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COST B27 ENOC Joint WGs Meeting Swansea UK, 16-18 September 2006. BOLD Changes During Driven Electrical Oscillations in Human Brain. A hmet Ademo ğ lu Bogazici University, Institute of Biomedical Engineering, Istanbul, Turkey. 2 Hz. 3 Hz. 4 Hz. 5 Hz. 1 Hz. 7 Hz. 8 Hz.

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COST B27 ENOC Joint WGs Meeting Swansea UK, 16-18 September 2006

BOLD Changes During Driven

Electrical Oscillations

in Human Brain

Ahmet Ademoğlu

Bogazici University,

Institute of Biomedical Engineering,

Istanbul, Turkey

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Recording Basal BOLD response (30 sec)

Recording BOLD response during stimulation (30 sec)

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BOLD Changes During Driven Electrical Oscillations in Human Brain

Zübeyir Bayraktaroğlu1, Uzay E. Emir2, CengizhanÖztürk2, Ahmet Ademoğlu2, Tamer Demiralp1

1 Istanbul University, Istanbul Faculty of Medicine, Department of Physiology,

2 Bogazici University, Institute of Biomedical Engineering, Istanbul, Turkey


While EEG represents the spatial summation of the synchronous electrical activity of neurons (real functional signal of neural activity), with a high temporal resolution, it is insufficient for localisation of the brain structures that generate these signals. On the other hand, fMRI BOLD (Blood Oxygene Level Dependent) response reflects perfusion and oxygen level changes in the brain tissue with a high spatial resolution, but its’ temporal resolution is not enough to follow neural dynamics.

The correct modeling of the neurovascular coupling is still a very important gap for revealing the relationship between the BOLD response and functional neural activity patterns. Tonic neural discharges can be expected to generate increases in BOLD response, whereas it is not yet systematically investigated how the metabolic activity changes during oscillatory activities in the EEG.

A good way of producing synchronization patterns in the EEG that are stationary within the time-constant of the BOLD response is to evoke steady-state evoked potentials. When the brain is stimulated with stimuli at a high repetition rate, steady-state evoked potentials are obtained in the EEG at the stimulation frequency and its’ harmonics (Regan, 1989). When steady-state evoked potentials to visual stimuli within the range of 1-100 Hz were investigated, amplitudeincreases have been foundat certain stimulation frequencies (10, 20, 40 and 80 Hz) close to the peaks in the EEG spectrum (Herrmann, 2001). Although the functional significance of this frequency selectivity is not yet fully understood, it is considered that they reflect the dynamic properties of the neural networks responsible from different stages of sensory processing.

In the preliminary stage of the present study, we investigated EEG and BOLD responses to steady-state visual stimulation with a checkerboard reversal pattern created by a computer. To overcome the refreshing rate limitations of the graphics card and the data projector and to increase the frequency range and resolution of the visual stimuli, we developed a LED (light emitting diode) based device with fiberoptic transmission system for the MRI room. This stimulus presentation system enabled us to apply visual stimuli within the 1-100 Hz frequency range with 1 Hz steps.

Materials and Methods:

In this study, the BOLD responses during diffuse light flickering at rates between 1-100 Hz have been systematically studied and the change of the BOLD response has been analyzed in relation to the stimulus presentation frequency.

fMRI – BOLD responses were recorded from 8 healthy subject (6 male, 2 female), aged 28.25 ± 5.06 years.

fMRI recordings:

BOLD measurements were conducted with a 1.5 Tesla Siemens Syngo MRI System using a single shot T2* weighted gradient echo planar imaging sequence. A 3D MPRAGE sequence was used for high resolution anatomic scan. The stimuli were presented at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 20, 22, 24, 28, 32, 36, 40, 44, 50, 60, 70, 80, 90 and 100 Hz. 3 minutes of fMRI recording has been taken for each flickering frequency.

Experimental design blocks

Figure 1. BOLD percentage change with increasing stimulation frequency.

