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Broken Brains or Flawed Studies? An update on Leo and Cohen's Critical Review of ADHD Neuroimaging

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This systematic review sought to examine neuroimaging results on Attention Deficit Hyperactivity Disorder (ADHD) published between 2003 and 2015, paying special attention to the major confound of prior medication use first brought to attention by Leo and Cohen (2003) and subsequently acknowledged in the ADHD literature. Neuroimaging studies comparing children and adolescents with ADHD were identified through searches in Web of Science (BIOSIS, Web of Science Core Collection, MEDLINE), PsychINFO, and EMBASE. All studies focusing on neuroimaging and ADHD were selected for consideration (n=62). Forty studies (64.5%) still included pre-medicated samples despite the confound and eight studies (13%) did not provide information to determine this, leaving only 14 studies with medication-free participants to be analysed. The findings on reported differences in physical systems and in electrical activation between ADHD participants and controls were inconsistent and, in part, short on methodological rigour. Despite technological advances, the current state of research suggests that the understanding of neurobiological underpinnings of ADHD and the significance of that research for individuals diagnosed with ADHD has not advanced since the Leo and Cohen review.
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© 2018 e Institute of Mind and Behavior, Inc.
e Journal of Mind and Behavior
Summer 2018, Volume 39, Number 3
Pages 205–228
ISSN 0271–0137
205
Broken Brains or FlawedStudies?An Update on Leo and
Cohens Critical Review of ADHD Neuroimaging
Charles Marley
University of Edinburgh
Anna M.O’Leary
University of Edinburgh
Va s il i k i - A li k i N ik o p o ul o u
University of Edinburgh
is systematic review sought to examine neuroimaging results on Attention Decit Hyper-
activity Disorder (ADHD) published between 2003 and 2015, paying special attention to the
major confound of prior medication use rst brought to attention by Leo and Cohen (2003) and
subsequently acknowledged in the ADHD literature. Neuroimaging studies comparing chil-
dren and adolescents with ADHD were identied through searches in Web of Science (BIOSIS,
Web of Science Core Collection, MEDLINE), PsychINFO, and EMBASE. All studies focusing
on neuroim aging and ADHD were selecte d for considerati on (n=62). Forty studies (64.5%) still
included pre-medicated samples despite the confound and eight studies (13%) did not provide
information to determine this, leaving only 14 studies with medication-free participants to be
analysed. e ndings on reported dierences in physical systems and in electrical activation
between ADHD participants and controls were inconsistent and, in part, short on method-
ological rigour. Despite technological advances, the current state of research suggests that the
understanding of neurobiological underpinnings of ADHD and the signicance of that research
for individuals diagnosed w ith ADHD has not advanced s ince the Leo and Cohe n review.
Keywords: ADHD, neuroimaging, Leo and Cohen, medication-naive
Atte ntion D ecit Hype ract ivit y Disor der (AD HD) is character ised by d evel op-
mentally inappropriate symptoms of hyperactivity, impulsivity, and inattentiveness
We would like to thank Raymond Russ and the anonymous reviewers for their insightful critique
on our initial dra of this article. Correspondence concerning this article should be sent to Charles
Marley, University of Edinburgh, School of Health in Social Science, Section of Clinical and Health
Psychology, Teviot Place, Edinburgh, EH8 9AG Scotland. Email: charles.marley@ed.ac.uk
MARLEY ET AL.
206
(American Psychiatric Association, 2013). It is considered to be the most preva-
lent child psychiatric disorder worldwide (Dubnov–Raz, Khoury, Wright, Raz,
and Berger, 2014), aecting 5–7% of young people (Polanczyk and Rohde, 2007;
Willcu tt, 2012). at said, accurately accounting for ADHD’s widespre ad expres-
sion is complicated by ambiguous symptoms, frequent comorbidities, as well as the
presence of many of ADHDs dening behaviours among the general population
(National Collaborating Centre for Mental Health, 2009). Researchers are increas-
ingly discussing the nature of this condition (Buitelaar and Rothenberger, 2004),
and even its very existence has been questioned in some quarters (Saul, 2014). Reli-
able diagnostic criteria are of critical importance in such a climate (Campbell, Shaw,
and Gilliom, 2000); however, the arbitrary cut-o scores and the vague terminology
used to distinguish between healthy and pathological levels of symptom expres-
sion undermine the criteria (National Collaborating Centre for Mental Health,
2009) and has led numerous commentators to argue that this has contributed to an
over-diagnosing of children (Chilakamarri, Filkowski, and Ghaemi, 2011; Jensen,
2002; LeFever, Arcona, and Antonuccio, 2003). In addition to these criticisms, the
prevailing diagnostic system most commonly used to guide ADHD diagnosis,
the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric
Association, h edition, 2013), has been criticised for lacking empirical ground-
ing (Frances, 2013; Kirschner, 2013) and thus has failed in the DSM committees
aspiration for the development of a pathophysiological-based classication system
(Carroll, 2013), with no conclusive evidence of biomarkers for ADHD or any other
psychiatric conditions oered as yet (Jaee, 2018; ome et al., 2012).
