Intern. J. Neuroscience, 115:1273–1305, 2005
Copyright 2005 Taylor & Francis Inc.
ISSN: 0020-7454 / 1543-5245 online
BIOPHYSICAL MODELING OF TONIC CORTICAL
ELECTRICAL ACTIVITY IN ATTENTION DEFICIT
D. L. ROWE
School of Physics
University of Sydney
New South Wales, Australia
Brain Dynamics Centre & Dept. of Psychological Medicine
Westmead Hospital & University of Sydney
New South Wales, Australia
The Brain Resource Company
Sydney, New South Wales, Australia
P. A. ROBINSON
School of Physics
University of Sydney
New South Wales, Australia
Brain Dynamics Centre & Dept. of Psychological Medicine
Westmead Hospital & University of Sydney
New South Wales, Australia
Received 2 November 2004.
The authors thank Chris Rennie and Daniel Hermens for comments on the manuscript. This re-
search was supported by the Australian Research Council and Postgraduate Awards from the Faculty
of Medicine, University of Sydney, and the Westmead Millennium Foundation, Westmead Hospital.
Address correspondence to Donald Rowe, School of Physics, University of Sydney, New South
Wales, 2006, Australia. E-mail: email@example.com
1274D. L. ROWE ET AL.
I. L. LAZZARO
Brain Dynamics Centre & Dept. of Psychological Medicine
Westmead Hospital & University of Sydney
New South Wales, Australia
R. C. POWLES
School of Physics
University of Sydney
New South Wales, Australia
Brain Dynamics Centre & Dept. of Psychological Medicine
Westmead Hospital & University of Sydney
New South Wales, Australia
The Brain Resource Company
Sydney, New South Wales, Australia
L. M. WILLIAMS
Brain Dynamics Centre & Dept. of Psychological Medicine
Westmead Hospital & University of Sydney
New South Wales, Australia
School of Psychology
University of Sydney
New South Wales, Australia
Psychophysiological theories characterize Attention Deficit Hyperactivity Dis-
order (ADHD) in terms of cortical hypoarousal and a lack of inhibition of
irrelevant sensory input, drawing on evidence of abnormal electroencephalographic
(EEG) delta–theta activity. To investigate the mechanisms underlying this dis-
order a biophysical model of the cortex was used to fit and replicate the EEGs
from 54 ADHD adolescents and their control subjects. The EEG abnormalities
in ADHD were accounted for by the model’s neurophysiological parameters as
follows: (i) dendritic response times were increased, (ii) intrathalamic activity
involving the thalamic reticular nucleus (TRN) was increased, consistent with
enhanced delta–theta activity, and (iii) intracortical activity was increased, con-
sistent with slow wave (<1 Hz) abnormalities. The longer dendritic response
BIOPHYSICAL MODELING OF ADHD1275
time is consistent with the increase in the activity of inhibitory cells types,
particularly in the TRN, and therefore reduced arousal. The increase in intracortical
activity may also reflect an increase in background activity or cortical noise
within neocortical circuits. In terms of neurochemistry, these findings may be
accounted for by disturbances in the cholinergic and/or noradrenergic systems.
To the knowledge of the authors, this is the first study to use a detailed bio-
physical model of the brain to elucidate the neurophysiological mechanisms
underlying tonic abnormalities in ADHD.
arousal, locus coeruleus, noise, norepinephrine, thalamic reticular
A large body of research has sought to determine whether the diagnostic
category of Attention Deficit Hyperactivity Disorder (ADHD; APA, 1994) is
distinguished by specific neural abnormalities. The most robust observation
has been an abnormal increase of global delta–theta neural activity (Chabot
& Serfontein, 1996; Clarke et al., 2001b; Lazzaro et al., 1999; Matsuura et
al., 1993), indexed via electroencephalographic (EEG) recording. The results
from these studies have been interpreted as an indication of cortical hypoarousal
(Satterfield & Cantwell, 1974; Satterfield & Dawson, 1971), which has been
further supported by measures of reduced skin conductance in ADHD sub-
jects (Hermens, 2003; Lazzaro et al., 1999; Satterfield & Dawson, 1971).
Consistent with the hypoarousal theory, stimulant medications, used as the
primary form of treatment in ADHD, have been found to improve symptoms,
particularly in subjects showing enhanced delta–theta activity and reduced
skin conductance (Chabot & Serfontein, 1996; Clarke et al., 2002; Satterfield
& Cantwell, 1974). Individuals with ADHD also show attentional deficits on
cognitive tasks involving the extraction of relevant signals from background
“noise” (Biederman & Spencer, 1999). This deficit may reflect an underlying
abnormality in arousal (Biederman & Spencer, 1999; Yerkes & Dodson, 1908)
or a primary breakdown in the mechanisms of selective attention and signal
processing (Pliszka et al., 1996; Volkow et al., 2001).
Proposals concerning the neural mechanisms underlying hypoarousal in
ADHD have focused on the locus coeruleus (LC) (Aston-Jones et al., 1999;
Solanto, 1998). This nucleus is the primary source of noradrenergic (NA)
fibers with widespread projections throughout the cortex (Berridge & Waterhouse,
2003), and the firing activity in these fibers increases in proportion to arousal
level and decreases monotonically with the onset of sleep (Gottesmann, 2002a;
1276D. L. ROWE ET AL.
Trulson & Jacobs, 1979). It has been hypothesized that overactivity of the
LC may underlie cortical dysfunction in ADHD (Konrad et al., 2003; Pliszka
et al., 1996; Solanto, 1998), given that stimulant medications act to suppress
LC activity via metabotropic α-2 receptors (Curet et al., 1992; Graham &
Aghajanian, 1971; Ramirez & Wang, 1986). These drugs are also thought to
increase extracellular norepinephrine (NE) and dopamine levels by blocking
reuptake (Solanto, 1998). These studies suggest the direct effect of stimulant
medication is to increase extracellular NE levels, whereas the indirect effect
is to decrease intracellular NE levels in LC terminals. However, the precise
physiological mechanisms in which these drugs modulate cortical activity
and arousal remain unclear.
Recent work on biophysical modeling has focused on the role of the
thalamocortical (TC) circuitry in the generation of neural activity across vari-
ous states of arousal (Robinson et al., 2001b; Rowe et al., 2004c). Notably, this
circuitry, which involves the thalamic reticular nucleus (TRN), has been shown
to generate delta and theta activity (Robinson et al., 2001b; Rowe et al., 2004c;
Steriade, 1999), and may be responsible for the delta–theta abnormalities in
ADHD (Rowe et al., 2004a, 2004b). Numerous studies have also implicated
the TRN in the modulation of arousal (e.g., Sherman & Guillery, 2001; Steriade
et al., 1986). During reduced arousal and early stages of sleep, the firing
activity of inhibitory neurons in the TRN increases (Contreras et al., 1996; Kim
et al., 1997; Terman et al., 1996), inhibiting TC relay cells (Kim et al., 1997;
Steriade et al., 1986). Such inhibition leads to significant changes in the
activity of cortical neurons and the resultant EEG, including enhancement of
delta–theta activity (Dossi et al., 1992; Robinson et al., 2001b; Steriade, 1999).
