Neocortical Dynamics: Implications for
Understanding the Role of Neurofeedback and
Related Techniques for the Enhancement of AttentionJoel F. Lubar1
For nearly 25 years, EEG biofeedback (neurofeedback) has been utilized in research and
clinical settings for the treatment and investigation of a number of disorders ranging from
attention deficit hyperactivity disorder to seizure disorders as well as many other established
and investigational applications. Until recently, mechanisms underlying the generation and
origins of EEG have been poorfy understood but now are beginning to become much man;
clarified Now it is important to combine the information gathered on the genesis of EEG
and neocortical dynamics with the findings from neurofeedback investigations. This will help
us to develop models of how neurofeedback might operate in producing the changes in EEG
and in clinical symptomatology. We know that the cortex operates in terms of resonant loops
between neocortical columns of cells known as local, regional, and global resonances. These
resonances determine the specific EEG frequencies and are often activated by groups of cells
in the thalamus known as pacemakers. There are complex excitatory and inhibitory
interactions within the cortex and between the cortex and the thalamus that allow these loops
to operate and provide the basis for learning. Neurofeedback is a technique for modifying
these resonant loops, and hence, modifying the neurophysiological and neurological basis for
learning and for the management of a number of neurologically based disorders. This paper
provides an introduction to understanding EEC and neocortical dynamics and how these
concepts can be used to explain the results of neurofeedback training and other interventions
particularly in the context of understanding attentive mechanisms and for the management
of attention deficit/hyperactivity disorders.
KEY WORDS: words: neocortical dynamics; neurofeedback; attention; ADHD; ADD.
Sunday, February 10, 2008
An Event-Related fMRI Study of
Visual and Auditory Oddball Tasks
Kent A. Kiehl1, Kristin R. Laurens2, Timothy L. Duty3,
Bruce B. Forster4, and Peter F. Liddle5
1Institute of Living, Department of Psychiatry, Yale University School of Medicine
Departments of 2Psychiatry, 3Physics, and 4Radiology, University of British Columbia
5Department of Psychiatry, University of Nottingham
Accepted for publication: 12 May 2001
Keywords: Event-related fMRI, oddball, P300, P3, novel, target, visual stimuli
Journal of Psychophysiology 15 (2001) 221–240 © 2001 Federation of European Psychophysiology Societies
Abstract Whole brain event-related functional magnetic resonance imaging (fMRI) techniques were employed to elucidate the
cerebral sites involved in processing rare target and novel visual stimuli during an oddball discrimination task. The analyses of the
hemodynamic response to the visual target stimuli revealed a distributed network of neural sources in anterior and posterior cingulate,
inferior and middle frontal gyrus, bilateral parietal lobules, anterior superior temporal gyrus, amygdala, and thalamus. The analyses
of the hemodynamic response for the visual novel stimuli revealed an extensive network of neural activations in occipital lobes and
posterior temporal lobes, bilateral parietal lobules, and lateral frontal cortex. The hemodynamic response associated with processing
target and novel stimuli in the visual modalitywere also comparedwith data froman analogous study in the auditory modality (Kiehl
et al., 2001). Similar patterns of activation were observed for target and novel stimuli in both modalities, but there were some
significant differences. The results support the hypothesis that target detection and novelty processing are associated with neural
activation in widespread neural areas, suggesting that the brain seems to adopt a strategy of activating many potentially useful brain
regions despite the low probability that these brain regions are necessary for task performance.
Visual and Auditory Oddball Tasks
Kent A. Kiehl1, Kristin R. Laurens2, Timothy L. Duty3,
Bruce B. Forster4, and Peter F. Liddle5
1Institute of Living, Department of Psychiatry, Yale University School of Medicine
Departments of 2Psychiatry, 3Physics, and 4Radiology, University of British Columbia
5Department of Psychiatry, University of Nottingham
Accepted for publication: 12 May 2001
Keywords: Event-related fMRI, oddball, P300, P3, novel, target, visual stimuli
Journal of Psychophysiology 15 (2001) 221–240 © 2001 Federation of European Psychophysiology Societies
Abstract Whole brain event-related functional magnetic resonance imaging (fMRI) techniques were employed to elucidate the
cerebral sites involved in processing rare target and novel visual stimuli during an oddball discrimination task. The analyses of the
hemodynamic response to the visual target stimuli revealed a distributed network of neural sources in anterior and posterior cingulate,
inferior and middle frontal gyrus, bilateral parietal lobules, anterior superior temporal gyrus, amygdala, and thalamus. The analyses
of the hemodynamic response for the visual novel stimuli revealed an extensive network of neural activations in occipital lobes and
posterior temporal lobes, bilateral parietal lobules, and lateral frontal cortex. The hemodynamic response associated with processing
target and novel stimuli in the visual modalitywere also comparedwith data froman analogous study in the auditory modality (Kiehl
et al., 2001). Similar patterns of activation were observed for target and novel stimuli in both modalities, but there were some
significant differences. The results support the hypothesis that target detection and novelty processing are associated with neural
activation in widespread neural areas, suggesting that the brain seems to adopt a strategy of activating many potentially useful brain
regions despite the low probability that these brain regions are necessary for task performance.
