3/26/2015

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Infra-slow Fluctuation Training

For Autism Spectrum Disorder

Mark Llewellyn Smith LCSW, BCN, QEEGT

Webinar, 2015

Biofeedback Certification International Alliance

NEUROFEEDBACK SERVICES

Of

NEW YORK

140 West 79th Street, Suite 2B

New York, NY 10024

neurofeedbackservicesny@gmail.com

www.neurofeedbacksericesny.com

212-877-7929

© Smith 2015 2

Typical Neurofeedback

• Focused on voluntary control of EEG parameters:

– Power Training

– Zscore training

– sLORETA training

– SCP training

• Teaches clients to make big thing small and small

things big

– Rewards brain electrical activity when the EEG value

exceeds or is below a value set by the practitioner

© Mark Smith 2015

3/26/2015

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Infra-slow Fluctuation Training

• State Discrimination Training:– Reflection of ISF activity causes the client to “feel” something

– “Training” is merely a display of the ISF signal

– No direction or quantity of the signal is desired

– Client focus: reporting state change

• During training:– Trainer optimizes session through frequency adjustments

– The subject responds with increased awareness of his/her physiological state—state shifts

– Subject is taught to identify optimal states of physiological response

• Once identified, repetitions of optimized training operantlycondition desired behavioral change

Infra-slow Fluctuation Training

© Mark Smith 2015

Infra-slow Fluctuation Training

• Trains the Infra-slow Fluctuations of Alternating Current (AC) current measured in microvolts

• That are driven by Direct Current (DC) fluctuations measured in milivolts

• DC coupled amplifier– Atlantis/Discovery

– Other manufacturers make DC coupled amps

• Single channel bipolar montage

• Ag/AgCl electrodes– Silver/Silver Chloride

© Mark Smith 2015

ISF is Bipolar Training

• In bipolar training the

reference lead is placed on

the head not the ear

• Bipolar = Imaging/training

the difference between two

signals

• All EEG training is bipolar

training

• Due to design of amplifiers:

differential

• Referential training fiction :

ear is silent to EEG

Referential Montage

© Mark Smith 2015

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Differential Amplifier Output(Common Mode Rejection)

Zero Output Non-Zero Output

© Mark Smith 2015

Differential EEG Amplifier: All EEG amplifiers are differential meaning that they reject

that which is common to two signals.

Definition of Terms

• Direct Current (DC) current flows in one direction

Batteries/motors

Brain activity

• Alternating Current (AC) flow of current alternates between (+) and (-)

House hold electricity

Brain waves

© Mark Smith 2015

DC the Steady State Potential

• DC is a standing potential measured in mV

• This potential fluctuates in voltage up and

down

• It is these shifts that are imaged in the

frequency domain in ISF training

• Feedback is given on the frequency equivalent

of the DC shift

• Much of the time

© Mark Smith 2015

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HE’S GETTING TECHNICAL AGAIN!

© Mark Smith 2015

DC Cushion

� DC gives the AC

current “bounce”

� DC acts like a

trampoline and allows

current to continue to

bounce/shift on the

DC like a kid on a

trampoline

� AC amplifiers allow

one big bounce and

stop like a kid who

jumps up from and

lands on the floor

© Mark Smith 2015

More Bounce with DC!

© Mark Smith 2015

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Definitions

• Slow Cortical Potential (SCP): changes in the cortical polarization of the EEG, lasting from 100 ms up to several seconds. It is part of the Direct Current signal

• Research defines Infra-slow Fluctuations(ISF): < .1 hertz

– ISF is Alternating Current

– ISF is not SCP but may reflect SCP activity

– ISF training is executed with a Direct Current (DC coupled) amplifier

• Clinical ISF frequencies trained: From 0.002 to 0.012

– We do not train .0001

© Mark Smith 2015

Definitions Cont.