During increased arousal, the tonic firing activity of LC neurons in-
creases (Rasmussen et al., 1986; Reiner, 1986). Many studies have found that
these neurons can also increase the firing rate of inhibitory neurons in the
TRN (Destexhe & Sejnowski, 2002; Sherman & Guillery, 2001), thereby
indirectly exerting inhibitory effects on TC relay neurons. Overactivity of the
LC in ADHD may therefore lead to over-stimulation of the TRN, potentially
pushing these individuals closer to a state of cortical activity that is charac-
teristic of high inhibitory TRN activity, leading to reduced cortical arousal
and increased delta–theta activity. This process would also interfere with the
relay of internal and external neural information via the TC relay neurons.
Rapid information transfer requires rapid tonic firing of TC relay neurons,
which closely follows incoming stimuli (Fanselow et al., 2001; Sherman,
2001). However, a tonic increase in TRN activity is likely to interfere with
this relay process, due to an over-inhibition of the relay neurons.
Deficits in the processing of inputs relayed via the TC projection system
BIOPHYSICAL MODELING OF ADHD 1277
may also occur in the recipient cells residing in local cortical networks, and
abnormalities in these local neural populations may also underlie the signal-
to-noise breakdowns observed in ADHD. This is thought to involve a deficit
in the successful processing of relevant stimuli from irrelevant or background
stimuli, which receives a similar level of attention. Assuming that high-level
information processing and integration occurs within local neural populations
(Gibson et al., 1999; Gupta et al., 2000; Porter et al., 2001), it might be
expected that most sources of noise relevant to such processes would origi-
nate primarily from large populations of local stellate interneurons (both in-
hibitory and excitatory), rather than long-range excitatory pyramidal types.
These local circuit neurons, particularly inhibitory types, are thought to form
important feed-forward inhibitory processes in response to TC input (Gibson
et al., 1999; Gupta et al., 2000; Porter et al., 2001) and it has been suggested
that they are involved in the sculpting and coordination of activities in their
recipient neurons (Galarreta & Hestrin, 2001; Gupta et al., 2000). These
interneurons also form the majority of synaptic connections within local neo-
cortical circuits, and significantly outnumber those from the important TC
inputs (Douglas et al., 1995; Gil et al., 1999; Zador, 1999). Therefore, it has
been suggested that the effective processing of sensory information relayed
via TC inputs requires a broad, tonic suppression of these local interneurons
(Kimura & Baughman, 1997; Oldford & Castro-Alamancos, 2003). In this
regard, noise intrusion in ADHD may be due to a deficit in the tonic suppres-
sion of local interneurons, which leads to an increase in neural chatter and/or
neural activities in response to spurious or “background” TC inputs.
These signal processing deficits due to intrinsic cortical noise may also
be related to the neural mechanisms of cortical hypoarousal (Biederman &
Spencer, 1999; Yerkes & Dodson, 1908); that is, increased “noise” due to
enhanced activity of the local circuit inhibitory interneurons may inhibit
potential information processing activity of excitatory cortical neurons and
create a state of cortical hypoarousal. This is consistent with the increased
activity of GABAergic neurons during reduced arousal and sleep (Gottesmann,
2002b), particularly in the subcortex and inhibitory interneurons (TRN) of
the thalamus (Thomson et al., 1996; Thomson, 1997), although the activities
of these neurons in the neocortex is less clear (Borg-Graham, 2001). The
sedative and anxiolytic effects of benzodiazepines and barbiturates, and anes-
thetics such as halothane and isoflurane are also known to (at least in part)
produce these effects through marked potentiation of GABA responses in the
cortex (Farrant, 2001). These studies suggest an increase in GABAergic re-
sponses may also be responsible for arousal and information processing ab-
normalities in ADHD.
1278 D. L. ROWE ET AL.
The aim of this study was to provide a more precise quantification of the
neural mechanisms underlying tonic cortical abnormalities in ADHD using a
biophysical model of neural activity, developed to quantify the mechanisms
of ongoing EEG activity (Rennie et al., 2002; Robinson et al., 2001b; 2004;
Rowe et al., 2004c). The neurophysiological basis of the model has enabled
an understanding of changes in cortical activity in terms of realistic physi-
ological parameters (Robinson et al., 2001b; 2004). In particular, this has
included the values of neurophysiological parameters underlying varying states
of arousal (Robinson et al., 2001b; 2002) and the theoretical and empirical
relationships between these parameters and changes in traditional quantita-
tive EEG measures (Rowe et al., 2004c). Results from fitting the model to
empirical EEG data indicate that states of reduced arousal, characteristic of
increased delta–theta EEG, are associated with (i) larger dendritic time con-
stants, particularly for inhibitory neurons (i.e., GABAB), and (ii) increased
activity in pathways involving the inhibitory TRN (Robinson et al., 2001b;
2002; Rowe et al., 2004c).
Given evidence for hypoarousal in ADHD (Satterfield & Cantwell, 1974;
Satterfield & Dawson, 1971) and associated increases in delta–theta activity
(Chabot & Serfontein, 1996; Clarke et al., 2001b; Lazzaro et al., 1999; Matsuura
et al., 1993), it is hypothesized that ADHD subjects would show the follow-
ing changes in model parameters: (i) larger dendritic time constants, and (ii)
increased activity in pathways involving the TRN. In view of the signal-to-
noise deficit and reduced arousal in ADHD (Pliszka et al., 1996; Volkow et
al., 2001), a third hypothesis predicts (iii) an abnormal increase in the activ-
ity of cortical interneurons, predominantly inhibitory stellate types. To test
these hypotheses, the biophysical model and its predictions were used to fit
and replicate tonic measures of EEG data in 54 adolescent males diagnosed
with ADHD and their age- and sex-matched healthy control subjects. This
provided values for each physiological parameter, thereby quantifying the
underlying neural activity in each individual subject and permitting subse-
quent comparisons between the groups.
MATERIALS AND METHODS
Overview of the Model
The structure of the model is reflected in a modest number of neurophysio-
logical parameters, which must lie within plausible physiological limits (Robinson
et al., 2004; Rowe et al., 2004c). Variation outside these limits leads to high
BIOPHYSICAL MODELING OF ADHD 1279
mismatch between model and experiment, and/or seizure like activity in the
waveforms (Robinson et al., 2002). Such variations are thus not relevant to
the clinical subjects of interest and are not considered here.
The model parameters appear in the expression for the theoretical EEG
spectrum used in inverse modeling of experimental EEG data (Rowe et al.,
2004c). For brevity the equations and numerical details have been omitted.