Figure 2. The anatomic localization and amount of BOLD response change with increasing stimulation frequency.

Changes in the localization and extent of the activation also varied between frequencies. The BOLD response peaked around 5, 10, 16, 36 and 60 Hz stimulation frequencies.At these stimulus frequencies a tendency for wider activation regions was observed.


The fact that the BOLD response did not show a linear increase or saturation withincreasing frequencies, but displayed peaks around certain frequencies that roughly correspond to the frequencies at wich steady-state potentials show higher amplitudes, points to the presence of a relationship between the metabolic activity and the electrical oscillations in the EEG.

Additionally, the wider distribution of BOLD activations at these frequencies suggests that these preferred stimulus rates are more effective in activating the neighbouring secondary areals around the primary visual cortex.


fMRI data were processed by AFNI software package. Images were registered in order to remove motion artifact. For each dataset, activated regions were determined by correlating one regressor general linear model.


In the preliminary study, it has been observed that the BOLD response showed two peaks at 5 and 10 Hz. The decreasing BOLD amplitude with increasing frequencies built another peak around 18 Hz. These results are consistent with the current fMRI literature [Parkes, 2004].

In the second phase of the study, diffuse light was used for stimulation. This allowed to see effects of stimulation frequency in a better resolution. BOLD signal did not show a linear increase or saturation withincreasing frequencies, but displayed certain peaks and deeps at certain frequencies (Figure 1).

  • References:

  • Herrmann CS (2001). Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena. Exp Brain Res 137, 346–353

  • Logothetis NK, Pfeuffer J (2004). On the nature of the BOLD fMRI contrast mechanism. Magnetic Resonance Imaging 22, 1517–1531

  • Parkes LM, Fries P, Kerskens CM, Norris DG (2004). Reduced BOLD response to periodic visual stimulation. NeuroImage 21, 236– 243

  • Regan D (1989). Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Elsevier, New York



Tolgay Ergenoglu4, Christoph S. Herrmann2, M. Emin Erdal3, Yasemin H. Keskin1,

Mehmet Ergen1, Hüseyin Beydagi4, Tamer Demiralp1

(1) Istanbul University, Istanbul Faculty of Medicine, Department of Physiology, Turkey

(2) Otto-von-Guericke University Magdeburg, Department of Biological Psychology, Germany

(3) Mersin University, Medical Faculty, Department of Medical Biology and Genetics, Turkey

(4) Mersin University, Medical Faculty, Department of Physiology, Turkey


  • Electrophysiological recordings in different species indicate that gamma oscillations (30-70 Hz) in the brain are associated with a variety of fundamental perceptual and cognitive processes [1]. Possible generation mechanisms have been proposed for gamma oscillations [2,3]. However, less is known about the neurochemical basis of the modulation of evoked gamma responses during cognitive processes, although attention and working memory tasks modulate them [4].

  • Significant changes in the gamma responses have been observed in schizophrenia and Attention Deficit Hyperactivity Disorder (ADHD), which lead to significant failure of attentional modulation and to working memory deficits [5]. Both disorders also have significant associations with three genetic polymorphisms concerning the dopamine system, which is critical for cognitive functions. Polymorphisms of the dopamine receptor D4 (DRD4) gene, dopamine transporter (DAT1) gene and catechol-Omethyltransferase (COMT) gene showed significant associations with schizophrenia and ADHD [6,7]. Therefore, we aimed to investigate whether direct relations exist between the DRD4, DAT1 and COMT polymorphisms and the amount of gamma oscillations of normal subjects evoked by an auditory selective attention paradigm.


  • Subjects: Fifty right-handed, healthy, male aged 21.5 ± 1.64 years

  • Electrophysiological Recordings: EEG and ERPs were derived from 16 electrodes placed according to the 10-20 system (Oz, O1, O2, Pz, P3, P4, Cz, C3, C4, T3, T4, Fz, F3, F4, Fp1, and Fp2). EOG was recorded for artifact elimination.

