Electroencephalography (EEG) Biofeedback Training to Improve Executive Functioning and Memory in Adults With a Dementing Illness (QMFFTD)
|Dementia||Behavioral: HEG Neurofeedback Behavioral: EEG Driven Photic Stimulation Behavioral: Peak Alpha Neurofeedback||Phase 2|
|Study Design:||Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)
Primary Purpose: Treatment
|Official Title:||Pilot Study of EEG and Cerebral Blood Flow Biofeedback Training in Remediating Cognitive and Behavioral Deficits in Adults With a Dementing Illness.|
- Delis-Kaplan Executive Function System, Behavior Rating Inventory of Executive Function- Adult Version, Integrated Visual and Auditory Continuous Performance Test, Symptom Checklist 90R [ Time Frame: Within two weeks of last session ]
- EEG amplitude [ Time Frame: within two weeks of last session ]decreased slow wave amplitudes from 1-4hz
|Study Start Date:||June 2007|
|Study Completion Date:||June 2008|
|Primary Completion Date:||June 2008 (Final data collection date for primary outcome measure)|
Experimental: HEG only
This group receives 30 sessions of infrared cerebral blood flow (hemoencephalographic) neurofeedback.
Behavioral: HEG Neurofeedback
Detects and displays real time variation in blood flow in outermost 1.5 cm of frontal cerebral cortex.
Other Name: Pendant HEG with Bioexplorer Software
A Pendant EEG with Bioexplorer software detects and displays the dominant alpha frequency in real time, rewarding faster frequencies.
Behavioral: EEG Driven Photic Stimulation
A Brainmaster EEG monitors the variation brain's dominant frequency in real time, while a Procyon photic stimulation device provides visual stimulation at 1/2 Hz faster, encouraging or "entraining" a faster frequency. In half of the neurofeedback arm, EEG Driven Photic Stimulation will be provided during neurofeedback displaying dominant frequency and rewarding faster frequencies.Behavioral: Peak Alpha Neurofeedback
A Pendant EEG with Bioexplorer software detects and displays the dominant alpha frequency in real time, rewarding faster frequencies.
No Intervention: Control
A randomly selected half of participants waits 15 weeks for the other half to complete treatment, and are then reassessed, serving as controls. They then receive the same treatment as the experimental group.
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Objectives and Significance
- What is (are) the primary goal(s) of the study The purpose of this study is to evaluate the efficacy of biofeedback training to increase regional cerebral blood flow (CBF) and peak alpha frequency (PAF) on the cognitive and behavioral symptoms of frontotemporal dementia (FTD). We will also measure the effects of EEG driven photostimulation during PAF neurofeedback on learning of higher PAF states and related clinical outcomes.
- What is (are) the secondary goal(s) of the study A diversity of clinical, neuropsychological, and physiological variables will be collected in this study. For instance, a full 19-channel quantitative EEG (QEEG) assessment will be performed at intake. The relationship between EEG power and coherence and the symptoms of this disorder could potentially provide an important contribution to the literature on the usefulness of QEEG for differential diagnosis.
An additional goal of the study is to contribute to the understanding of how neurofeedback works. For instance, we will take measurements to determine if learning to control the PAF involves awareness of some internal subjective state related to the PAF. Also, the effect of the reward signal on a brainwave called the P300 may show the importance of having the reward tone sounding the majority of the time during neurofeedback sessions. Finally, we will measure whether blood flow changes during EEG biofeedback and whether the EEG is affected by blood flow biofeedback.
We propose several measurements that will contribute to an understanding of the mechanism of action of neurofeedback.
Frontotemporal Dementia and Cerebral Hyperperfusion Single photon emission computed tomography (SPECT) studies have shown cerebral blood flow to be significantly reduced in the frontal and temporal regions in FTD patients (Miller et al., 1997; Read et al., 1995). The anatomical distribution of reduced CBF corresponds to the pattern of neuropsychological deficits (McMurtray et al., 2006).