These, including the complete methodology, are summarized in Rowe et
al. (2004b), whereas the full mathematical analysis is also given elsewhere
(Rennie et al., 2002; Robinson et al., 1997; 2001b). The physiological fea-
tures used in the model have also been justified in previous studies (Robinson
et al., 1997; 2001a; 2001b; Rowe et al., 2004c). In this study, the focus is
on the ability of the model to provide physiological insight into the tonic
EEG abnormalities occurring in ADHD, and whether the results are consis-
tent with known physiology.
Neurophysiology—Mass Action—Macroscopic Approach. The neurophysi-
ology of the model is illustrated in Figure 1. Action potentials from various
neurons, represented as neural pulse-rate fields φb = φe, φi, φs (cortical exci-
tatory, intracortical inhibitory, and TC relay, respectively) arrive at the den-
dritic tree (Figure 1a) inducing perturbations in the membrane potential Va,
which varies according to the net effect of all inhibitory and/or excitatory
inputs, including characteristic rate constants. The temporal spread and con-
duction delay of these signals within the dendritic tree are parameterized by
the dendritic rate constants β and α, representing the typical rise and decay
rates, respectively, of the soma response to incoming action potentials at the
synapse. This is characteristic of the low-pass response characteristics of neurons
including synaptic delays associated with receptor dynamics (Robinson et al.,
The mean firing rate Qa (or pulse density) of the neuron (Figure 1b) is
assumed to vary according to a typical nonlinear sigmoid function, such as
that found in the McCulloch-Pitts neuron. The sigmoid relates the firing rate
to the average membrane potential Va, and resembles a smoothed step func-
tion (Freeman, 1975). However, if the EEG signal is treated as being due to
small perturbations about a steady state, the sigmoidal response can be lin-
earized by replacing it by its steady-state slope ρa and combining this with
the number Nab and response strength sb of synapses to give the neural gains
Gab = ρaNabsb listed in Table 1 (Robinson et al., 1997; 2001b). These gains
parameterize the differential number of neural pulses out per pulse in and
describe the effect of input perturbations from the various afferent neural
1280D. L. ROWE ET AL.
fields φb on the firing rate Qa of excitatory and inhibitory neurons (a = i, e).
Action potentials propagate away from cells in a given region along
multiple axons, forming average pulse density fields φa (Figure 1c). The
potentials propagate at an average velocity υa = 5–10 m s–1 depending on
axonal myelination (Bullier & Henry, 1979; Dinse & Kruger, 1994). The
pulse density fields have reduced effects at greater distances due to decreas-
ing terminal density. This effect is incorporated in the model via the damping
rate γa = υa /ra, where ra is the characteristic range of type a axons and υa is
the velocity (Jirsa & Haken, 1996; Robinson et al., 1997). This function is
incorporated in a continuum approach, where the equations describe a con-
tinuum of points having the average properties of typical neurons, as de-
scribed earlier. This also uses a two-dimensional continuum, which is justi-
fied by the relative thinness of the cortex and the scale of neural modeling
and experimental measures (Robinson et al., 1997; 2001b).
Figure 1. The basic neuronal physiology incorporated by the EEG model is illustrated in
a cortical neuron showing (a) synaptic connections at the dendritic tree originating from
pulse-rate fields φb (b = i, e, s), (b) the somatic membrane potential Va (a = e, i) at the cell
body with resultant impulse firing rate Qa, and (c) spread of action potentials as the field φa
BIOPHYSICAL MODELING OF ADHD 1281
Cerebral Connectivity. The axonal range of intracortical inhibitory and ex-
citatory stellate cells (ri ~ .1 mm) is significantly shorter than the axons of
pyramidal cells (re ~ 80 mm) and significantly smaller than the minimum
scale of EEGs (10–50 mm for scalp recordings; Braitenberg & Schüz, 1991;
Nunez, 1981). This permits two simplifications to the model equations: the
inhibitory field φi can be taken as approximately equal to mean firing rate Qi;
and the time constant 1/γi (very large γi), relating to the inhibitory fields, can
be approximated by zero (Robinson et al., 1997; 2001b). A further simplifi-
cation described in Robinson et al. (1997) is that on average the number of
synapses are proportional to the number of neurons involved, and it is argued
that Gee ≈ Gie and Gei ≈ Gii.
The pyramidal cells, as well as having intracortical and corticocortical
connections Gee, also have subcortical projections (see Figure 2). Here, the
Figure 2. Schematic of pathways and connections in the model, and their anatomical sig-
nificance. Open circles represent excitatory neurons and inhibitory neurons are shown with
solid circles. Long-range projections are depicted by solid arrows and short-range projec-
tions by bars. (i) Local intracortical loops are formed by inhibitory and excitatory stellate
cells, and pyramidal types as Gei, with the spatial extent of projections confined within the
minicolumns (dashed). (ii) Corticocortical projections from pyramidal cells extend both locally
and across the cortex as Gee. (iii) These cells also project φe to the thalamus where signals
may propagate via (a) the TRN then SRN with gain Gesre = GesGsrGre, or (b) directly via SRN
as gain Gese = GesGse. (iv) TC afferents returning from the SRN project activity φs to the cor-
tex as gain Ges. (v) Within the thalamus, intrathalamic loops GsrGrs comprise reciprocal pro-
jections between the inhibitory TRN and excitatory SRN. (vi) Cortical activation or sen-
sory input occurs via φN and φs with gain GesGsn. (vii) Additional small delays are induced
by dendritic filtering.
1282D. L. ROWE ET AL.
various pathways have gains Gab, where additional subscripts r and n refer to
the thalamic reticular nucleus (TRN) and external sources, respectively. The
pyramidal cells φe synapse with thalamic relay nuclei (SRN, Gse), which then
project to the cortex via φs (gain Ges). The total gain of this pathway Gese =
GesGse is positive because it involves excitatory glutamatergic neurons. There
is also a negative feedback pathway Gesre = GesGsrGre where corticothalamic
collaterals synapse with the inhibitory TRN (gain Gre), which in turn projects
to thalamic relay nuclei (Gsr), and back to the cortex. An intrathalamic loop,
with overall gain Gsrs = GrsGsr is also present, comprising reciprocal connections
between TC relay nuclei and the TRN. Both Gsrs and Gesre are negative because
the TRN consists of inhibitory GABAergic neurons. The axonal transmission
through Gese or Gesre also induces a signal delay time t0 ≈ .085 s, in addition
to small delays from dendritic filtering. The activity of these gains, transmis-
sion delays, and dendritic filtering exert specific and interdependent effects
on the spectral properties of the EEG (Robinson et al., 2001b; 2002; Rowe et
al., 2004c) and are used to interpret variance in the measures in this study.