  • Cognitive Paradigm: A classical auditory oddball paradigm was employed.

  • Analysis of ERPs and Oscillations: After rejection of artifacts, peak amplitudes and latencies of the P50, N100, and P300 waves of the averaged ERPs were measured. For the analysis of event-related oscillations, the data were transformed to the time-frequency plane using a wavelet transform.

  • Wavelet Transform: To compute a wavelet transform, the original signal was convolved with a complex Morlet wavelet. Evoked acitivity phase-locked to stimuli was calculated by applying WT on average responses of each subject.

  • Genotyping:

    • Venous blood sample collection into ethylene diamine tetra acetic acid (EDTA)

    • DNA extracted from peripheral blood leukocytes by salting out procedure

    • The polymerase chain reaction (PCR) based genotyping of the polymorphisms

    • The PCR products resolved on a 2.5 % agarose gel containing 0.5µg/ml ethidium bromide

    • The gel visualized under UV light using Vilber Lourmat system

  • Polymorphisms and genotypes:

  • DRD4 exon III : 2/2, 2/4, 3/4, 4/4, 4/6, 2/7, 3/7, 4/7, 7/7

  • DAT1 VNTR : 8/10, 9/9, 9/10, 10/10

  • COMT Val 108/158 Met : H/H, H/L, L/L

  • Statistics: The amplitude and latency differences of ERPs and amplitude differences of evoked gamma responses between groups with different genotypes were tested by a repeated measures ANOVA design with the genotype as the between subjects factor (genotype: 2 levels - 7 repeat vs others for DRD4, homozygous 10/10 vs. others for DAT1 and homozygous H/H vs others for COMT), and stimulus (2 levels: standard vs. target), anteroposterior topography (3 levels: frontal, central, parietal) and lateral topography (3 levels: left, midline, right) as the within-subject factors.


  • Because 2 of the 50 subjects had a high number of trials with artifacts, they were excluded from further analyses. Each of the DRD4, DAT1 and COMT polymorphisms were divided into two subgroups according to the associations of the genotypes with cognitive disorders. Genotype 2 always shows some association with cognitive disorders.


In our study, the 7-repeat isoform of DRD4 polymorphism yielded a significant increase in the auditory evoked gamma responses to both target and standard stimuli. This finding is in line with the gamma results [5] and DRD4 results in ADHD [6], which showed a significant association between ADHD and the 7-repeat allele of the DRD4 polymorphism. The D4 receptor can affect potassium channels as well as GABAergic chloride channels [3] thus modulating the excitability of neurons. Generally, dopamine is believed to inhibit activity of pyramidal cells if effective via the D4 receptor. Increased gamma activity in subjects with the 7-repeat isoform of DRD4 polymorphism might be the result of less inhibition via the D4 receptor.

The homozygous 10-repeat allele (10/10) of the DAT1 polymorphism introduced a significant amplitude increase specifically in evoked gamma responses to targets, whereas no significant change was observed in gamma responses to standards. The inefficient variant of DAT1 that yielded the enhanced gamma response to targets probably also resulted in enhanced dopamine levels in extracellular space. Because it has been proposed that task-related activity in neurons of prefrontal cortex (PFC) during working memory is modulated by dopamine mainly via the D1 receptor [8], it seems plausible to assume that this DAT1 effect was mediated through the action of increased extracellular dopamine on the D1 receptor.

The absence of any differences between the evoked gamma responses of the subjects with the high and low-activity variants of the COMT gene seems to be contradictory to the results obtained with DAT polymorphism. However, the facts that the uptake by the DAT is the most effective mechanism for the termination of the synaptic action of dopamine in the brain and that the role of COMT remains minimal under normal conditions [9] could explain this difference between the DAT and COMT results.

In conclusion, our results suggest that the action of dopamine via the D4 receptor inhibits the evoked gamma response nonselectively to all stimuli. However, increased levels of extracellular dopamine, due to an inefficient DAT, selectively enhance target gamma responses and probably reflect the D1-mediated dopaminergic contribution to a prefrontal target detection mechanism.

