (McIntosh, 2008).”

• Conclusions: Find networks affiliated with abnormal ROIs

Sample Meta-Network Pattern: How Networks Interface

• Memory related processing.HF

• Self-relevant mental simulations.MPFC

• Virtual Sensory Synthesis PCC

Buckner, 2008

Theta-Beta Network Relations “In light of this suggestion, we hypothesize that the functional significance of theta oscillations in PMC is related to a shared oscillatory dynamic with the medial temporal lobe (MTL) and other association cortices. The role of theta oscillations in coordinating ensemble activity in the hippocampus is well characterized (Buzsaki, 2002). Given the strong anatomical connections between PMC and MTL (Parvizi et al., 2006), and the clear involvement of PMC in episodic memory processing (Cabeza and St Jacques, 2007; Hassabis and Maguire, 2009; Schacter et al.” Foster et al 2012

Rabinovich et al, 2012

Other Rules of Contextual Dependency: Training a TBI Case

• “A region can contribute to a function, yet not be critical for its normal expression.”

• “Contextual dependency may change in different populations due to factors such as maturation, learning, brain damage, or disease.”

• Hubs have unique capacity for facilitating information integration.

• Damage to hub nodes in large-scale network simulations has the most dramatic effect on the integrative capacity of the remaining network*.

• Regional deficiencies are an expression of the state of the network. Focal deficiencies are part of a larger network.

Brodmann area 22 is most impacted by the dorsal lateral frontal network damage. McIntosh, 2008

Training Strategies For Networks

• NFB training may be different than other modes of intervention or treatment such as TMS.

• Train system based on best intervention points.

• Train based on clinical experience.

• Training interhemispheric with EEG mag appears to affect system more than intrahemsipheric.

• Train based on functional location compensation.

• Train based on frequency composition.

• Training based on frequency compensation

Mag Functions vs Coherence Functions

• Magnitude represents areas-node and hubs- that are dysregulated most frequently in response to stress and trauma.

• Coherence shows the adaptive compensatory response to abnormality. It represents how effectively the information is being transferred.

• Emma M 6 mosPre Post Z score 50 sess?

Seizure Disorder

How Do We Train?

• Network Based

• Bilateral

• Accounting for Compensatory Processes

• Monitoring Vertical & Horizontal Integration

Horizontal Integration

• A Bilateral Asymmetry Measure

• Looks at the relationship between magnitude asymmetry, and coherence between hemispheres.

• Is primarily related to emotional performance measures (Alpha Asym for Depression and Beta Asym for Anxiety.)

Research On Horizontal

Consequently, it has been suggested (e.g., Heller et al. 1997) that self reported trait anxiety (TA) indexes anxious apprehension or worry (i.e., verbal rumination about future events), which may be distinct from anxious arousal (i.e., symptoms of physiological arousal). In this and other subsequent studies, the former correlated with greater relative left frontal activity, whereas the latter induced right frontal or parietal activation (Heller et al. 1997; Mathersul et al. 2008; Metzger et al. 2004). (Avram et al, 2010)

According to the diathesis-stress hypothesis of frontal activation asymmetry, an affective style characterized by reduced relative right frontal activity is associated with altered aversive and withdrawal responses consistent with anxiety, whereas one characterized by reduced relative left frontal activity is associated with altered appetitive and approach responses consistent with depression (e.g., Coanand Allen 2004; Davidson 1992). (Avram et al, 2010).

Vertical Integration

• Looks at measures of cooperation in the triune brain.

• Is based primarily on magnitude measures (ratio of fast to slow wave activity.)

• Determines the inhibitory control of the cortex with respect to subcortical structure.

• Is mostly related to cognitive performance as a function of arousal measures.

Research On Vertical

Reduced SW activity in healthy awake subjects has been associated with increases in both behavioural inhibition and CSC-ct, whereas heightened SW activity is linked to increased behavioural activation and reduced CSC-ct. Together with the observation that FW activity does not significantly modulate CSC-ct, the findings illustrate that humans are in essence emotionally and not cognitively driven operating systems during relaxed wakefulness (Panksepp, 2003). (Shutter et al, 2005)

Several studies have provided evidence for the notion that the coupling between slow and fast frequency in the EEG spectrum indicates cortico-subcortical cross-talk (CSC-ct). In addition, findings for increased limbic activation due to reduced cortical inhibition have recently been acquired. (Shutter et al)

Recent publications in the International Journal of Psychophysiology on evolutionary accounts of human brain development have suggested phylogenetically distinct subcortical and cortical brain systems (Maclean, 1985) that relate to delta (1–3 Hz) and beta (13–30 Hz) oscillations in the EEG spectrum.