Independent Parameters. The preceding parameters with their typical pa-
rameter values are listed in column 4 of Table 1. These values were obtained
from group averages of parameters generated from fits to eyes-closed spectra
of 100 healthy controls during earlier experimental work (Robinson et al.,
2004; Rowe et al., 2004c). The values serve as the initial parameter values at
the commencement of the fitting procedure, and are consistent with indepen-
dent sources and physiological measures (Nunez, 1995; Rall, 1967; Rennie et
al., 2002; Robinson et al., 1997; 2001b; 2004; Shwedyk et al., 1977; Stulen
& DeLuca, 1981; van Boxtel, 2001). Varying the initial parameter values
before the fitting procedure has also been found to yield the same spectral fit
and end parameter values to within their uncertainties (Rowe et al., 2004c).
Recent work by Robinson et al. (2004) using a Monte Carlo fitting routine
on the same EEG model has also been found to produce nominal parameter
values that are consistent with those found in Table 1 and Rowe et al. (2004b).
Some parameters in Table 1 are independent of the spectral shape, but are
important factors when simulating EEG. First, k0re is a fixed parameter and is
introduced to approximate the filtering of high spatial frequencies (>k0) due to
volume conduction by the cerebrospinal fluid, skull, and scalp (Rennie et al.,
2002; Robinson et al., 2001b). The overall power normalization parameter P0
(Table 1) is calculated from the experimental data and is related to the model
parameters GesGsn, φn and re , and is adjusted during fitting according to the
overall power of the experimental spectrum (Rowe et al., 2004b).
BIOPHYSICAL MODELING OF ADHD 1283
The electromyogram (EMG) power normalization parameter A is part of
an EMG correction algorithm (Rowe et al., 2004c) that was developed from
the EMG modeling work of van Boxtel et al. (2001) and Shwedyk et al.
(1977). During the fitting procedure the EMG parameter A is adjusted to
correct for high-frequency pericranial muscle artifact and does slightly effect
the amplitude of the high-frequency (>25 Hz) component of the spectra (Rowe
et al., 2004c). This is consistent with observations by the present authors and
others of enhanced spectral power at high frequencies (>25 Hz) during con-
ditions of jaw clenching, frowning, and other facial movements (Shwedyk et
al., 1977; van Boxtel, 2001).
EEG data were acquired for 54 adolescent males diagnosed with ADHD (mean
age = 13.7 years; SD = 1.4; age range = 11–17 years) and 54 age- and sex-
Table 1. Typical parameter values for the EEG and electromyogram (EMG) theoretical
model spectrum as described in text
Model Parameter DescriptionTypical value
Cortical damping rate (υ/re)
Dendritic decay rate
Dendritic rise rate
Conduction delay through thalamic nuclei
Excitatory gain in pyramidal cells
Local intracortical gain (net inhibitory–
Cortico-thalamocortical gain via SRN
Cortico-thalamocortical gain via TRN
Volume conduction filter parameter
Characteristic pyramidal axon length
Overall power normalization (µV2/Hz) Calculated from data
Power normalization0.5 µV2/Hz
The neural gains Gab reflect the input/output response characteristics of the respective
neural populations, whereas other parameters reflect dendritic and axonal delays, power nor-
malization and filtering properties of the scalp. The EMG parameter A, independent to the
EEG model, is a normalization factor, which corrects for pericranial muscle artefact according
to an EMG algorithm.
1284D. L. ROWE ET AL.
matched normal control subjects (mean age = 13.4 years; SD = 1.5; age range =
11–17 years). All subjects were required to have had no history of neurological
disorder or substance abuse. The EEGs for these subjects were obtained from
a series of studies by Lazzaro et al. (1999; 2001). In these studies patients
were referred by pediatricians, clinical psychologists, and psychiatrists who
considered them to have a diagnosis of ADHD. All patients were further
categorized according to DSM-IV criteria (APA, 1994) using a semi-structured
interview. This included 47 of the Combined type (Inattentive and Hyperac-
tive-Impulsive) and 7 of Predominantly Hyperactive–Impulsive type.
At the time of EEG testing all ADHD subjects were unmedicated. Of the
total, 34 were drug naive and 20 were withdrawn from stimulant treatment
for at least 2 weeks prior to testing. Subsequently, each patient was rated
using the Conners’ Parent (48-item) and Conners’ Teacher (28-item) Rating
Scales (Conners, 1989), and the Achenbach Child Behavior Check List for
parents (Achenbach, 1991a) and Teacher’s Report Form (Achenbach, 1991b).
The selection criteria were based on a hyperactivity index that was 1.5 SDs
above published norms for the Conners’ Teaching Ratings and 1.0 SDs above
the norm for the Conners’ Parent Rating. The control subjects were recruited
from local high schools and further evaluated to ensure no history of ADHD.
Only subjects with a T-score of <1.0 SD above the norm on the Conner’s
Parent and Teacher Rating Scales were accepted into the studies (Lazzaro et
al., 1999; 2001). The adolescents in both groups were also evaluated for
intellectual ability using the assessment protocols described in Lazzaro et al.
(1999; 2001) and were required to have an IQ estimate of 75 or greater.
The total of 54 subjects rather than either the 47 or 7 subtype groups
were used to permit comparison with the prior EEG studies examining this
group. Note also that the pattern of statistical results remained very similar
with the latter group removed (see Results section).
EEG Data Acquisition and Scoring
EEG data were acquired using the recording protocol in Lazzaro et al. (1999).
The focal recording sites of interest in this study were the midline frontal (Fz),
central (Cz), and parietal (Pz) sites. During the recording subjects were awake
and non-drowsy and EEGs were acquired continuously for 2 min during a
resting eyes-closed condition. Ocular artifacts were corrected offline according
to the method of Gratton et al. (1983). For each EEG recording the average
experimental power spectrum Pexp from .24–49.8 Hz (204 data points) was
calculated for 27 successive 4-s epochs using a fast Fourier transform analysis.
BIOPHYSICAL MODELING OF ADHD 1285
EEG Data Fitting
For model fitting, loge of the sum Pest of the theoretical EEG and EMG spec-
tra was fitted to logePexp (experimental spectra) measured at a single site.
Logarithms were taken to permit each frequency decade to be weighted roughly
equally, thereby maintaining fits based on spectral detail rather than the num-
ber of data points (Rowe et al., 2004c). To minimize noise Pexp was also
smoothed over a full width of 1.0 Hz, as this has been found to reduce
uncertainty in the model parameters (Rowe et al., 2004c). The error between
Pest and Pexp was reduced by parameter optimization using the Levenberg-
Marquardt method (Press et al., 1992), in which,
χ2 = Σ
[loge(Pexp( fi)) – loge(Pest( fi))]2
was minimized (Rowe et al., 2004c).
The data fitting procedure was identical to that detailed in Rowe et al.