is disillusion, along with the appropriate technological advances, have fuelled
the popularity of more rigorous methods of establishing the specic biological
markers of disorder. e most prominent of these approaches are neuroimaging
studies, with numerous research and review papers suggesting a dysfunction in
fronto-striatal and fronto-parietal networks (Bush, Valera, and Seidman, 2005;
Castellanos et al., 2002; Durston et al., 2003; Giedd, Blumenthal, Molloy, and
Castellanos, 2001; Sowell et al., 2003) and reduced volume of the prefrontal cortex,
cerebellum, and cerebrum (Almeida et al., 2010; Bledsoe, Semrud–Clikeman, and
Pliszka, 2013; Mostofsky, Cooper, Kates, Denckla, and Kaufmann, 2002; Soliva,
Moreno et al., 2010) as being common across ADHD-diagnosed patients. e
recently developed diusion tensor imaging (DTI) technique has added to these
ndings, indicating possible variations in functional connectivity in fronto-striatal
and cerebellar circuitry when ADHD subjects are compared to healthy controls
(Fall, Querne, Le Moing, and Berquin, 2015; Silk, Vance, Rinehart, Bradshaw, and
Cunnington, 2009a).
While the ndings of these studies appear authoritative, they have received
considerable criticism due to several methodological aws. As an example,
a systematic review of over thirty neuroimaging studies by Giedd et al. (2001)
concluded that there are physiological dierences between the brains of children
BROKEN BRAINS OR FLAWED STUDIES? 207
diagnosed with ADHD as compared to a non-ADHD sample. However, Leo and
Cohen (2003) reconsidered the review. e main thrust of Leo and Cohens criti-
cism was that Giedd et al. (2001) failed to consider a major confounding variable:
that the ADHD samples had been medicated over months or years prior to inclu-
sion in a number of the reviewed studies. is important omission led Leo and
Cohen to argue that the evidence suggesting that ADHD is a neurobiological
condition cannot be assumed. In support of their position, the authors high-
lighted the eects of drug administration from animal studies, arguing that the
anatomical dierences revealed by neuroimaging could equally be the impact of
stimulant medication use as opposed to ADHD-specic abnormalities (Breggin,
2000; Sproson, Chantrey, Hollis, Marsden, and Fone, 2001). However, this is not
to say that neuroimaging studies involving medication-naive samples have not
been conducted; for example, in one study involving a medication-naive sample
(Castellanos et al., 2002), the control group participants were more than two years
older, as well as heavier and taller than the ADHD participants, suggesting that
the dierences located could be explained by maturity and growth. As such, the
main criticism of neuroimaging studies still stands: that it is impossible to disen-
tangle cause and eect (Leo and Cohen, 2003).
As neuroimaging studies constitute the bulk of the evidence for the prevailing
neurodevelopmental explanation of ADHD, it is imperative that the ndings are
reliable and valid and drawn from methodologically robust studies. is imper-
ative is further emphasised by the exponential rise in ADHD diagnosis globally
and its associated increase in stimulant medication prescriptions (Bachmann et
al, 2017; Davidovitch, Koren, Fund, Shrem, and Porath, 2017; Nyarko et al, 2017).
Given that treating children and young people with medication is associated with
severe side-eects (Greene, Kerr, and Braitberg, 2008; Higgins, 2009; Holmskov
et al., 2017; Kovsho et al., 2016; Sparks and Duncan, 2004; Swanson et al., 2007),
that the authority for this approach is underpinned by methodologically awed
studies is a profound ethical concern. Utilisation of a pre-medicated sample has
been fully acknowledged within the neuroimaging literature as a major confound
as a result of the Leo and Cohen review (Smith, Taylor, Brammer, Toone, and
Rubia, 2006). Our review engages with this important issue by updating the Leo
and Cohen (2003) study by systematically reviewing neuroimaging studies from
2003 onwards. We also consider sample matching of the studies that have cor-
rected this methodological limitation in light of the other major confound of
maturity and growth highlighted by Leo and Cohen. A nal aim of our review is
to synthesise and contrast the reported ndings of the included studies.