• AC amplifier: filters and amplifies the alternating

current and excludes DC

– Most Neurofeedback amplifiers

• DC amplifier: filters and amplifies AC + DC

• Atlantis & Discovery, Nexus, are DC coupled

amplifiers

• Both Atlantis & Discovery can be operated in AC only

mode

– Z Score Training/Traditional Neurofeedback

© Mark Smith 2015

Definition of Terms

• All brain

electrical activity

contains both AC

& DC unless one

is filtered out

© Mark Smith 2015

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DC Drives ISF

ISF amplitude

(uV)fluctuations

DC amplitude (mV)

fluctuations

© Mark Smith 2015

Protocol is Data Driven

• Typical Bipolar montage– 1 channel 3 electrodes

– Cannot image the activity under each electrode site

• ISF Electrode array:– 3 Channels-5 electrodes

– 2 channel linked ears

– 1 channel Bipolar montage executed in software

– Simultaneous referential/bipolar montages

• Allows for:– Z-score monitoring

– Amplitude monitoring at each site

– SCP monitoring

© Mark Smith 2015

ISF Metrics & Current

Training Screen

� DC/SCP=Direct Current/Slow

Cortical Potential

Measured in millivolts (thousandths

of a volt) “mV”

� ISF= Infra-slow Fluctuations

Measured in microvolts (millionths of

a volt) “uV”

� Optimum Frequency @ T3/T4 =

right side ISF uV dominance-

� left side SCP greater negative

gradient mV or less positive

gradient

� Optimum Frequency: reduction in

negativity at both sites—cortex

becomes less active

© Mark Smith 2015

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ISF Training

• Turns on discovering an Optimum Frequency(OF)

for each client

• The most common OF from 0.002 to 0.012

• Some outliers are higher, some lower

• Provide feedback on small changes in the

amplitude of the signal both increase and

decrease

• Feedback equates to relative phase of the signal

© Mark Smith 2015

ISF Training

• Not encouraging increase or decrease of ISF amplitude

• Providing info to the brain about change in the signal’s amplitude

• Info causes state shifts—Autonomic

• Seek optimum behavioral state: most alert/most relaxed

• Looking for parasympathetic response-in most cases

• Use peripheral measures:– Finger temp

– GSR

– Capnometry

• Use physiological response while shifting frequency and 24hour report to determine optimization

© Mark Smith 2015

What is reinforced in

Infra-slow Fluctuation

Training?

� Provide info on the change

in amplitude of the signal

compared to a damped

average

o Not simply rewarding an

increase in amplitude

o Not inhibiting or

enhancing the signal

� Providing data on a ratio of

amplitude increase and

decrease/ratio of change

� This data may reflect the

relative phase of this slow

moving signal

� Mirror the Infra-slow

energy

© Mark Smith 2015

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The ISF Training Paradigm

• Basic:

– ISF combined with 1-40 hertz inhibits

• More advanced:

– ISF training/Z-score inhibits

• A ceiling and floor

– ISF combined inhibit/enhance protocol

• QEEG based

– ISF sLORETA

• Full cap

© Mark Smith 2015

Name Change: Infra-low Frequency to

Infra-slow Fluctuation. Why?• Infra-slow does not train an oscillatory event

– .0020 does not train an 8.3 minute oscillation

• ISF images fluctuations in amplitude measured in uV of Infra-slow frequencies that reflect changes in amplitude of DC voltage shifts measured in in mV

• Consonant with the research

• Differentiates ISF from the Othmer’s approach

– Othmer AC amplifier until 2013• HD = High Definition

• A true DC coupled amp

– ISF Atlantis Amplifier DC amp since 2006 at introduction

– No optimum frequency equivalent

© Mark Smith 2015

Origin of the SignalInfra-slow Oscillations (ISO)

� Hughes(2011)

� ISF is part of the local field potential

� Thalamus consistently reveals ISOs.

� ISOs can be observed in slices in isolated thalamic nuclei maintained in

vitro.

� Proposed that in addition to Thalamic Nuclei, Astrocytes are the origin

of ISOs

• There is evidence that ionic shifts between the compartments

of blood and tissue in the cortex gives rise to potential

gradients across the blood-brain barrier and contribute to

these slow oscillations

• Hemodynamic origin as measured by fMRI/BOLD signal

© Mark Smith 2015

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Origin of the ISF Signal

• Palva and Palva (2012) concluded that ISOs originate in

neurons, glia, blood, and the blood/brain barrier

compartment

• Palva and Palva (2012)

– have determined that the “Ultradian Rhythm” (< 0.01 hz) in EEG

recordings, BOLD signals, neuronal activity levels, and behavioral time

series are likely to image the same fundamental phenomenon; a

superstructure of oscillatory ISFs that regulate both the excitatory

level of functional networks and the integration between them.