(2004b), with the following exceptions: (i) a stopping criterion was set at
χ2 < 25 to ensure a good fit, (ii) σi = 0.2 was assumed on the basis of
relatively even fluctuations in log P(f) versus frequency, and (iii) γe was
constrained within the limits 50–210 s–1. Previous work implied that γe should
be within this range since 89% of values for γe converged within these limits,
8% converged within the following broader limits, 210 < γe < 400 or 35 < γe
< 50, and only 3% failed to converge (Rowe et al., 2004c). Furthermore,
axonal velocity of myelinated neurons in the mammalian cortex is also ex-
pected to be within 5–10 m s–1 (Bullier & Henry, 1979; Dinse & Kruger,
1994), and axonal range re within .05–.1 m (Braitenberg & Schüz, 1991;
Nunez, 1981). Therefore, given γe = υe/re, broad limits of 50–210 s–1 for γe
can be determined that approximate experimental findings (Robinson et al.,
2004; Rowe et al., 2004c).
Quantitative EEG (qEEG) Analyses
Because the focus of this study is the physiological model parameters, for
brevity the full methodological and statistical details of the qEEG analysis
have been omitted, these are found in Lazzaro et al. (1999). However, a
summary of these results is included to confirm the abnormal increase in
1286D. L. ROWE ET AL.
theta qEEG activity in the ADHD sample. This was determined by comput-
ing the relative and absolute qEEG power in the Delta (1.0–3.0 Hz) and
Theta (4.0–7.0 Hz) bands. Each frequency band was submitted separately to
a repeated two-way analysis of variance (ANOVA), with Group (control sub-
jects vs. ADHD) as the between subject factor and Site (Fz, Cz, Pz) as the
repeated within subject factor. As the central focus in this study is the be-
tween-groups effects, main effects of site were not analyzed.
Delta activity did not show any significant Group differences. However
ADHD patients showed significantly increased relative (F1,106 = 8.54, p <
.005) and absolute (F1,106 = 12.6, p < .005) global Theta activity. There was
also a significant decrease in relative beta activity in the ADHD group com-
pared with the control group (F1,106 = 9.98, p < .005). There were no signifi-
cant Group × Site interactions for the midline sites for these band powers
(Lazzaro et al., 1999).
Model Parameter Analyses and Data Screening
In Figure 3, one example chosen at random from each group illustrates the
high accuracy of the model fits obtained for most subjects (further fit ex-
amples are available in Rowe et al., 2004c). The model is shown to match
the characteristic spectral properties of the EEG closely, and with such spectra
the model provided robust parameter values.
Figure 3. Sample of model fit for one subject selected at random from each group (a) ADHD
and (b) healthy control. Each frame compares the subject’s experimental spectrum (–) with
their modelled spectrum (···), and lists the subject’s ID number, site, and corresponding χ2
value, reflecting goodness of fit.
BIOPHYSICAL MODELING OF ADHD 1287
Consistent with previous results using large sample sizes, the physiologi-
cal parameter values followed a normal distribution (Rowe et al., 2004c).
However, outliers did occur in limited cases where spectra did not accurately
constrain and fit the complete set of physiological parameters, possibly due
to noise and/or featureless spectra. It was found that these cases had very
wide basins (flat valleys) of attraction in parameter space, which caused pa-
rameters to be widely scattered, with some values becoming abnormally large.
Following the convention in brain imaging studies, these outliers were ac-
counted for by removing outlying data points (median +/– 2 SD or more) in
parameters of interest, γ, α, Gei, Gee, Gese, Gesre, and Gsrs, and replacing these
with the new mean. These amounted to the replacement of approximately 5%
of data points within each group. Each parameter was then analyzed using
the previous two-way ANOVA. Significant interactions were explored fur-
ther using simple effects analysis.
Significant main effects of Group were found for the dendritic rate pa-
rameter α (F1,106 = 2112, p < .001, MSe = 295) and intracortical gain |Gei|
(F1,106 = 4.02, p < .05, MSe = 8.4). These findings were consistent with
hypotheses (i) and (iii) respectively, indicating longer dendritic response times
and increased cortical activity (involving local stellate cells) in the ADHD
The final parameters worth noting were the intrathalamic gain Gsrs and
the cortical excitatory gain Gee. The Group × Site interaction for Gsrs was
nearly significant at the 0.05 level (F2,212 = 2.77, p = .06, MSe = .12), indicat-
ing |Gsrs| (involving the TRN) was greater for the ADHD group at sites Cz
(F1,212 = 6.09, p < .05, MSe = 0.12), and Pz (F1,212 = 9.44, p < .05, MSe =
.12), consistent with hypothesis (ii). The main effect for Gee (involving pyra-
midal cells) was also nearly significant (F1,106 = 3.42, p = .07, MSe = 8.5),
showing a trend of increased activity in the ADHD group, also consistent
with hypothesis (iii). No other effects were significant at the p < .05 level.
These trends are illustrated in Figure 4, showing that the dendritic rate
parameter α is consistently lower (longer time constant) in the ADHD group
across all sites. Both |Gsrs| and |Gei| are shown to be greater in the ADHD
group, particularly at central and parietal sites.
The model parameter effects were reanalyzed using the same methodol-
ogy, but with the 7 Predominantly Hyperactive–Impulsive type subjects re-
moved. This analysis produced a similar pattern of results as found earlier
with these subjects inclusive. The main effect of group for α remained sig-
nificant at p < .005, |Gei| was nearly significant with p = .05, and for Gee, p =
.09. The Group × Site interaction for |Gsrs| was now significant at p < .05.
1288D. L. ROWE ET AL.
A biophysical model of brain activity has been used based on primary neural
properties, cell populations, and networks, to infer physiological abnormali-
ties underlying tonic EEG measures in ADHD. Consistent with predictions,
the model has been able to significantly discriminate the ADHD subjects
from their controls according to three key model parameters: (i) a decrease in
the dendritic rate parameter α (longer recovery time), (ii) an increase in the
magnitude of inhibitory intrathalamic gain (|Gsrs|) involving the TRN, and
(iii) an increase in the magnitude of intracortical (net inhibitory) gain (|Gei|)
involving local circuit inhibitory and excitatory interneurons. These differ-
ences may be interpreted with some confidence given the robustness of fits
between the model and experimental data. These fits showed a very high
accuracy (low χ2) over the entire spectral range (.25–50 Hz), and the model
also faithfully reproduced the inter-subject variability seen in the spectral
shape (Figure 3).
The examination of ADHD subtypes requires mentioning given the im-
portant work that is being carried out in this area with respect to EEG (e.g.,
Clarke et al., 1998; 2001a; 2001c; Kuperman et al., 1996). However, because
it is not a focus of this study, given hyperactivity was the primary selection
criteria, and there was only a small number (n = 7) of the Predominantly
Hyperactive–Impulsive type, the examination of ADHD subtypes with larger
numbers using the EEG model is left for future work. The exclusion of the
Predominantly Hyperactive–Impulsive subtype group also produced very similar
statistical results with only small statistical variations, such as the Group ×
Figure 4. Mean values across site for significant parameters showing significant effects between
the Control (light bar) and ADHD (dark bar) groups.