Method
An extensive search for neuroimaging studies comparing children diag-
nosed with ADHD with healthy controls, published between 2003 and 2015,
MARLEY ET AL.
208
was conducted using the following databases: BIOSIS, Web of Science Core Col-
lection, EMBASE, and psychINFO. e keyword search terms included were:
Attention Decit Hyperactivity Disorder (ADHD), neuroimaging, brain scan,
Computerized Tomography (CT), Magnetic Resonance Imaging (MRI), Single-
Photon Emission Computed Tomography (SPECT), and Positron Emission
To mo g r a ph y (P E T ) .
Neuroimaging studies comparing children and/or adolescents (5–18 years)
with ADHD and matched controls were included. Studies in which the diagno-
sis of ADHD was performed according to standard criteria (DSM or ICD) were
retained. Studies including participants with any comorbidities (including con-
duct disorder or oppositional deant disorder) were excluded. Reviews, books,
case reports, theses, and abstracts of conference papers were also excluded from
the original search. At this stage, 3715 papers were sourced. Aer removing the
duplicates and irrelevant papers, 253 full-text articles were screened. Applying the
inclusion and exclusion criteria yielded a total of 62 studies. Details of the studies
that met the inclusion criteria are provided in the results section. A review of the
selection process is displayed in Figure 1.
Figure 1: Literature search results.
BROKEN BRAINS OR FLAWED STUDIES? 209
Results
Prior-Medicated Sample
Of the 62 papers eligible for inclusion, 40 involved pre-medicated participants
and eight failed to provide sucient information. Six studies used the ADHD 200
database and were unable to provide specic demographic information of the
participants. Two studies (Li et al., 2007; Wang, Jiang, Cao, and Wang, 2007) did
not mention prior medication use. In addition, 61 studies excluded for comorbid-
ities also continued to use a pre-medicated sample, meaning 101 neuroimaging
studies regarding ADHD since the original Leo and Cohen study have continued
to use a pre-medicated sample. We will return to this point in the discussion. An
overview of the excluded studies is presented in Table 1.
Table 1
Studies Using Pre-Medicated Samples or Not Providing Sucient
Information to Review
Fullled Inclusion Criteria but Medicated (N=40) Insucient Information on Demographics
and Medication (N=8)
Bledsoe, Semrud–Clikeman, and Pliszka, 2011 Cheng et al., 2012 (ADHD 200)
Bledsoe, Semrud–Clikeman, and Pliszka, 2013 Colby et al., 2012 (ADHD 200)
Booth et al., 2015 Eloyan et al., 2012 (ADHD 200)
Carmona et al., 2015 Kessler et al., 20 14 (ADHD 200)
Chabernaud et al., 2012 Siqueira et al., 2014 (ADHD 200)
Courvoisie, Hooper, Fine, Kwock, and Castillo, 2004 Sripada et al., 2014 (ADHD 200)
Dias et al., 2015 Li et al., 2007
Fair et al., 2010 Wa n g e t a l . , 2 0 0 7
Fan, Gau, and Chou, 2014
Fassbender et al., 2009
Fassbender et al., 2011
Garvey et al., 2005
Li et al., 2012
Liotti, Pliszka, Perez, Kothmann, and Woldor, 2005
Lopez–Larson, King, Terry, McGlade, and Yurgelun–
To dd , 2 0 12
Ma et al., 2012
McAlonan et al., 20 09
Mostofsky, Cooper, Kater. Denckla, Kaufmann, 2002
Mostof sky et al., 2006
Muri as, Sw anson, and Srini vasan, 2006
Paloyel is, Mehta, Faraone, Ashe rson, and Kuntsi, 201 2
Peng, Li n, Zha ng, an d Wang , 2013
Pineda et al., 2002
Poiss ant, Me ndrek, and Senha dji, 2 014
(continued on next page)
MARLEY ET AL.