EEG biofeedback: physiological behavior modification.
Sterman MB.
The author reviews the use of operant conditioning to alter electroencephalogram (EEG) patterns. A discrete rhythmic EEG pattern directly related to modulation of motor patterns (sensorimotor rhythm, SMR) was brought under voluntary control in the cat. This technique was modified for use in epileptic human volunteers in order to reduce motor seizures. The use of a newer experimental design and its successful application in one subject is described.
PMID: 7301228 [PubMed - indexed for MEDLINE]
www.pubmed.com
Sterman MB.
The author reviews the use of operant conditioning to alter electroencephalogram (EEG) patterns. A discrete rhythmic EEG pattern directly related to modulation of motor patterns (sensorimotor rhythm, SMR) was brought under voluntary control in the cat. This technique was modified for use in epileptic human volunteers in order to reduce motor seizures. The use of a newer experimental design and its successful application in one subject is described.
PMID: 7301228 [PubMed - indexed for MEDLINE]
www.pubmed.com
Brain-computer communication: self-regulation of slow cortical potentials for verbal communication.
Kübler A, Neumann N, Kaiser J, Kotchoubey B, Hinterberger T, Birbaumer NP.
Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany. andrea.kuebler@uni-tuebingen.de
OBJECTIVE: To test a training procedure designed to enable severely paralyzed patients to communicate by means of self-regulation of slow cortical potentials. DESIGN: Application of the Thought Translation Device to evaluate the procedure in patients with late-stage amyotrophic lateral sclerosis (ALS). SETTING: Training sessions in the patients' homes. PARTICIPANTS: Two male patients with late-stage ALS. INTERVENTIONS: Patients learned voluntary control of their slow cortical potentials by means of an interface between the brain and a computer. Training was based on visual feedback of slow cortical potentials shifts and operant learning principles. The learning process was divided into small steps of increasing difficulty. MAIN OUTCOME MEASURES: Accuracy of self-control of slow cortical potentials (percentage of correct responses). Learning progress calculated as a function of training session. RESULTS: Within 3 to 8 weeks, both patients learned to self-regulate their slow cortical potentials and to use this skill to select letters or words in the Language Support Program. CONCLUSIONS: This training schedule is the first to enable severely paralyzed patients to communicate without any voluntary muscle control by using self-regulation of an electroencephalogram potential only. The protocol could be a model for training patients in other brain-computer interface techniques. Copyright 2001 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation.
www.pubmed.com
Kübler A, Neumann N, Kaiser J, Kotchoubey B, Hinterberger T, Birbaumer NP.
Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany. andrea.kuebler@uni-tuebingen.de
OBJECTIVE: To test a training procedure designed to enable severely paralyzed patients to communicate by means of self-regulation of slow cortical potentials. DESIGN: Application of the Thought Translation Device to evaluate the procedure in patients with late-stage amyotrophic lateral sclerosis (ALS). SETTING: Training sessions in the patients' homes. PARTICIPANTS: Two male patients with late-stage ALS. INTERVENTIONS: Patients learned voluntary control of their slow cortical potentials by means of an interface between the brain and a computer. Training was based on visual feedback of slow cortical potentials shifts and operant learning principles. The learning process was divided into small steps of increasing difficulty. MAIN OUTCOME MEASURES: Accuracy of self-control of slow cortical potentials (percentage of correct responses). Learning progress calculated as a function of training session. RESULTS: Within 3 to 8 weeks, both patients learned to self-regulate their slow cortical potentials and to use this skill to select letters or words in the Language Support Program. CONCLUSIONS: This training schedule is the first to enable severely paralyzed patients to communicate without any voluntary muscle control by using self-regulation of an electroencephalogram potential only. The protocol could be a model for training patients in other brain-computer interface techniques. Copyright 2001 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation.
www.pubmed.com
Mathematical model of a learning process by biofeedback
[Article in French]
Gallego J, Laurenti-Lions L.