The introduced concept of brain-rate, representing the weighted mean frequency of the potential/power EEG spectrum, may serve as a preliminary indicator of general mental activation (mental arousal) level, similar to blood pressure, heart-rate and temperature used as standard preliminary indicators of corresponding general bodily activations. Characterizing the EEG spectrum Nada Pop-Jordanova, Jordan Pop-Jordanov, 2005.

Non Exclusive Domains

• Training Horizontal affects training vertical.

“Moreover, Knyazev and coworkers have repeatedly demonstrated this hypothesized relationship between stress-related indices of behavior (i.e., behavioral inhibition and anxiety) and enhanced subcortical–cortical cross-talk as indexed by brain oscillations in the EEG spectrum.” Shutter et, 2005

Emotional Activity alters arousal levels and activity in limbic and brain stem regions: consequently asymmetrical inter-hemispheric dynamics correspond to changes in cortical-sub-cortical dynamics.

Compensation Transcallosal Inhibitory Control

Compromised regions in one hemisphere are invaded by the contralateral homologous region and managed until pyramidal, supporting cells or network functions recover and begin activating again.

Pascual-Leon, 2005

Adaptive Response To Trauma Compromised regions or nodes increase and decrease connections to supportive networks depending on node valence in network constellations.

Alstott, 2009

Training Hemispheres Independently

• By training each hemisphere independently we can place the emphasis of the training on the interhemispheric transcallosal inhibitory mechanisms across the corpus collosum which must react to the inputs of both hemispheres. The complexity of this process requires that the brain determine the outcome dynamically.

Transcallosal Inhibitory Control

“The results of this pilot study support the notion that the overactivity of the unaffected hemisphere (ipsilateral hemisphere) may hinder hand function recovery, and neuromodulation can be an interventional tool to accelerate this recovery. The findings are consistent with results in normal subjects, where ipsilateral motor cortex activation on functional MRI during unilateral hand movements is indeed related primarily to interhemispheric interactions (Kobayashi et al. 2003), and disruption of the activity of one hemisphere reduces transcallosal inhibition to the contralateral hemisphere and can indeed improve ipsilateral motor function (Kobayashi et al. 2004).” Pg368 Pascual Leone, (2005)

Gomez & Teipel: Bilateral Coherence Coherence Contributions to Bilateral Training

“Coherence and SL have proven to be effective in discriminating MCI patients from controls subjects. Our study revealed that MCI subjects have lower connectivity values than controls in all frequency bands. Our findings support the notion that MCI involves a loss of functional connectivity. Moreover, significant statistical differences were found in the beta band with both measures (p < 0.05, Mann–Whitney U-test).” (Gomez, 2009)

Low coherence is a reliable measure of the “ integrity of neuronal fibers,” especially the functional interhemispheric connectivity (FIC). The FIC measure also provides a measure of midbrain, pons and cerebellar integration. Alpha coherence show the best correlations. Teipel notes that “interhemispheric coherence depends on the integrity of intracortical and subcortical fiber systems.” Indicating that interhemispheric coherence is also a proxy measure of intrahemispheric integrity. “Subcortical centers modulate interhemispheric coherence.” Teipel et al, 2009

“These results support the notion that spatial attention may be explained in terms of interhemispheric competition between subcortical and cortical structures; this competition may be asymmetrical.” Pascual Leone, 2000: pg3

Supportive Network Research

• Parallel information flow between hemispheres modulates and is in term modulated by information flow from posterior to anterior regions. Long delay loops feed back from frontal area to other regions in conscious processing.

Rabinovich et al, 2012

• There appear to be more interhemispheric dominant networks than intrahemispheric networks. Laird et al, 2012

Network Types and Training NFB Network Interaction Dynamics Result

In Two Phases of Training

• Four network layers that relate to different time domains from millisecond to days and from cell assemblies to architecture (Meehan & Bressler, 2012) appear to define two major phases of NFB training.