List of detected genotypes and number of subjects with each genotype is demonstrated in table.1.

ERP Results:P50, N100, or P300 amplitudes or latencies were not significantly different between the 2 groups of genotypes for any polymorphism.

Evoked gamma band responses: The analysis of the DRD4 polymorphisms revealed a significant increase of gamma activity for the 7-repeat allele (genotype 2) both for target and standard stimuli (genotype: F(1,46)=10.66, p<0.01). For the DAT polymorphism an interaction of the factors genotype and stimulus indicated that only targets are influenced by this polymorphism (genotype x stimulus type interaction: F(1,46)=4.33, p<0.05). In the group with genotype 2, the target gamma response was significantly higher than in the subjects with genotype 1 (genotype: F(1,46)=4.62, p<0.05). COMT genotypes revealed no effect on gamma activity. (Figures.1 & 2)

Figure 1. Time-frequency representations of the ERPs to auditory stimuli (average of target and standards) in frontal midline location (Fz) for genotypes 1 and 2.

Figure 2. Time courses of evoked gamma activity in response to auditory stimuli in Fz for genotypes 1 and 2.

Figure 3. Topographical distribution of the evoked gamma activity in the time interval from 40 to 60 ms for genotypes 1 and 2. Responses are maximal over frontal electrodes (anteroposterior: F(2,94)=6.33, p<0.01). The increase of gamma oscillations for the 7-repeat allele (genotype 2) of the DRD4 polymorphism and 10/10 genotype (genotype 2) of the DAT polymorphism are also maximal over frontal electrodes.


  • Basar E, Schürmann M, Basar-Eroglu C, Demiralp T (2001) Selectively distributed gamma band system of the brain. Int. J. Psychophysiol., 39, 129- 135.

  • Gray CM, König P, Engel AK, Singer W (1989) Oscillatory response in the cat visual cortex exhibit intercolumnar synchronization which reflects global stimulus properties. Nature 338, 334-337.

  • Traub RD, Jefferys JG, & Whittington MA (1999) Fast oscillations in cortical circuits. MIT press.

  • Herrmann CS, Mecklinger A (2001) Gamma activity in human EEG is related to highspeed memory comparisons during object selective attention. Vis. Cogn., 8, 593-608.

  • Yordanova J, Banaschewski T, Kolev V, Woerner W & Rothenberger A (2001) Abnormal early stages of task stimulus processing in children with attentiondeficit hyperactivity disorder--evidence from event-related gamma oscillations. Clin.Neurophysiol., 112, 1096-1108.

  • Faraone SV, Doyle AE, Mick E, Biederman J (2001) Meta-analysis of the association between the 7-repeat allele of the dopamine D(4) receptor gene and attention deficit hyperactivity disorder. Am. J. Psychiatry, 158, 1052-1057.

  • Herken H, Erdal ME (2001) Catechol-O-methyltransferase gene polymorphism in schizophrenia: evidence for association between symptomatology and prognosis. Psychiatr. Genet. 11, 105-109.

  • Seamans JK, Durstewitz D, Christie BR, Stevens CF, Sejnowski TJ (2001) Dopamine D1/D5 receptor modulation of excitatory synaptic inputs to layer V prefrontal cortex neurons. Proc Natl Acad Sci USA. 98, 301-306.

  • Huotari M, Santha M, Lucas LR, Karayiorgou M, Gogos JA, Mannisto PT (2002) Effect of dopamine uptake inhibition on brain catecholamine levels and locomotion in catechol-O-methyltransferase-disrupted mice. J. Pharm. Exp. Therap., 303, 1309-1316.

Table.1: Distribution of genotypes.