The aim of Biofeedback is to teach a subject to control some of his biological functions. This experimental method was numerous therapeutic applications. General laws of these particular learning processes have not yet been well understood. Mathematical models can be used in order to make evident these laws can lead to an optimization of the learning processes. The stochastic model presented here underlines the shortcomings of certain experimental procedures. Although it has been performed from one of our particular experiments, it can be applied to analogous experiments.
www.pubmed.com
[Article in French]
Gallego J, Laurenti-Lions L.
The aim of Biofeedback is to teach a subject to control some of his biological functions. This experimental method was numerous therapeutic applications. General laws of these particular learning processes have not yet been well understood. Mathematical models can be used in order to make evident these laws can lead to an optimization of the learning processes. The stochastic model presented here underlines the shortcomings of certain experimental procedures. Although it has been performed from one of our particular experiments, it can be applied to analogous experiments.
www.pubmed.com
A MATHEMATICAL MODEL OF BIOFEEDBACK
AND ITS RELATION TO NEURAL ACTIVITY
C. Nishimura*, L-Q. Wang**, A. Nagase*, K. Terada* and Y. Miyamoto***
*Toho University School of Medicine, Tokyo, Japan
**Research Center for Advanced Technologies, Tokyo Denki University, Tokyo, Japan
***Department of Mechanical Engineering, Osaka Sangyo University, Osaka, Japan
nishimuc@med.toho-u.ac.jp
Abstract: Biofeedback is an acquisition technique of self-regulation ability of a biological function, of which we are normally unaware, through a series of training aided by an additional outer feedback pathway. We proposed a mathematical model of biofeedback in which a learning system on the conscious level learns characteristics of a subconscious regulation system corresponding to the biological function. When the learning converges, the learning system itself becomes an inverse system of the regulation system. Then, if a regulation command is put to the learning system on the conscious level, it drives the regulation system strictly following the command without the outer feedback pathway, which enables voluntary control of the biological function. Based on the model, we measured neural activities relating to a phenomenon of state alteration of consciousness in the course of training in search for an appropriate connection between the learning system and the target regulation system. The situation was modelled as an interpretational change in depth of an ambiguous stereogram. Functional MRI measurement revealed neural activities in bilateral prefrontal area. The results show an important role of neural activities in the prefrontal area in connecting the learning system with the appropriate subconscious regulation system.
AND ITS RELATION TO NEURAL ACTIVITY
C. Nishimura*, L-Q. Wang**, A. Nagase*, K. Terada* and Y. Miyamoto***
*Toho University School of Medicine, Tokyo, Japan
**Research Center for Advanced Technologies, Tokyo Denki University, Tokyo, Japan
***Department of Mechanical Engineering, Osaka Sangyo University, Osaka, Japan
nishimuc@med.toho-u.ac.jp
Abstract: Biofeedback is an acquisition technique of self-regulation ability of a biological function, of which we are normally unaware, through a series of training aided by an additional outer feedback pathway. We proposed a mathematical model of biofeedback in which a learning system on the conscious level learns characteristics of a subconscious regulation system corresponding to the biological function. When the learning converges, the learning system itself becomes an inverse system of the regulation system. Then, if a regulation command is put to the learning system on the conscious level, it drives the regulation system strictly following the command without the outer feedback pathway, which enables voluntary control of the biological function. Based on the model, we measured neural activities relating to a phenomenon of state alteration of consciousness in the course of training in search for an appropriate connection between the learning system and the target regulation system. The situation was modelled as an interpretational change in depth of an ambiguous stereogram. Functional MRI measurement revealed neural activities in bilateral prefrontal area. The results show an important role of neural activities in the prefrontal area in connecting the learning system with the appropriate subconscious regulation system.
A learning model of autonomic function
in biofeedback
Chiaki Nishimura a,⁎, Li-Qun Wang b, Aki Nagase a,
Kazuko Terada a, Yoshifumi Miyamoto c,
Hisayuki Tsukuma a, Masuo Muro a
a Toho University School of Medicine, Japan
b Research Center for Advanced Technologies, Tokyo Denki University, Japan
c Department of Mechanical Engineering, Osaka Sangyo University, Japan
Abstract. Biofeedback is an acquisition technique of self-regulation ability of an autonomic
function, of which we are normally unaware, through a series of training aided by an additional outer
feedback pathway. We proposed a mathematical model of biofeedback in which a learning system on
the conscious level learns characteristics of a subconscious regulation system corresponding to the
biological function. When the learning converges, the learning system itself becomes an inverse
system of the regulation system. Then, if a regulation command is put to the learning system on the
conscious level, it drives the regulation system strictly following the command without the outer
feedback pathway, which enables voluntary control of the biological function. © 2007 Elsevier B.V.