© Mark Smith 2015

ISF Correlation

Coefficients-ISF Signal

Correlation Coefficients between ISF

(0.002 – 0.05) and conventional band

magnitudes

100 seconds of data sampled 8

times/second

Left Parietal area using sLORETA ROI

estimation

© Mark Smith 2015

ISF Correlation

Coefficients-Random

Noise

Correlation Coefficients between ISF

(0.002 – 0.05) and conventional band

magnitudes

100 seconds of random signal sampled

8 times/second

Left Parietal area using sLORETA ROI

estimation

© Mark Smith 2015

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ISF

Power Law Correlated

�Nir (2008) described a 1/f-like power distribution of <.1 activity

�A power law posits a functional relationship between two quantities.

�The Infra-slow signal quantities:

� Frequency

� Microvolts

© Mark Smith 2015

ISF Research & Function

� Aladjalova (1957, 1964) � ISF becomes intensified, increases in amplitude, by agents that elicit a defense

reaction similar to the response to "stress“

� Aladjalova proposed a role for the ISF in hypothalamic functioning/autonomic function

� She theorized that the increase in amplitude reflected the Hypothalamus’s reparative, parasympathetic, response.

� Supporting a role in the Neuroendocrine system:

� Marshall (2000) � ISF is associated with Hypothalamic-Pituitary secretory activity

� An increase in amplitude of ISF coupled with the release of the Luteinizing Hormone

� Released by the Hypothalamus a LH surge triggers ovulation and stimulates production of testosterone

© Mark Smith 2015

ISF & Higher Frequencies

• Kekovic (2012) ISOs

– Found significant coupling between ISOs and higher frequencies

– Concluded from spectral coherence analysis that ISOs have an

important role in the synchronization of neuronal networks

• Vanhatalo (2004)

– ISOs are embedded in and determinant of the excitability cycle

of higher frequencies

– Established a role for ISOs in the control of gross cortical

excitability

– The phase of the ISO robustly correlated with the amplitude of

higher frequencies.

© Mark Smith 2015

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ISF as Embedded Frequency

• Vanhataloo Cont.

– slow fluctuations are tightly associated with K

complexes (wave form during stage 2 sleep) and

inter-ictal activity (epileptiform discharges)

• Vanhataloo (2005) became so convinced of

ISOs centrality in cortex he stated that any

attempt to attenuate the signal eliminates the

most salient features of the human EEG

© Mark Smith 2015

ISF & DMN & Attention

• Ko (2011)

– Default Mode Network (DMN) high gamma band

(65-110 hz) coherence at ISO frequencies

– Coherence centered at 0.014 hz

• Monto (2008)

– Strong correlation between a subject’s ability to

detect a sensory stimuli and the phase of the slow

frequency signal

© Mark Smith 2015

ISF & Attention

• Broyd (2011)

– Found attention induced deactivations of the ISF signal do not occur in DMN areas in ADHD

– Suggests ADHDers get stuck in self-referential processing

– Unable to turn off DMN when appropriate

• Mairena (2012), Dong (2012)

– Supported Monto & Broyd’s research

– Behavioral performance is correlated with ISOs

© Mark Smith 2015

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ISF & Sleep

• ISOs become prominent during sleep

• Picchioni (2011)

– ISOs organize a broad dissociation of cortical and sub-

coritical activities during sleep

– Organize positive correlations between Cerebellum,

Thalamus, Basil Ganglia, lateral neocortices, &

Hippocampus

– Suggested a role in the organization of sleep-dependent

neuroplastic processes generally

– Consolidation of episodic memory specifically

© Mark Smith 2015

ISF & Function

� Case reports corroborate the research

� Sleep

� Memory

� Migraines

� Attention Disorders

� Sexual dysfunction

� PTSD

� Autism

� RAD

� Affective Disorders

© Mark Smith 2015

How is ISF Training Conceptualized?

• Largely symptom driven

• Three distinct but broad categories

– Homeostatic Deficits of brain state

– Developmental Disorders & Trauma

– Arousal and Activation Disorders

• Use QEEG to identify treatment responders and to assess treatment efficacy

• QEEG is an aid to beginning electrode placement but is not definitive

© Mark Smith 2015

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QEEG Analysis & ISF Treatment

• Developmental Disorders & Trauma

• Florid symptomatic expression

– Anxiety

– RAD

– PTSD

• Autism

• Substantial hypercoherence

• Often start T4/P4

© Mark Smith 2015

QEEG Analysis & ISF Low

Power/Hypocoherence

• Often Instabilities

• QEEG indicators:

– Global insufficient power

– hypocoherenceabnormalities

– Argues for T3/T4 starting placement

– Often tolerates only inter-hemispheric placements

© Mark Smith 2015

Mixed Hypo/Hyper Coherence

Mixed Slowed/Accelerated Phase

• Anxiety

• Symptom dictates

T4/P4

• QEEG suggests

interhemispheric T3/T4

• Start T4/P4, if not good

switch to T3/T4

• If mixed response add

in T3/T4 to T4/P4

© Mark Smith 2015

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Autism Definition DSM IV

• DSM-IV

– Autistic patients fall into four categories:

• Autism Spectrum Disorder (ASD)

• Asperger’s Disorder

• Pervasive Developmental Disorder (PDD)

• Childhood Disintegrative Disorder (CDD)

– These categories were not consistently applied

across clinics and treatment centers

© Mark Smith 2014

DSM 5

• The DSM 5:

– The four classifications of DSM IV are collapsed into one broad catch all: Autism Spectrum Disorder

– Symptoms:

• Fall on a continuum

– Mild-the old Asperger’s group

– More severe

» PDD

» CDD

– Must show symptoms from early childhood even if they are recognized later

© Mark Smith 2014

Autism Definition DSM 5

• A highly varied developmental disorder– Typically characterized by:

• Deficits in:– Reciprocal social interaction

» Difficulty building and maintaining friendships

– Verbal and non-verbal communication

» From functionally non-verbal to:

» Misreading non-verbal interactions

» Inappropriate in conversation

• Restricted interests

• Repetitive behaviors– Overly dependent on routine

– Tics

– OCD

© Mark Smith 2014

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Autism and the Brain

• Research:

– ASD associated with abnormalities in neural

connectivity

• Local and global

– In our field:

• Cantor

• Thatcher

• Coben

– Ubiquitous in the larger neuroscience field

© Mark Smith 2014

Connectivity & Research in

Autism

Disordered connectivity in research both:

� Increased

� Decreased

Disordered connectivity:

� Complicates normal network

synchronization

� Thereby hindering

communication among

functional networks

� Negatively impacts behavior

associated with affected

networks

In QEEG:

� Increased connectivity = hypercoherence

� Decreased connectivity = hypocoherence

Among Autistic clients in ISF treatment, the vast majority reveal hypercoherence

© Mark Smith 2014

Autistic Client QEEG

11 year old boy

Notice the Hypercoherence in all

bands

This is the typical QEEG that we see

among ASD clients

© Mark Smith 2014

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ASD Client QEEG

8 yo female

Diagnosed PDD

Abnormal coherence

Hypercoherence is more prevalent

Hypocoherence in frontal areas.

© Mark Smith 2014

Autism is a Profound Dysautonomia

• Produced by:

– Abnormal neuronal network communication

• Affects all behavioral domains

• Exacerbated by:

– Poor sensory processing

• Trapped in a painful sensory world (Markham, 2007,

2010)

– In combination with Autonomic dysregulation

© Mark Smith 2015

Autism & ISF Training

• Majority start T4/P4

• Right hemisphere focus

• Epileptiform activity

– T3/T4

• Other sites:

– T4/T6 facial recognition/social contact

– T4/F8 language/prosody

– T4/Fp2 emotional lability

© Mark Smith 2015

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Autism ISF Training

• OCD behaviors– T4/Fz

– Fz/Pz

– T4/Fp1

• Left hemisphere training later in treatment course

• Sites dependent on symptoms

• Attention:– T3/Fp1

– T3/F3

• Learning difficulties:– T3/P3

– T3/T5

– T3/O1

• OCD behaviors– T3/Fp1

© Mark Smith 2015

The Child School Neurofeedback Program

Roosevelt Island, New York

© Mark Smith 2015

The Child School

• Established 1973

• School for children with learning disabilities

• 8:1:1 student to teacher & teacher assistant

ratio

• Multi-disciplinary approach that includes:

– Psychologists, speech pathologists, and

occupational therapists

© Mark Smith 2015

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Neurofeedback Program

• Staff:

– John Ferrera, PhD Program

Director/neurotherapist

– Mark L. Smith, LCSW Clinical Director

– Jason Park, MS Neurotherapist

– Xian Zhang, PhD Researcher

• Program initiation: April 2011

© Mark Smith 2015

Neurofeedback Program

• 17 students were enrolled 6-15 years of age

• Two broad groups: – Pervasive Developmental Disorders (PDD)