Mean |Gii| intracortical gain
Mean |Gsrs| intrathalamic gain
Mean dendritic rate parameter
BIOPHYSICAL MODELING OF ADHD 1289
Site interaction for Gsrs now becoming significant at p < .05. Therefore, the
inclusion of this ADHD subgroup is not considered to alter the interpretation
of results for this study, and also permits comparison with prior studies by
Lazzaro et al. (1999, 2001), which included all 54 subjects. Nevertheless, the
sensitivity of the model parameters is expected to increase in larger samples
when examining ADHD subtypes, which are known to display heterogeneity
in qEEG band powers (e.g., Clarke et al., 1998; 2001a; 2001c; Kuperman et
Intrathalamic Gain |Gsrs|, Delta–Theta
Activity and Hypoarousal
In addition to the finding of increased intrathalamic gain |Gsrs|, the analysis of
QEEG also confirmed that these ADHD subjects exhibited an increase in
delta–theta (7 Hz) power across the midline sites compared to controls, con-
sistent with previous results (Lazzaro et al., 1999), and other studies (Chabot
& Serfontein, 1996; Clarke et al., 2001b; Matsuura et al., 1993). The present
authors also found in a previous study that examined transitions in the EEG
of 100 healthy subjects, that changes in relative delta and theta power were
positively correlated with changes in inhibitory intrathalamic gain |Gsrs| (Rowe
et al., 2004c). In this regard, the increases in intrathalamic activity |Gsrs| in-
volving the TRN, observed for ADHD subjects in this study, may account
for the known enhancements in delta–theta power in this group. This is fur-
ther consistent with indications of hypoarousal in ADHD (Satterfield & Cantwell,
1974; Satterfield & Dawson, 1971) given previous experimental and theoreti-
cal studies have also shown that the TRN is involved in the generation of
delta–theta (1–5 Hz) EEG rhythms during states of hypoarousal, such as
drowsiness and the early stages of sleep (Destexhe & Sejnowski, 2002; Dossi
et al., 1992; Robinson et al., 2001b; 2002; Steriade, 1999). However, this
does not mean that ADHD individuals display spectra resembling those seen
in sleep (Figure 3), but that some spectral components, found particularly in
sleep, appear abnormally enhanced.
Intracortical Gain |Gei| and Hypoarousal
The finding of increased intracortical gain |Gei| in the ADHD group can also
support the hypoarousal theory. Recent work in a previous article has shown
1290 D. L. ROWE ET AL.
that this parameter is both theoretically and experimentally associated with
changes in the slow wave (<1 Hz) component of the EEG (Rowe et al.,
2004c). This frequency range is not typically cited in EEG studies; however,
increases in |Gei| alone tend to lead to a flattening of this spectral component
as an indicator of over-stability. This characteristic is also found during re-
duced arousal and the early stages of sleep (Robinson et al., 2001b). Gener-
ally the firing activity of these inhibitory neurons |Gei| closely follows the
activity of their excitatory counterparts Gee to maintain stability in the cortex,
and prevent runaway inhibitory and/or excitatory activity (Rowe et al., 2004b).
An abnormal tonic increase in one of these populations in ADHD would be
expected to have significant debilitating effects on the efficiency of informa-
tion processing in the cortex.
Dendritic Rate Parameter α α α α α and GABA Responses
The increase in |Gei|, predominantly GABAergic neuronal types, is consistent
with the finding of a lower dendritic rate parameter α (slower response) in
the ADHD group, because time constants for GABAergic neurons are longer
than those of glutamatergic pyramidal (predominantly AMPA) neurons (Thomson
et al., 1996; Thomson, 1997). Reductions in α also reflect an attenuation of
high frequency (>20 Hz) EEG signals (Robinson et al., 2001b; Rowe et al.,
2004c), which would be likely to interfere with the efficiency of cognitive
and sensorimotor processes known to occur within the high-frequency bands
[e.g., gamma EEG (Haig et al., 2000)]. The attenuation of high-frequency
EEG in these subjects is consistent with the findings by Lazzaro et al. (1999)
who found a reduction in relative beta (130 Hz) levels in this subject group
and in other studies (Callaway et al., 1983; Mann et al., 1992).
These findings are particularly relevant to the hypoarousal theory because
activation of GABA receptors are preferentially associated with reduced arousal
and sleep, particularly in the subcortex and inhibitory interneurons (TRN) of
the thalamus (Contreras et al., 1996; Juhasz et al., 1994; Kim et al., 1997).
Furthermore, the sedative and anxiolytic effects of benzodiazepines and barbi-
turates, and anesthetics such as halothane and isoflurane are known (at least in
part) to produce their effects through marked potentiation of GABA responses
(Farrant, 2001). These examples of reduced states of arousal are also character-
ized by enhanced slow wave, delta, and theta activity, but reduced high-
frequency (>8 Hz) activity (Niedermeyer & Lopes da Silva, 1999). Such
effects occurring in ADHD would be expected to significantly impede the
efficiency of information processing and attention.
BIOPHYSICAL MODELING OF ADHD1291
Interneurons and Information Processing
The increase in |Gei| may also directly relate to the primary breakdown in the
mechanisms of selective attention and signal processing proposed in ADHD
(Pliszka et al., 1996; Volkow et al., 2001). This is possible because the acti-
vation of these neuronal types in the neocortex, particularly the fast-spiking
GABAergic interneurons, is not necessarily solely an indication of reduced
arousal, but may also reflect an increase in cortical noise and interference.
Given neural gains Gab = ρaNabsb are proportional to firing rate, and the num-
ber Nab and response strength sb of synapses, the increase in |Gei| suggests an
overall increase in the tonic firing rate and/or the synaptic activity of local
excitatory and inhibitory interneurons during minimal sensory processing and
cognitive demand (eyes-closed). This is opposite to previous findings where
increases in cortical gains were associated with an increase in sensory pro-
cessing (e.g., eyes-open), rather than minimal sensory processing, although
this was for both |Gei| and Gee (Rowe et al., 2004b). A tentative interpretation
of these results is expanded on in the following sections and is summarized
as follows: (i) The brains of the ADHD individuals may excessively process
extrinsic information during periods of minimal sensory load, consistent with
the role of interneurons in the rapid control of neural activity in intracortical
networks (Beierlein et al., 2002; Gupta et al., 2000). (ii) Their brains may
simply be noisier as neurons continue to “chatter” during minimal sensory
processing and cognitive demand, suggesting they lack functional mecha-
nisms to suppress this intrinsic cortical activity. (iii) Or it may relate to the
hypoarousal theory as discussed earlier, assuming an increase in the action
of inhibitory interneurons (like the TRN) can also indicate reduced cortical
The EEG model is based on a quantification of the average properties of
certain neural sub-populations, and inverse modeling allows values to be
inferred for the parameters that characterize these sub-populations. These
values, such as the gains Gab, are combinations of factors (e.g., synaptic
distributions on dendrites, cell subtypes, resting potentials, extracellular con-
ditions) that are more fundamental. At this stage quantitative models of large-
scale EEG do not explicitly incorporate all available neurophysiological details,
although the most relevant are being incorporated in current and future work.