All rights reserved.
Keywords: Biofeedback; Learning; Mathematical model; Autonomic function; Self-regulation
www.elsevier.com
in biofeedback
Chiaki Nishimura a,⁎, Li-Qun Wang b, Aki Nagase a,
Kazuko Terada a, Yoshifumi Miyamoto c,
Hisayuki Tsukuma a, Masuo Muro a
a Toho University School of Medicine, Japan
b Research Center for Advanced Technologies, Tokyo Denki University, Japan
c Department of Mechanical Engineering, Osaka Sangyo University, Japan
Abstract. Biofeedback is an acquisition technique of self-regulation ability of an autonomic
function, of which we are normally unaware, through a series of training aided by an additional outer
feedback pathway. We proposed a mathematical model of biofeedback in which a learning system on
the conscious level learns characteristics of a subconscious regulation system corresponding to the
biological function. When the learning converges, the learning system itself becomes an inverse
system of the regulation system. Then, if a regulation command is put to the learning system on the
conscious level, it drives the regulation system strictly following the command without the outer
feedback pathway, which enables voluntary control of the biological function. © 2007 Elsevier B.V.
All rights reserved.
Keywords: Biofeedback; Learning; Mathematical model; Autonomic function; Self-regulation
www.elsevier.com
Sunday, February 3, 2008
learning model of autonomic function
in biofeedback
Chiaki Nishimura a,⁎, Li-Qun Wang b, Aki Nagase a,
Kazuko Terada a, Yoshifumi Miyamoto c,
Hisayuki Tsukuma a, Masuo Muro a
a Toho University School of Medicine, Japan
b Research Center for Advanced Technologies, Tokyo Denki University, Japan
c Department of Mechanical Engineering, Osaka Sangyo University, Japan
Abstract. Biofeedback is an acquisition technique of self-regulation ability of an autonomic
function, of which we are normally unaware, through a series of training aided by an additional outer
feedback pathway. We proposed a mathematical model of biofeedback in which a learning system on
the conscious level learns characteristics of a subconscious regulation system corresponding to the
biological function. When the learning converges, the learning system itself becomes an inverse
system of the regulation system. Then, if a regulation command is put to the learning system on the
conscious level, it drives the regulation system strictly following the command without the outer
feedback pathway, which enables voluntary control of the biological function. © 2007 Elsevier B.V.
All rights reserved.
Keywords: Biofeedback; Learning; Mathematical model; Autonomic function; Self-regulation
www.elsevier.com
in biofeedback
Chiaki Nishimura a,⁎, Li-Qun Wang b, Aki Nagase a,
Kazuko Terada a, Yoshifumi Miyamoto c,
Hisayuki Tsukuma a, Masuo Muro a
a Toho University School of Medicine, Japan
b Research Center for Advanced Technologies, Tokyo Denki University, Japan
c Department of Mechanical Engineering, Osaka Sangyo University, Japan
Abstract. Biofeedback is an acquisition technique of self-regulation ability of an autonomic
function, of which we are normally unaware, through a series of training aided by an additional outer
feedback pathway. We proposed a mathematical model of biofeedback in which a learning system on
the conscious level learns characteristics of a subconscious regulation system corresponding to the
biological function. When the learning converges, the learning system itself becomes an inverse
system of the regulation system. Then, if a regulation command is put to the learning system on the
conscious level, it drives the regulation system strictly following the command without the outer
feedback pathway, which enables voluntary control of the biological function. © 2007 Elsevier B.V.
All rights reserved.
Keywords: Biofeedback; Learning; Mathematical model; Autonomic function; Self-regulation
www.elsevier.com
A MATHEMATICAL MODEL OF BIOFEEDBACK
AND ITS RELATION TO NEURAL ACTIVITY
C. Nishimura*, L-Q. Wang**, A. Nagase*, K. Terada* and Y. Miyamoto***
*Toho University School of Medicine, Tokyo, Japan
**Research Center for Advanced Technologies, Tokyo Denki University, Tokyo, Japan
***Department of Mechanical Engineering, Osaka Sangyo University, Osaka, Japan
nishimuc@med.toho-u.ac.jp
Abstract: Biofeedback is an acquisition technique of self-regulation ability of a biological function, of which we are normally unaware, through a series of training aided by an additional outer feedback pathway. We proposed a mathematical model of biofeedback in which a learning system on the conscious level learns characteristics of a subconscious regulation system corresponding to the biological function. When the learning converges, the learning system itself becomes an inverse system of the regulation system. Then, if a regulation command is put to the learning system on the conscious level, it drives the regulation system strictly following the command without the outer feedback pathway, which enables voluntary control of the biological function. Based on the model, we measured neural activities relating to a phenomenon of state alteration of consciousness in the course of training in search for an appropriate connection between the learning system and the target regulation system. The situation was modelled as an interpretational change in depth of an ambiguous stereogram. Functional MRI measurement revealed neural activities in bilateral prefrontal area. The results show an important role of neural activities in the prefrontal area in connecting the learning system with the appropriate subconscious regulation system.