Source Position


Spatial Characteristics

Source 1

Superficial Delta (3 Hz)

High Spatial Frequency Map

Source 2

Deeper Delta (3 Hz)

Low Spatial Frequency Map

Source 3

Superficial Delta (3 Hz)

High Spatial Frequency Map

Source 4

Deeper Delta (3 Hz)

Low Spatial Frequency Map

Source 5

Superficial Alpha (14 Hz)

High Spatial Frequency Map

Source 6

Deeper Alpha (14 Hz)

Low Spatial Frequency Map

Source 7

Superficial Alpha (14 Hz)

High Spatial Frequency Map

Source 8

Deeper Alpha (14 Hz)

Low Spatial Frequency Map

Subtopographic EEG Source Localization After

Spatio-Temporal Wavelet Decomposition

Duru A. D*1., Eryilmaz H1., Bayram A1., Ademoglu A.1, Demiralp T. 2

1 Biomedical Engineering Institute, Bogazici University, Istanbul, Turkey

2 Department of Physiology, Istanbul Medical School, Istanbul University, Turkey


Dipole Source Configuration of Simulated EEG


Localization of the cognitive activity in the brain is one of the majorproblems inneuroscience.Current techniques for neuro-imaging are based on fMRI, PET and EEG recordings.The highest temporalresolutionis achieved by EEG, which is crucial for temporallocalizationof activities. But spatial resolution of scalp topography for EEG is low.

To overcome the spatial resolution limitation of scalp topography, several current-density estimation techniques were developed.

The goal is to find the location of the three-dimensional(3D) intracerebral activities by solving an inverse problem.

EEG generally consists of several electrical sources some of which are temporally as well as spatially overlapping. For this reason the scalp topologiesconstituted by these multiple sources makes the inverse problem more complicated.

The aimofthe spatio-temporal decomposition of EEG scalp mapsby wavelet transform is twofold;

i) to isolate temporal frequency components of EEG into bands like delta, theta, alpha ...

ii) to isolate the scalp maps of these individual components into spatial frequency maps which are determined by the depth and extension of individual sources prior to their source localization.


Source Localization

Realistic Head Model



Figure 4) 64 channel Simulated Total EEG activity for the 8 Sources in defined in Table 1. Sampling Rate is 256 Hz and the duration is 1 s.

Table 1) Source configuration for simulated EEG data.


Source Localization (MUSIC)

Figure 5) Topography of Total EEG at 180 th time sample.

5 octave Temporal Decomposition

Figure 6) Music Spectrum of Total EEG data given in Fig. 5.

Delta right deeper

Alpha right superficial

5 octave Spatial Decomposition and Localization for delta and alpha bands


Delta right deeper

Alpha right deeper

Delta left superficial

Alpha left superficial

Delta left deeper

Alpha left deeper

Figure 1) MRI images. (177x240x256, voxel size 1mm x 1mm x 1mm)

The head model that we used in this study is developed using the average T1 weighted human brain MRI data provided by Montreal Neurology Institute (MNI). Statistical Parameter Mapping software 99 release (SPM99) which is developed by Wellcome Institute is used for 3-D segmentation of the brain, skull and scalp. After segmentation, the surfaces are triangulated in order to generate the realistic head model that we need to solve the forward problem.


Real EEG data is obtained from Istanbul University, Istanbul Medical School. Twenty-four healthy right-handed volunteers (13 males and 11 females) were recruited as subjects with a mean age of 25.8 ± 5.6 and a mean education of 17.8 ± 3.3 years.The CPT paradigm consisted of 400 stimuli, 10distractors (B, C, D, E, F, G, H, J, K, L), 1 primer “A”, 1 target “Z”appearing with the following probabilities: 20% primers, 10%Go stimuli (any “Z” after an “A”), 10% NoGo stimuli (anydistractor letter after an “A”) and 60% distractors. EEG was amplified with a band pass of 0.1–70 Hz from 30 scalp electrodes, Oz, O1, O2, Pz, P3, P4, P7, P8, Cz, C3, C4, T7, T8, Fz, F3, F4, FCz, FC3, FC4, CPz, CP3, CP4, FT7, FT8, F7, F8, TP7, TP8, FP1, FP2, and sampled at 200 Hz. After building the ERP epochs of 1500 msduration between −500 and 1000 ms, trials with EEG or EOGamplitudes exceeding ±90 μV were rejected automatically asartifact. ERPs were averaged for the Go and NoGo CPT paradigms