For now the bridge between quantifications and the ultimate mechanisms
1292 D. L. ROWE ET AL.
remains somewhat speculative, but provides groundwork for future studies. It
is with this context in mind that the following sections consider potential
interpretation and neurochemical mechanisms that may account for the ab-
normal variation found in the neural gains in ADHD studies.
Recent evidence suggests that individuals with ADHD may have abnormali-
ties in the cholinergic system (Biederman & Spencer, 2000; Spencer et al.,
2000). This system uses acetylcholine (ACh) and other ligands to modulate
various excitatory (e.g., AMPA, NMDA) and inhibitory (e.g., GABA) pro-
cesses throughout the cortex (Sarter & Bruno, 2000). Nicotine, which acti-
vates cholinergic nicotinic ACh receptors (nAChRs), has been found to im-
prove symptoms in individuals with ADHD (Conners et al., 1996; Levin,
2002; Wilens et al., 1999), and a strong association has been found between
ADHD diagnosis and nicotine intake (Milberger et al., 1997; Pomerleau et
al., 1995; Tercyak et al., 2002). The administration of acetylcholinesterase
inhibitor donepezil (Aricept, an ACh agonist) in youths (8–17 years) with
ADHD has also shown improvements in symptoms (Wilens et al., 2000).
The finding of increased intracortical |Gei| and intrathalamic |Gsrs| gains
in ADHD can be accounted for by a reduction in the tonic activity of the
cholinergic system. In addition to exerting tonic affects on cortical arousal,
the cholinergic system can suppress the intrinsic activity of cortical circuits
while enhancing TC inputs and receptivity to external stimuli (Hasselmo,
1995; Oldford & Castro-Alamancos, 2003). Cholinergic activity leads to in-
hibitory and excitatory effects. The activation of metabotropic muscarinic
ACh receptors (mAChRs) strongly suppresses the activities of intracortical
interneurons, whereas the activation of ionotropic nAChRs on TC terminals
increases glutamatergic responses, thereby enhancing TC input (Gil et al.,
1997; Kimura & Baughman, 1997; Koós & Tepper, 2002). The muscarinic
mechanisms generate a tonic (slow, but sustained) reduction in the intrinsic
noise or “chatter” generated by the dominating neocortical cells (Douglas et
al., 1995; Gil et al., 1999; Zador, 1999), so that external inputs from the
fewer, but modality-specific, TC inputs can be processed successfully in as-
sociated networks, assisted by the rapid nAChRs mechanisms. Therefore,
increases in tonic intracortical activity |Gei| in ADHD in the absence of active
sensory processing may reflect a deficit in this mechanism of muscarinic
suppression, such that spurious signals, or ones that are meant to be inhib-
ited, are not. This view is broadly consistent with the proposed executive
BIOPHYSICAL MODELING OF ADHD 1293
function deficits (Barkley, 1997) and deficits in the frontal lobe inhibitory
system (Barry et al., 2003).
The finding of increased intrathalamic gain |Gsrs| in ADHD can also be
explained by an abnormal reduction in cholinergic activity and therefore hypoarousal.
During waking states and REM sleep the activity of the cholinergic system
increases, whereas during drowsiness and the early stages of sleep there is a
decrease (Jasper & Tessier, 1971; Jones, 1993; Vazquez & Baghdoyan, 2001).
Increased cholinergic activity enhances the depolarization of TC cells via
nAChR activation (Curro et al., 1991; McCormick, 1990), but tonically sup-
presses the activity of the TRN via the activation of M2 mAChRs, also
indirectly enhancing TC activity (Cox & Sherman, 2000; McCormick & Prince,
1986; Steriade, 1999). In contrast, reductions in cholinergic activity lead to
increased activity in the TRN (i.e., increased |Gsrs|) due to disinhibition. In
turn, increased TRN activity leads to the inhibition (hyperpolarization) of TC
cells (Losier & Semba, 1993; Steriade et al., 1986; Timofeev et al., 1996).
This can lead to a reduction in alpha and beta activity via reduced Gese (Robinson
et al., 2001b; Rowe et al., 2004b), consistent with findings showing reduced
beta activity in ADHD (Clarke et al., 2001b; Lazzaro et al., 1999), an indi-
rect consequence of hypoarousal in this context. Therefore, deficits in tonic
levels of ACh can lead to disinhibition of the TRN and increased |Gsrs|, sug-
gesting that individuals with ADHD may also be suffering from overactivity
of the TRN, leading to hypoarousal. This is consistent with the hypoarousal
model (Satterfield & Cantwell, 1974) and findings of reduced skin conduc-
tance response (Lazzaro et al., 1999), indicating low cortical arousal (Satterfield
& Dawson, 1971), elevated delta–theta, and reduced beta EEG activity (Barry
et al., 2003; Defrance et al., 1996) in ADHD subjects.
Norepinephrine and Dopamine
Of further importance are medicinal effects of methylphenidate and dextro-
amphetamine on ADHD symptoms (Anastopoulos et al., 1991; Swanson &
Volkow, 2002; Zeiner et al., 1999), drugs that increase cortical NE levels and
act via the NA and DA systems (Pliszka et al., 1996; Solanto, 1998). Like the
cholinergic system, the activity of NA system is associated with states of
increased arousal and wakefulness promoting actions (Berridge & Waterhouse,
2003). During aroused states, the firing of NA, LC, and DA neurons in-
creases (Gottesmann, 2002a; Trulson & Jacobs, 1979) and this is thought to
improve signal-to-noise ratio in the cortex by using similar metabotropic
receptor mechanisms to the cholinergic system (Curet et al., 1992; Hasselmo
1294 D. L. ROWE ET AL.
et al., 1997; Segal & Bloom, 1976; Volkow et al., 2001). Stimulation of the
LC, in addition to the application of NE, also leads to increased depolariza-
tion of both the TRN and TC relay cells (McCormick, 1989). In contrast,
stimulant medications decrease the baseline tonic firing of the LC via the
activation of metabotropic α-2 receptors (Curet et al., 1992; Graham & Aghajanian,
1971; Ramirez & Wang, 1986), similar to the effects of Clonodine (Pliszka
et al., 1996). These results suggest that stimulant medications may act to
decrease the activity of the TRN by suppressing LC afferents to the TRN,
given this effect outweighs the direct extracellular effects of NE administra-
tion on increasing TRN depolarization (Rowe et al., 2004). Thus, supporting
the hypothesis that individuals with ADHD may suffer from overactivity of
the LC (Konrad et al., 2003; Pliszka et al., 1996; Solanto, 1998) in addition
to reduced activity of the cholinergic system and overactivity of the TRN
leading to hypoarousal.