Not surprisingly, magnetic resonance imaging (MRI) and computed tomography (CT) in FTD patients shows atrophy in the frontal and temporal regions (Mendez et al., 1996; Neary and Snowden, 1996). However, Spilt et al. (2005) hypothesized that neurodegeneration and dementia are largely secondary to pathologies of cerebral blood flow. When compared to elderly controls with optimal cognitive function, patients with DSM-IV dementia did not differ significantly from elderly controls with respect to the number of cerebral infarctions. Demented patients showed significantly more white matter lesions (p=.028) and cerebrospinal fluid (CSF; p=.016), but a reduction in cerebral blood flow had the largest effect size (p<.001). An attempt to build a logistic regression model showed that no significant residual variance could be explained after cerebral blood flow was included in the model.
Another argument for the central role of blood flow in dementia is that Alzheimer's patients with brain damage (regions of MRI signal hyperintensity) have increased oxygen extraction per mL/min. That is, blood supply rather than demand seems to be the problem. Oxygen extraction would be expected to be unaltered if reduced blood flow were secondary to tissue damage (Spilt et al., 2005; Yamaji et al, 1997).
Positron emission tomography (PET) imaging in FTD patients reveals reduced glucose metabolism in the frontal and anterior temporal lobes, but also in the cingulate gyrus, insula, uncus, and subcortical structures (Jeong et al., 2005; Garraux et al., 1999; Ishi et al., 1998). Grimmer et al. (2003) performed a longitudinal study on ten patients diagnosed with FTD. At the initial assessment, FTD patients had reduced metabolic activity compared to controls in frontal cortical areas, the caudate nuclei, and the thalami. On a 1-2 year follow-up, significant progression of the original deficits was observed in the orbitofrontal cortex and the subcortical structures.
Given the substantial evidence linking dementia and FTD in particular to reduced cerebral blood flow, we hypothesize that training FTD patients to increase cerebral blood flow will alleviate FTD symptoms and slow the progress of the disease.
Recent studies have suggested that individuals can learn to increase CBF through biofeedback. Yoo et al. (2006) showed that participants given feedback of fMRI activity of the auditory cortex while listening to music were able to significantly increase the mean blood oxygenation as well as the number of significant voxels. Another study (deCharms et al., 2005) trained participants to change fMRI activity in the rostral anterior cingulate gyrus (RACG), a region implicated in pain perception. Control conditions included sham feedback or feedback from a different brain region. When a noxious thermal stimulus was applied, participants had decreased pain sensation when trained to decrease RACG activity and increased pain sensation when trained to increase RACG activity. In another phase of the study, eight chronic pain patients reported decreased pain after down-training fMRI in the same region.
fMRI costs more than $1000 per session, which places this form of therapy beyond the reach of most patients. However, it is possible to provide CBF neurofeedback for the outermost 1.5 cm of cerebral cortex with a relatively inexpensive device that uses the refractive properties of oxygenated hemogoblin to red and infrared light (Toomim et al., 2004). A light source is attached to the scalp (typically on the forehead) with a headband, 3 cm away from an infrared sensor, which detects the relative absorption by oxygenated blood. This procedure is known as hemoencephalography or HEG. Toomim et al. (2004) showed that ten sessions improved impulsivity scores on the Test Of Variables of Attention (TOVA) in 28 patients of diverse psychopathology. Carmen (2004) provided frontal HEG to 100 migraine patients, and found that 90% of those who completed at least six sessions reported significant improvement in migraine symptoms. In a single case study, Mize (2004) reported that a child with ADHD showed significant improvement on the IVA, which improvement persisting into the 18-month follow-up.
Frontotemporal Dementia and Peak Alpha Frequency
The PAF in health adults has an average of 10-11 Hz. Higher PAF is associated with higher memory performance (Klimesch, 1997), reading ability (Suldo, 2000), vocabulary, and response control (Angelakis et al., 2004a). After a series of cognitive tasks, PAF was reduced in traumatic brain injury patients compared to normal controls, but only weakly or nonsignificantly reduced compared to controls during the task or the baseline conditions. Thus, Angelakis et al. (2004b) argued that PAF is both a trait and a state marker of cognitive preparedness. Passant et al (2005), Chan et al. (2004) and Yenner et al. (1996) all observed a reduction in PAF in FTD patients.
We hypothesize that EEG biofeedback rewarding higher PAF will result in an improvement in symptoms in FTD patients. In EEG biofeedback or neurofeedback, an individual's real-time EEG is presented continuously as a visual or auditory signal, and desired variations are rewarded. A recent double-blind controlled study (Angelakis et al., 2007) showed that neurofeedback rewarding increased PAF improved cognitive processing speed and executive function in normal elderly adults.