• Includes children with Autism

– Emotionally Disturbed (ED)• Anxiety

• Or another mood related-disorder

• All participants were having difficulty meeting academic and/or social demands of the school environment– Extreme cases-students at risk of forced transfer to a

different school

© Mark Smith 2015

Neurofeedback Program

• Students also grouped by amount of difficulty:

– High Risk

– Moderate Risk

– Low Risk

• All trained with Infra-slow Fluctuation training

• Average number of NF sessions: 23

• All trained without QEEG guidance

• Assessed with pre/post Child Behavior Check List

© Mark Smith 2015

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Neurofeedback Program

© Mark Smith 2015

Main Group

ED (n=7)

Participants from the ED group (7 total) included three students with a combination of social and generalized anxiety, three students with reactive attachment disorder, and one student with an atypical form of bipolar depression.

PDD (n=10)

Participants from the PDD group (10 total) include children diagnosed to be on the autistic spectrum. The group included students with High Functioning Autism (HFA) and Asperger's syndrome.

Subgroup

Low Risk

Student is making adequate academic and social progress.

Moderate Risk

Student exhibits emotional and/or behavioral difficulties that hinder academic progress and/or leads to behavioral outbursts during school. Student at moderate risk of a forced transfer to a different school.

High Risk

Exhibits significant emotional and/or behavioral difficulties, including disruptive classroom behavior, that cause student to miss a significant amount of instructional time. Student at serious risk of forced transfer to another school as a result of disruptive classroom behavior and lack of adequate academic and/or social development.

Participant Breakdown

Pre-treatment breakdown of

participants by diagnosis and

presenting concern

*ED=Emotional Disorder

**PDD=Pervasive Developmental

Disorder.

• ED group (7 total)

• 3 with social and generalized

anxiety

• 3 with Reactive Attachment

Disorder

• 1 atypical form of bipolar

• PDD group (10 total)

• High Functioning Autism

• Asperger’s Syndrome

© Mark Smith 2015

ED Group

• Three Students with anxiety:– Difficulty initiating and completing tasks

– Under performing academically

– Socially isolated

– Sensory issues• Hypersensitive-overreact to seemingly innocuous stimuli (i.e. touch

and sound)

• Remaining four difficulties with:– Regulating emotional response to academic or social challenges

– Led to serious behavioral disruptions and removal from the classroom

– Sensory seeking-sought out excessive amounts of stimulation

© Mark Smith 2015

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PDD Group

• 10 total= High Functioning Autism/Asperger’s Syndrome

– Emotional reactivity

• Tendency to overreact in response to environmental stressors

• This hypersensitivity to stressors caused disruptive outbursts which often led to removal from classroom

– Making consistent attendance in an academic environment difficult

– Making transitions between different classes and activities challenging

© Mark Smith 2015

Results

• 14 of 17 students had a positive response that involved either:

– a significant reduction of behavioral disruptions and/or

– a reduction or elimination of psychotropic medication and/or

– improved ability to sustain attention during class and continued academic progress

• One student’s positive results were confounded by initiation of an SSRI

• The other two were status quo at completion of treatment

© Mark Smith 2015

Child Behavior Check List

• Pre/post CBCL data available for 12 students

• The CBCL is a standardized measure of

emotional functioning available in:

– Parent, teacher, and self-report forms

– Present group= Pre/post teach rating scales

• Teacher form:

– 113 questions

– 3 point Likert scale

© Mark Smith 2015

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CBCL Cont.

Each CBCL item loads onto one or more

clinical scales:

– Anxious/depressed

– Depressed

– Somatic complaints

– Social problems

– Thought problems

– Attention problems

– Rule breaking behavior

– Aggressive behavior

And six DSM-oriented scales

• Affective problems

• Anxiety problems

• Somatic problems

• ADHD

• Oppositional defiant problems

• Conduct problems

© Mark Smith 2015

•15 years old, 9th grade. 26

sessions ILF at T4P4. During last 15

sessions, T4T6 was added.

•PDD (Aspergers). Can be very

rigid in unstructured situations.

Difficulty with transitions. When

anxious, he would yell very loudly

and refuse to comply with

instructions. Above average

intelligence, average academic

skills.

•Now much calmer and better

with transitions. He still has his

moments, but they are less

frequent and less severe.