Neurotransmitter Interactions between Acetylcholine,
Norepinephrine, and Dopamine
Many of the effects of the NA system on arousal and attention may also
occur through interaction with the cholinergic system (Koyama & Kayama,
1993; Zaborszky et al., 1993). This may occur via NA projections from the
LC to the forebrain, brainstem, and cortical areas rich in cholinergic neurons
(Aston-Jones et al., 1999; Dringenberg & Vanderwolf, 1998), which appear
to enhance cholinergic activity (Fort et al., 1995; Tellez et al., 1999). These
results suggest stimulant drugs might increase cholinergic activity by increas-
ing the levels of extracellular NE, assuming that this outweighs the reduction
in LC activity. Similarly, the influence of dopamine on arousal and/or corti-
cal activation may also occur indirectly via DA excitation of basal forebrain
cholinergic cells (Dringenberg & Vanderwolf, 1998; Jones & Cuello, 1989;
Smiley et al., 1999), thereby increasing the release of ACh in the cortex
(Casamenti et al., 1986; Levin, 2002; Pepeu & Bartolini, 1968). Alterna-
tively, DA activity may (in turn) be increased by cholinergic and glutamatergic
neurons from the mesopontine tegmental area (Grenhoff et al., 1986; Lavoie
& Parent, 1994; Miller et al., 2002).
With these results in mind, the proposed “over-activity” of the LC in
ADHD (Pliszka et al., 1996; Solanto, 1998) may seem inconsistent with the
proposed “reduced activity” of the cholinergic system. Over-activity of the
LC may be expected to lead to an increase in the activity of the cholinergic
system, rather than a decrease, because LC activity can lead to stimulation of
BIOPHYSICAL MODELING OF ADHD 1295
the cholinergic system. Similarly, an increase in LC activity may also be
expected to enhance signal-to-noise ratio via metabotropic receptor activa-
tion. However, differences in expected results and effects can be accounted
for by the various subtype NA receptors, which differ in neuromodulatory
effects and desensitization rates (Hasselmo et al., 1997; Summers et al., 1997;
Suzuki et al., 1990).
Theta and Alpha Peaks
Of particular interest are the subjects who show both theta and alpha peaks.
The model does not appear to capture the theta peaks in some subjects com-
pletely, suggesting some of the effects in the neurophysiological parameters
may be underestimated. In particular, a greater proportion of “negative” feed-
back (rather than positive) via the corticothalamic-TC pathway Gesre involving
the TRN has also been shown to lead to an enhancement in the delta–theta
resonance, but also proportionate decreases in the alpha and beta resonance,
particularly for reduced arousal (Robinson et al., 2001b; 2002; Rowe et al.,
2004b), as found in some ADHD subjects (Clarke et al., 2001b; Lazzaro et
al., 1999). The TC projection system shows strong modality specificity and
topographical specialization (Montero, 2000; Newman, 1995). Therefore, there
may be separate TC-corticothalamic pathways with functional specificity that
are influencing the alpha resonance, whereas others involving the TRN are
influencing the delta–theta resonance.
The EEG model does have the capacity to model both alpha and delta–
theta peaks (Robinson et al., 2001b), however there is difficulty in generating
both peaks simultaneously. These two spectral features generally reflect in-
congruous states (wake versus sleep) that are not often simultaneously present
in the healthy brain, at least for tonic measures of neural activity. This phe-
nomenon may be simulated by more complicated network structures where
some networks are subject to excess negative feedback via the inhibitory
TRN, whereas others receive more positive feedback via the excitatory TC
relay neurons. Such variant TC activity across the cortex may also become
more apparent with full topographical modeling. Alternatively the delta–theta
rhythm may arise due to increased activity in cortico-hippocampal feedback
loops (Kahana et al., 1999). However, given the hypothesis of tonic hypo-
arousal in preference to hippocampal memory-related abnormalities (presum-
ably phasic), the possibility of tonic abnormalities in TC mechanisms involv-
ing the TRN seems more likely. Alternatively, it is possible that theta and
alpha peaks are being generated independently during separate epochs. This
1296 D. L. ROWE ET AL.
suggests that in future work, smaller averages, or careful selection of epochs
may be required to examine these data thoroughly, but this would be at the
expense of signal-to-noise.
The aforementioned results suggest that the potential neurochemical mecha-
nisms underlying ADHD may be more complicated than previously thought,
and it is not possible to attribute ADHD to the failure of a single neurotrans-
mitter system, but to a complex set of imbalances occurring in different
neurotransmitters and neural networks. Here this has been considered as an
“overactivity” in a number of systems including inhibitory interneurons (Gei)
in the neocortex and the TRN that seem particularly consistent with larger
dendritic time constants (α) and a state of over-inhibition, leading to reduced
arousal. The increase in |Gei| relating to the slow wave component of the
EEG spectra can be further accounted for by reduced activity of cholinergic
and/or NA metabotropic receptor activation which normally acts to suppress
intrinsic cortical noise and neural “chatter.” In contrast, enhancements in
delta–theta EEG activity can be accounted for by an increase in intrathalamic
gain (Gsrs) involving the inhibitory TRN, and therefore reduced arousal. This
can occur due to disinhibition of the TRN from reduced activation of cholin-
ergic M2 mAChRs and/or increased stimulation of the TRN from LC overactivity.
Therefore, in addition to the abnormal activity of neocortical interneurons
and their interference in the processing of specific TC inputs, a state of
suboptimal arousal may also interfere with signal processing and attention.
It is also noted that the electrophysiological responses examined in this
article are that of “tonic” baseline activity in the EEG rather than the “phasic”
EEG activity apparent in the examination of event-related potentials (ERPs).
The latter incorporate more complex temporal cortical dynamics, as discussed
in Rennie et al. (2002). However, the current study of tonic cortical mecha-
nisms has allowed the determination of some of the potential tonic abnormali-
ties that may be occurring in ADHD. In future work the authors aim to apply
the same principles and similar methodology to EEG measures from multiple
scalp sites in addition to midline, and varying cognitive states and ERPs in
ADHD, including their subtypes. The neurochemical hypotheses proposed in
this study relating to NA and cholinergic mechanisms can also be tested in
future studies aimed at determining the physiological action of drugs in ADHD,
particularly relating to affects on primary neural populations and circuitry
(Rowe et al., 2004a).
BIOPHYSICAL MODELING OF ADHD 1297
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