210
Medication Controlled Studies
e majority of the reviewed studies are underpowered: ten out of 14 studies
fell signicantly below the recommended sample size of 25 required for an accu-
rate activation map (Desmond and Glover, 2002; Fall et al., 2015; Fayed, Modrego,
Castillo, andDávila, 2007; Fernández et al., 2009; Massat et al., 2012; Murphy
and Garavan, 2004; Pueyo et al., 2003; Silk et al., 2005; Silk, Vance, Rinehart,
Bradshaw, andCunnington, 2008; Spalletta et al., 2001; Vance et al., 2007; Weber,
Lütschg, andFahnenstich, 2005). e largest sample size involved forty ADHD
children (Kim, Lee, Shin, Cho, and Lee, 2002); however, due to the ethical concern
of including healthy controls for SPECT imaging, children who had previously
received SPECT for headaches were used as controls, limiting the control group
sample to seventeen. A discrepancy in experimental group and control group
sample size was noted in an additional four studies (Fayed et al., 2007; Li, Li et al.,
2014; Massat et al., 2012; Weber et al., 2005) with three studies also underpowered
(Fayed et al., 2007; Massat et al., 2012; Weber et al., 2005).
In terms of matched control samples, all studies adequately controlled for con-
founds such as comorbidities, utilising only samples with an ADHD diagnosis.
e impact of diering brain-region activation between le and right handedness
(Cuzzocreo et al., 2009) was controlled by utilising only right-handed partici-
pants; in doing so, however, all studies inadvertently limited the external validity
(i.e., generalization) of their ndings. Further, experimental groups were insu-
ciently matched in age in the majority of studies. A number of studies tested for
dierences in age between groups, and reported no signicant dierences, but
none of the studies compared the age range of groups with the mean age of the
Posner e t al., 2011
Qiu et al., 2009
Shaw et al., 2009
Silk, Vance, Rinehart, Bradshaw, and Cunnington, 2009a
Silk, Vance, Rinehart, Bradshaw, and Cunnington, 2009b
Soliva, Fauquet et al., 2010
Soliva, Moreno et al., 2010
Sowell et al., 2003
Stevens, Pearlson, and Kiehl, 2007
Tam m , M e no n , a n d R e is s , 2 0 06
Tam m , M e no n , R i ng e l, a nd R ei s s, 2 00 4
Tian et al., 2006
Tian et al., 2008
Tom a s i an d Vo lk o w, 20 1 2
Tre m ol s e t al . , 2 00 8
Xia, Foxe, Sroubek, Branch, and Li, 2014
Table 1 (continued)
BROKEN BRAINS OR FLAWED STUDIES? 211
groups. As an example, in the study by Fayed et al. (2007), the age range of the
experimental sample was 6–16 and the control group was 4–12; there may not
be signicant dierences in the mean age of the group, but there is signicant
variation in age within and between samples, which will be discussed below. is
observation is worth considering especially in the studies with apparently less
variance in age. For example, in the study by Pueyo et al. (2003), participant ages
were not provided, but by using the standard deviation, we can calculate that
99.7% (assuming normal distribution) of the experimental group fell between
the ages of 12.6–17.58, an age range of almost 5 years, while in the control group
the age range was 3.48 years. Given that adolescence is a time period of substan-
tial changes in the brain (Dahl, 2004), not controlling for age range undermines
claims that dierences between participant groups highlight a neurological basis
for ADHD.
Finally, the various subtypes of ADHD are oen not explored. One would
assume that, given the manifestly dierent presentations between the subtypes
(ADHD-Combined and ADHD-Inattentive), dierent brain regions or systems
are involved, which would require closer matching across samples. is position
appears to be substantiated by Fair et al. (2013) who argued that, whilst the dier-
ent subtypes possess some overlapping aspects, they also display unique patterns
of atypical connectivity. However, the only study to utilise dierentiated samples
(ADHD-I and ADHD-combined) was Lei et al. (2014). Six studies only used the
ADHD-combined subtype (Fall et al., 2015; Fernández et al., 2009; Massat et al.,
2012; Silk et al., 2005, 2008; Vance et al., 2007), with the remaining seven studies
using a mixed subtype sample and not providing sucient information on which
to assess this issue. An overview of the sample characteristics of reviewed studies
can be found in Table 2.
Synthesis of Findings
According to the reviewed articles, several brain regions (frontal, parietal,
occipital, cerebellum, striatum, and basal ganglia) are associated with ADHD.
However, the brain regions and associated dysfunction implicated in the expres-
sion of ADHD are inconsistent across the reviewed studies, with the brain regions
implicated diering across dierent imaging technologies as well diering among
studies using the same imaging technology. An overview of reviewed study nd-
ings can be viewed in Table 3.
Frontal lobe. e one study to utilise structural MRI (Pueyo et al., 2003) found
that ADHD participants had a higher degree of myelination in the right frontal lobe
than did controls. Lei et al.s (2014) results suggest reduced axial and radial diu-
sivity in the le middle frontal gyrus. Spalletta et al.s (2001) nding of decreased
regional cerebral blood ow (rCBF) in the le prefrontal region seems in accor-
dance with the oxygenated and deoxygenated haemoglobin imbalance reported by
MARLEY ET AL.