MF

0

10

20

30

40

50

60

70

80

90

T1

T2

0

10

20

30

40

50

60

70

80

T1

T2

T s

co

re

sT

sco

re

s

CBCL

Teacher

Rating

Scale

CBCL

Teacher

Rating

Scale(DSM

Scales)

•12 years old, 7th Grade. 26

sessions ILF at T3T4. During last 15

sessions, T3F3 was added.

•History of severe affective

disorder. Extremely depressed and

anxious. Cries daily, becomes

aggressive toward self and objects

(lockers!). Average academic skills,

though little confidence and

compliance with homework.

•He has showed steady progress,

with many blips along the way. He

has stopped directing his anger

outward at others, however .

•Now completely compliant and

finally beginning to make

academic and developmental

gains.

NV

0

10

20

30

40

50

60

70

80

T1

T2

0

10

20

30

40

50

60

70

80

90

T1

T2

T s

co

re

sT

sco

re

s

CBCL

Teacher

Rating

Scale

CBCL

Teacher

Rating

Scale(DSM

Scales)

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CBCL Results

For 11 of the 12 students for which

CBCL data was available,

improvements of greater than one

standard deviation on relevant clinical

scales

© Mark Smith 2015

References

• Billeci, L., Sicca, F., Maharatna, K., Apicella, F., Narzisi, A., Campatelli, G., . . . Muratori, F.

(2013). On the application of Quantitative EEG for characterizing autistic brain: a systematic

review. Frontiers in Human Neuroscience, 7. doi: 10.3389/fnhum.2013.00442

• Broyd, S. J., Helps, S. K., & Sonuga-Barke, E. J. S. (2011). Attention-Induced Deactivations in

Very Low Frequency EEG Oscillations: Differential Localisation According to ADHD Symptom

Status. PLoS ONE, 6(3), e17325.

• Brock, J., Brown, C. C., Boucher, J., & Rippon, G. (2002). The temporal binding deficit

hypothesis of autism. Dev Psychopathol, 14(2), 209-224.

• Cantor, D. S., Thatcher, R. W., Hrybyk, M., & kaye, H. (1986). Computerized analysis of autistic

children. Journal of Autism and Developmental Disorders, 16(2), 169-187.

• Coben, R., & Myers, T. E. (2008). Connectivity Theory of Autism: Use of Connectivity

Measures in Assessing and Treating Autistic Disorders. Journal of Neurotherapy, 12(2-3), 161-

179. doi: 10.1080/10874200802398824

• Jarusiewicz, E. (2002). Efficacy of neurofeedback for children in the autistic spectrum: A pilot

study. Journal of Neurotherapy, 6(4), 39-49.

• Kaiser, M. D., Hudac, C. M., Shultz, S., Lee, S. M., Cheung, C., Berken, A. M., . . . Pelphrey, K. A.

(2010). Neural signatures of autism. Proceedings of the National Academy of Sciences,

107(49)

© Mark Smith 2015

References Cont

• Keković, G., Sekulić, S., Podgorac, J., Mihaljev-Martinov, J., & Gebauer-Bukurov, K. (2012). The slow and infraslow oscillations of cortical neural network. Neurology, Psychiatry and Brain Research.

• Khan, S., Gramfort, A., Shetty, N. R., Kitzbichler, M. G., Ganesan, S., Moran, J. M., . . . Kenet, T. (2013). Local and long-range functional connectivity is reduced in concert in autism spectrum disorders. Proceedings of the National Academy of Sciences, 110(8), 3107-3112. doi: 10.1073/pnas.1214533110

• Ko, A. L., Darvas, F., Poliakov, A., Ojemann, J., & Sorensen, L. B. (2011). Quasi-periodic fluctuations in default mode network electrophysiology. The Journal of Neuroscience, 31(32), 11728-11732. doi: 10.1523/jneurosci.5730-10.2011

• Lynch, C. J., Uddin, L. Q., Supekar, K., Khouzam, A., Phillips, J., & Menon, V. (2013). Default Mode Network in Childhood Autism: Posteromedial Cortex Heterogeneity and Relationship with Social Deficits. Biological psychiatry, 74(3), 212-219.