• 1. Acquisition reflects cell assembly process (Hebbian>LTP).

• 2. Consolidation reflects architectural changes (LTP>structural change).

• We choose frequencies to train as reflected in map static architecture .

• We adjust thresholds to affect change through dynamic cell assembly function.

Using Magnitude To Train Selective Measure of Activation

Sauseng & Klimesch (2008) citing previous research observe “that amplitude variations very selectively indicated cortical activation/deactivation in various sensory and cognitive modalities.”

Clinical experiments indicate that bilateral amplitude training is as effective and in some cases more effective than z-score using power, coherence, phase, and asymmetry. All crows are not black!

If coherence is an adaptive response to irregularities in network recruitment, then activation of nodes may have ascendance over connectivity in network dynamics.

Allowing Compensatory Response

• Plasticity is a critically important component of network re-organization.

• Some parameters need to go outside the standard deviation for others to adjust to normative levels.

• This process needs to be auto-correlative. It is a non-linear adaptive response that generates its own attractor states.

Review dynamic systems characteristics in adaptive response Show successive maps with reduced anterograde responses.

Gyorgy Buzsaki: Network Dynamics

• Network hierarchy is determined by computational solution- ie no real top and bottom solution.

• Function is highly distributive.

• Synaptic weighting emerges from auto-asssociative attractor networks.

• All circuits can sustain autonomous self-organizing activity.

Successive Maps Show Increasing Anterograde and Decreasing Retrograde Z score Changes

Stages of Adaptation & Consolidation

Concerns of Overtargeting and sLORETA Training Modalities

• Ignores network dynamics

• May undermine compensatory patterns.

• Assumes a rigid location function model.

• May result in regression of symptoms.

The Big Picture: Intra-Frequency Compensation Training one frequency influences another frequency through phase coding.

Meehan & Bressler, 2012

Beta Results from Gamma-High Beta Interaction

Beta is Cognitive

Indeed, evidence suggests that phase synchronization of oscillations in the beta frequency range is more important than that of gamma oscillations for the interregional binding that takes place in neurocognitive networks. The phase synchronization of beta oscillations between temporal and parietal cortices has been associated with long-distance binding in multimodal perception (von Stein et al., 1999). Furthermore, one simulation study found that activity in the gamma range did not appear to persist after driving input was removed, whereas a switch to beta oscillations allowed activity to persist in the absence of driving input; this persistent beta activity may represent a ‘trace’ of the input that serves as a kind of working memory (Kopell et al., 2011). In vitro studies suggest that the switch from gamma to beta activity occurs as an interaction between gamma oscillations in superficial neocortical layers andbeta2 (20–30 Hz) oscillations in deep layers, such that the period of the two frequencies concatenates to produce beta1 (14–20 Hz) oscillations (Roopun, 2008; Roopun et al., 2006). A temporal switch from gamma to beta oscillations has been observed in both human EEG (Haenschel et al., 2000) and intracranial recordings (TallonBaudry et al., 2001). Meehan & Bressler, 2012: pg8

Within vs Across Meta-Networks? Train Within Meta-Networks

• F7-F8 or P3-P4 Within Network • F7-P3 or F8-P4 Across Networks

• The Majority of cortical connections are short path connections dominated by “Rich Club Connections.” • Rich Club connections are connections that dominate

major network activity. • Rich Club are long path: Manage 77% communication • Rich Club contributions vs peripheral node

contribution? • Rich Club vs 18 networks: It is one of them

Individual Differences • Some superior adaptive patterns may contain

abnormal parameters. Peak performers have statistically non-normative aspects.

Justin Loftin: NASCAR

Sutton, Nick 2012. A MIXED-METHODS STUDY OF INCREASING PERFORMANCE AND ATHLETIC VALUE USING NEUROFEEDBACK, LIGHT THERAPY, AND LIFE COACHING

Training alpha down indirectly downtrains beta and high beta and induces stage 1 sleep- a natural progression of arousal stages in the brain.

Same Client Using Two Channel Bilateral Training

LH Beta Up, Alpha down, High Beta Down

RH Lo Beta up, Beta down, High Beta Down

Shift Target Fahad

Alpha is abnormally elevated and not responding to alpha inhibit in LH. Added beta inhibit in RH at 12 min and overall alpha began to drop.