Temporal Decompositon (Delta Coefficient 3 (350-525ms))

Temporal Decompositon (Theta Coefficient4 (525-700 ms))

Figure 2) Tesellated a) brain, b) skull and c) scalp surfaces tesselated with 2000, 1000, 1016 triangles, respectively.

Figure 7) 30 channel averaged ERP activity for Go and NoGo CPT respectively. Sampling Rate is 200 Hz and the duration is 1.5 s.





EEG Electrode Registration for Simulation and Processing

5 octave Spatial Decomposition and Localization for D3

5 octave Spatial Decomposition and Localization T4

Figure 3) a) Position of 64 electrodes, b)The surface of scalp registered with electrodes.

Blue colored points shows the electrode positions.

64 channel EEG electrode locations are registered to the scalp surface by spline interpolation using the T1 weighted MR data, the inion-nasion and pre-auricular coordinates, and the 5-10 Electrode Placement which is similar to the International 10-20 Electrode Placement System. The surface of the scalp is densely represented by 16188trianglesfor registration and topographic mapping (Fig 3).

Forward Problem

Forward problem of EEG, which computes the electrical potentials on the scalp surface given the source positions and strenghts, is solved using the

Boundary Element Method (BEM) with the Center of Gravity (COG) approximation on a realistic head model given in Fig 2.

Inverse Problem

The inverse problem, which estimates the source positions and their strength from multichannel EEG data, is solved using the Multiple Signal Classification (MUSIC) scanning algorithm. MUSIC is based on subdividing the brain tissue into a 3-D grid and computing the spatial power spectrum with an eigenbased approach for each voxel element. In this study, gray matter is scanned as a solution space for MUSIC algorithm with a voxel grid of 8mm x 8 mm x 8 mm.


The T4 coefficient that represents the theta response between 525 and 700 ms shows a left lateralized activation in temporally decomposed data. After spatial decomposition we obtain a clear activation on the left motor cortex that probably corresponds to the motor activity related with the button press with the right hand in addition to a cerebellar activation. The same time-frequency region in the NoGo condition shows two separate activations after spatial decomposition: One in the posterior parietal area and another activation in prefrontal cortex, which does not appear at all in the source localization of the temporally decomposed data. The orbito-frontal activation might correspond to the response inhibition in the NoGo condition of the CPT paradigm.

The D3 coefficient corresponding to the delta response between 350 and 525 ms mostly resembling the topgraphy of the Go-P3 wave is represented with a single source in the temporaly decomposed data of the Go condition, whereas after spatial decomposition multiple generators appear in the mesial surface of the posterior parietal cortex and in left frontal area as expected for the Go-P3. The same delta coefficient in the NoGo condition seems to be generated by a parietal and two bilateral temporal generators when the raw data is used for source localization. After spatial decomposition two generators in the left frontal region appear in addition to a sharper dissociation of the parietal and bilateral temporal sources.

Temporal wavelet analysis of EEG at a given spatial location yields temporally stationary components at temporal frequency bands like delta, theta, alpha. Spatial wavelet analysis of EEG at a given temporal location yields spatially stationary scalp maps at spatial frequency bands. The characteristics of these maps are determined by the depth and extension of individual EEG sources. A spatiotemporal preprocessing of the EEG simplifies the complexity of the scalp map by separating it into several submaps each of which is produced by an individual EEG source. This is a very convenient preprocessing prior to source localization for the isolations of different maps corresponding to different dipole sources. This way, even the temporally correlated EEG sources can be localized after spatio-temporal decomposition of EEG.