www.ieee.com
AND ITS RELATION TO NEURAL ACTIVITY
C. Nishimura*, L-Q. Wang**, A. Nagase*, K. Terada* and Y. Miyamoto***
*Toho University School of Medicine, Tokyo, Japan
**Research Center for Advanced Technologies, Tokyo Denki University, Tokyo, Japan
***Department of Mechanical Engineering, Osaka Sangyo University, Osaka, Japan
nishimuc@med.toho-u.ac.jp
Abstract: Biofeedback is an acquisition technique of self-regulation ability of a biological function, of which we are normally unaware, through a series of training aided by an additional outer feedback pathway. We proposed a mathematical model of biofeedback in which a learning system on the conscious level learns characteristics of a subconscious regulation system corresponding to the biological function. When the learning converges, the learning system itself becomes an inverse system of the regulation system. Then, if a regulation command is put to the learning system on the conscious level, it drives the regulation system strictly following the command without the outer feedback pathway, which enables voluntary control of the biological function. Based on the model, we measured neural activities relating to a phenomenon of state alteration of consciousness in the course of training in search for an appropriate connection between the learning system and the target regulation system. The situation was modelled as an interpretational change in depth of an ambiguous stereogram. Functional MRI measurement revealed neural activities in bilateral prefrontal area. The results show an important role of neural activities in the prefrontal area in connecting the learning system with the appropriate subconscious regulation system.
www.ieee.com
Biofeedback systems architecture.
Paolini F, Bosetto A.
Hospal-Dasco SpA, Medolla, Italy. francesco.paolini@gambro.com
The capability for a dialysis machine to use a measurement of the patient's status to automatically tune the dialysis session on-line is commonly addressed by physicians and bioengineers working in the hemodialysis field as "biofeedback." This paper presents the basics of mathematical modeling and control theory normally used in bioengineering, together with some advanced techniques, such as adaptive and multi-input/multi-output control systems. The architectural requirements for implementing biofeedback techniques in renal replacement therapy are then discussed, with due attention paid to the safety aspects, which play a central role in machines hosting such new techniques as well as their therapeutic mission. Finally, the blood volume tracking system, which is aimed at performing the intradialytic water removal, while maintaining a balance inside the body fluids compartments and thus preserving cardiovascular stability, is used as a paradigmatic example of such a class of advanced techniques. The significant results shown by the blood-volume-controlled treatments during a multicenter study focused on its clinical application (30% reduction of intradialysis collapses, 13% reduction of interdialysis symptoms) indicate the technical feasibility and the remarkable benefits of such systems, which get closer to a structurally complete artificial kidney.
www.pubmed.gov
Paolini F, Bosetto A.
Hospal-Dasco SpA, Medolla, Italy. francesco.paolini@gambro.com
The capability for a dialysis machine to use a measurement of the patient's status to automatically tune the dialysis session on-line is commonly addressed by physicians and bioengineers working in the hemodialysis field as "biofeedback." This paper presents the basics of mathematical modeling and control theory normally used in bioengineering, together with some advanced techniques, such as adaptive and multi-input/multi-output control systems. The architectural requirements for implementing biofeedback techniques in renal replacement therapy are then discussed, with due attention paid to the safety aspects, which play a central role in machines hosting such new techniques as well as their therapeutic mission. Finally, the blood volume tracking system, which is aimed at performing the intradialytic water removal, while maintaining a balance inside the body fluids compartments and thus preserving cardiovascular stability, is used as a paradigmatic example of such a class of advanced techniques. The significant results shown by the blood-volume-controlled treatments during a multicenter study focused on its clinical application (30% reduction of intradialysis collapses, 13% reduction of interdialysis symptoms) indicate the technical feasibility and the remarkable benefits of such systems, which get closer to a structurally complete artificial kidney.
www.pubmed.gov
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