Sarah

Theta is significantly higher in the RH and not responding to training.during first 12 min. Began a new session and added beta inhibit to RH and theta decreases.

Coherence Decrease With Beta Magnitude Inhibit

Downtrain Beta Only

Downtrain Theta Only Acquisiton & Consolidation Phases

Pre Post Seizure 40 sessions Acquisition Phase Consolidation Phase

Normal Asym Train left vs Zscore Train Right

Key References

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• • Avram J., BaltesF.R., Miclea, M., Miu A. C. (2010). Frontal

EEG activation asymmetry reflects cognitive biasesin anxiety: Evidence from an emotional face stroop task. Appl Psychophysiol Biofeedback 35, 285–292 doi: 10.1007/s10484-010-9138-6

• • Bassettt, D.S., Meyer-Lindenberg, Andreas, Archard, Sopjie,

Duke, Thomas, Bullmore, Edward.(2006). Adaptive reconfiguration of fractal small-world human brain functional networks. PNAS, 103(51). doi:10.1073/pnas.0606005103

• • Buckner, R. L., Andrews-Hanna, J. R., Schacter, D. L. (2008).

The brain’s default network: Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences 1124, 1-38.

• Buzsaki, G. (2006). Rhythms of the brain. New York: Oxford University Press.

• Freeman, W. J., Ahlfors, S. P., Menon, V. (2009). Combining fMRI with EEG and MEG in order to relate patterns of brain activity to cognition. International Journal of Psychophysiology. doi:10.1016/j,ijpsych0.2008.12.019. In Press.

• Gomez, C., Stam, C.J., Hornero, R., Fern´andez, A.Z., Maestu,

F. (2009). Disturbed beta band functional connectivity in patients with mild cognitive impairment: An MEG Study. IEEE Transactions on biomedical engineering 56 (6).

• • Laird,Angela R., P. Mickle Fox, Simon B. Eickhoff , Jessica A.

Turner, Kimberly L. Ray1, D. Reese McKay,David C. Glahn5, Christian F. Beckmann,Stephen M. Smith, and Peter T. Fox. Behavioral Interpretations of Intrinsic Connectivity Networks. Journal of Cognitive Neuroscience X:Y, pp. 1–16 Massachusetts Institute of Technology. In Press.

• • Nada Pop-Jordanova1, Jordan Pop-Jordanov (2005).

Spectrum-weighted EEG frequency “Brain-Rate” as a quantitative indicator of mental arousal. Contributions, Sec. Biol. Med. Sci XXVI/2, 35–42.

• • Timothy P. Meehan, Steven L. Bressler (2012).

Neurocognitive networks: Findings, models, and theory. Neuroscience and Biobehavioral Reviews. In Press.

• Michelea, F. di., Prichep, L., John, E.R., Chabot, R.J. (2005). The neurophysiology of attention-deficit/hyperactivity disorder. International Journal of Psychophysiology, 58, 81- 93.

• McIntosh, A. R., Korostil, M. (2008). Interpretation of neuroimaging data based on network concepts. Brain Imaging and Behavior 2, 264-269. doi 10.1007/s11682-008-9031-6.

References Continued

• Nunez, P. L., Srinivasan, R.(2006). Electric fields of the brain: The neurophysics of EEG. (2ng Ed.) New York: Oxford University Press.

• Nishikawa, T. & Motter, A.E. 2011. Discovering Network Structure Beyond Communities. Sci. Rep. 1, 151; DOI:10.1038/srep00151.

• Pascual-Leone, A., Amedi, A., Fregni, F., Merabet, L.B. (2005). The plastic human brain cortex. Annual Review of Neuroscience 28, 377-401.

• • M.I. Rabinovich et al. Information flow dynamics in the brain.

Physics of Life Reviews 9 (2012) 51–73. • • Schutter, D., Leitner C., J. Kenemans L.J., van Honk, J. (2006).

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• • Teipal, S.J., Pogarell, O., Meindl, T., Dietrich, O., Sydykova, D.,

Hunklinger, U., Georgii, B., et.al. (2009). Regional Networks Underlying Interhemispheric Connectivity: An EEG and DTI Study in Healthy Ageing and Amnestic Mild Cognitive Impairment. Human Brainmapping, 30, 2098-2119.