• Markram, H., Rinaldi, T., & Markram, K. (2007). The Intense World Syndrome – an alternative hypothesis for autism. Frontiers in Neuroscience, 1(1). doi: 10.3389/neuro.01.1.1.006.2007

• Markram, K., & Markram, H. (2010). The Intense World Theory – a unifying theory of the neurobiology of autism. Frontiers in Human Neuroscience, 4. doi: 10.3389/fnhum.2010.00224

• Marshall, L., Mölle, M., Fehm, H. L., & Born, J. (2000). Changes in direct current (DC) potentials and infra-slow EEG oscillations at the onset of the luteinizing hormone (LH) pulse. European Journal of Neuroscience, 12(11), 3935-3943. doi: 10.1046/j.1460-9568.2000.00304.x

• Mion, M., Patterson, K., Acosta-Cabronero, J., Pengas, G., Izquierdo-Garcia, D., Hong, Y. T., . . . Nestor, P. J. (2010). What the left and right anterior fusiform gyri tell us about semantic memory. Brain, 133(11), 3256-3268. doi: 10.1093/brain/awq272

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References Cont.

• Mairena, M., Martino, A., Domínguez-Martín, C., Gómez-Guerrero, L., Gioia, G., Petkova, E., & Castellanos, F. X. (2012). Low frequency oscillations of response time explain parent ratings of inattention and hyperactivity/impulsivity. European Child & Adolescent Psychiatry, 21(2), 101-109. doi: 10.1007/s00787-011-0237-6

• Monto, S., Palva, S., Voipio, J., & Palva, J. M. (2008). Very Slow EEG Fluctuations Predict the Dynamics of Stimulus Detection and Oscillation Amplitudes in Humans. J. Neurosci., 28(33), 8268-8272. doi: 10.1523/jneurosci.1910-08.2008

• Palva, J. M., & Palva, S. (2012). Infra-slow fluctuations in electrophysiological recordings, blood-oxygenation-level-dependent signals, and psychophysical time series. Neuroimage, 62(4), 2201-2211. doi: http://dx.doi.org/10.1016/j.neuroimage.2012.02.060

• Picchioni, D., Horovitz, S. G., Fukunaga, M., Carr, W. S., Meltzer, J. A., Balkin, T. J., . . . Braun, A. R. (2011). Infraslow EEG oscillations organize large-scale cortical–subcortical interactions during sleep: A combined EEG/fMRI study. Brain Research, 1374(0), 63-72.

• Pineda, J. A., Brang, D., Hecht, E., Edwards, L., Carey, S., Bacon, M., . . . Rork, A. (2008). Positive behavioral and electrophysiological changes following neurofeedback training in children with autism. Research in Autism Spectrum Disorders, 2(3), 557-581. doi: http://dx.doi.org/10.1016/j.rasd.2007.12.003

• Pineda, J. A., Juavinett, A., & Datko, M. (2012). Self-regulation of brain oscillations as a treatment for aberrant brain connections in children with autism. Medical Hypotheses(0). doi: 10.1016/j.mehy.2012.08.031

© Mark Smith 2015

References Cont.

• Thatcher, R. W., North, D. M., Neubrander, J., Biver, C. J., Cutler, S., & DeFina, P. (2009). Autism and EEG Phase Reset: Deficient GABA Mediated Inhibition in Thalamo-Cortical Circuits. Developmental Neuropsychology,

34(6), 780-800. doi: 10.1080/87565640903265178

• Uhlhaas, Peter J., & Singer, W. (2012). Neuronal Dynamics and Neuropsychiatric Disorders: Toward a Translational Paradigm for Dysfunctional Large-Scale Networks. Neuron, 75(6), 963-980.

• Vanhatalo, S., Palva, J. M., Holmes, M. D., Miller, J. W., Voipio, J., & Kaila, K. (2004). Infraslow oscillations modulate excitability and interictal epileptic activity in the human cortex during sleep. Proc Natl Acad Sci U S A,

101(14), 5053-5057. doi: 10.1073/pnas.0305375101

• Vanhatalo, S., Voipio, J., & Kaila, K. (2005). Full-band EEG (FbEEG): an emerging standard in electroencephalography. Clinical neurophysiology :

official journal of the International Federation of Clinical Neurophysiology,

116(1), 1-8.

© Mark Smith 2015

ISF Workshops

• New York Metro Region—Secaucus, NJ

– May 1, 2, 3, 2015

– Call 212-877-7929

• Munich Germany

– June 17-19, 2015

– http://www.neurofeedback-info.de/en/

• Cleveland, Ohio-Stress Therapy Solutions

– July 2015 exact datesTBA

© Mark Smith 2015