The efficacy of neurofeedback as a therapy has been demonstrated for attention deficit hyperactivity disorder (ADHD), epilepsy, anxiety, and addictive disorders. Other disorders such as schizophrenia, depression, learning disabilities (LD), and traumatic brain injury are under investigation as candidates for neurofeedback therapy (Monastra, 2003).
Frontotemporal Dementia and EEG-Driven AVS
Like EEG neurofeedback, EEG-dependent auditory and visual stimulation (AVS), has showed promise for improving cognitive function by modifying the PAF. A substantial body of research has demonstrated that rhythmic AVS can induce EEG rhythms corresponding to the frequency of stimulation (Frederick et al., 2004). Russell (1997) reported on a study in which the continuously varying PAF of LD and ADHD children was used as a signal to produce AVS alternately at 5% above and 5% below the PAF for 30 second intervals, for 20 minute sessions. While the theoretical aim of this study was to improve the flexibility of the PAF (not to change the mean frequency), it showed that treating the PAF can effectively treat cognitive dysfunction. These children showed significant gains in cognitive and behavioral measures that persisted to the 16-month follow-up.
We hypothesize concurrent EEG-Driven photostimulation during PAF enhancement neurofeedback (where rewards are presented as auditory tones, with eyes closed) will increase the rate of learning of PAF enhancement, and have increased therapeutic efficacy compared to PAF neurofeedback alone. In addition to helping induce higher PAF, rhythmic photostimulation has the benefit of increasing CBF by inducing repetitive waves of activation throughout the brain. It may therefore also enhance the effects of the HEG training described above in part I.
Understanding the mechanism of action of neurofeedback could potentially lead to more refined methods of treatment with improved efficacy. We propose three measurements that would contribute to an improved understanding of how neurofeedback works.
- A common practice in EEG neurofeedback is for the reward signal to be adjusted so that the client spends the majority of the time with the reward tone on. This is believed to improve motivation and compliance, but there might be another reason. When the reward tone goes off, it amounts to a rare event requiring a behavioral change. This situation is similar to the design of many studies which have shown that the absence of an expected stimulus evokes a positive deflection in the EEG called the P300 wave. The P300 has been associated with the orientation reflex, and with a phase-resetting of the background EEG rhythms. If a greater P300 during reward tone offset is observed than during reward tone onset, this would suggest that the brain responds to the loss of reward with an electrical signal that disrupts and resets ongoing EEG activity. This would provide independent physiological support to the conventional clinical wisdom of providing frequent rewards.
- Some theorists argue that the efficacy of neurofeedback depends on the normalization of the abnormal physiology (e.g., PAF or CBF) correlating with the disorder (Duff, 2004). Others (Othmer, Othmer and Kaiser, 1999) argue that the efficacy of neurofeedback results from the brain's adaptive responses to the altered brain states induced by training. This model is similar to that explaining the efficacy of antidepressant drugs, and could explain the importance of multiple sessions over weeks and months of training. Thus, we will measure whether changes in clinical and neuropsychological test variables relate with the changes of PAF and CBF (average and variance per session). The relationships (or lack thereof) between physiological and psychological variables from this study will contribute to the field's understanding of how neurofeedback works.
- A criticism raised against HEG training in favor of EEG biofeedback is that blood flow changes are typically secondary to metabolic demands created by neuronal activity. Thus, it is unclear whether HEG feedback is training a nonspecific EEG activation or if some other mechanism is involved. We propose to measure the EEG adjacent to the HEG training site to determine if HEG changes are correlated with EEG activation (enhanced 14-21 Hz and reduced 4-7 Hz amplitude, or increased PAF). It is of similar interest to see if the HEG blood flow intensity measure increases during PAF training.
Please refer to this study by its ClinicalTrials.gov identifier: NCT01168466
|United States, Pennsylvania|
|Lafayette Hill, Pennsylvania, United States, 19444-1800|
|Principal Investigator:||Marvin H Berman, Ph.D.||Quietmind Foundation|
|Study Director:||Jon Frederick, Ph.D.||Quietmind Foundation|