212
Table 2
Breakdown of Included Study Sample Characteristics
Study Sample Description*
ADHD Control ADHD Group Constitution
Fall et al., 2015 N = 11 N = 11 11 ADHD-Combined
M/F = 10/1 M/F = 8/3
Mean age: No details Mean age: No details
SD: No details SD: No details
Range: No details Range: No details
Fayed et al., 2007 N = 22 N = 8 No details
M/F = 18/4 M/F = 4/4
Mean age: 9 Mean age: 7
SD: 2.91 SD: 3
Range: 6–16 Range: 4–12
Fernández et al., 2009 N = 14 N = 17 14 ADHD-Combined
M/F = 14/0 M/F = 17/4
Mean age: 9.64 Mean age: 10.36
SD: 1.04 SD: 1.48
Range: 8–12 Range: 8–13
Kim et al., 2002 N = 40 N = 17 No details
M/F = 32/8 M/F = 15/2
Mean age: 9.7 Mean age: 10.4
SD: 2.1 SD: 2.2
Range: 8–12 Range: 8–12
Lei et al., 2014 N = 28 N =28 Group 1: 28
(ree groups — M/F = 25/3 M/F = 25/3 ADHD-Inattentive
two experimental and Mean age: 9.3 Mean age: 9.2 Group 2: 28
one control group) SD: 1.3 SD: 1.4 ADHD-Combined
Range: No details Range: No details
N = 28
M/F = 25/3
Mean age: 9.3
SD: 1.3
Range: No details
Li, He et al., 2014 N = 33 N = 32 22 ADHD-Combined
M/F = 33/0 M/F = 32/0 11 ADHD-Inattentive
Mean age: 10.1 Mean age: 10.9
SD: 2.6 SD: 2.6
Range: 6–16 Range: 8–16
Li, Li et al., 2014 N = 33 N = 27 22 ADHD-Combined
M/F = 33/0 M/F = 27/0 11 ADHD-Inattentive
Mean age: 9.9 Mean age: 10.9
SD: 2.4 SD: 2.7
Range: 6–15 Range: 8–16
(continued on next page)
BROKEN BRAINS OR FLAWED STUDIES? 213
Massat et al., 2012 N = 19 N = 14 19 ADHD-Combined
M/F = No details M/F = No details
Mean age: 10.75 Mean age: 10.05
SD: 1.31 SD: 1.28
Range: No details Range: No details
Pueyo et al., 2003 N = 11 N = 20 No details
M/F = 8/3 M/F = 15/5
Mean age: 15.09 Mean age: 14.85
SD: 0.83 SD: 0.58
Range: 14–17 Range: No details
Silk et al., 2005 N = 7 N = 7 ADHD-Combined
M/F = 7/0 M/F = 7/0
Mean age: 14.38 Mean age: 14.56
SD: 1.85 SD: 1.77
Range: 11–17 Range: No details
Silk et al., 2008 N = 12 N = 12 12 ADHD-Combined
M/F = 12/0 M/F = 12/0
Mean age: 11.15 Mean age: 11.09
SD: 1.53 SD: 1.50
Range: No details Range: No details
Spalletta et al., 2001 N = 8 N = 8 7 ADHD-Combined
M/F = No details M/F = No details 1 ADHD-Inattentive
Mean age: 9.4 Mean age: 9.0
SD: 2.0 SD: 2.1
Range: 6–12 Range: 6–12
Vance et al., 2007 N = 12 N = 12 12 ADHD-Combined
M/F = No details M/F = No details
Mean age: 11.1 Mean age: 10.2
SD: 1.5 SD: 1.3
Range: 8–12 Range: No details
Weber et al., 2005 N = 11 N = 9 7 ADHD-Hyperactive
M/F = No details M/F = No details 4 ADHD-Inattentive
Mean age: 10.4 Mean age: 11.3
SD: 1.2 SD: 1.3
Range: No details Range: No details
* Eect sizes for the reviewed studies are not provided as it would appear that eect sizes are not
normally calculated for traditional neuroimaging studies (Reddan, Lindquist, and Wager, 2017). It
is possible to calculate eect sizes for the dierence between groups in relation to tasks performed
as part of the neuroimaging process (i.e., anker task) but this would only be for the studies that
utilised a task (not all did) and for the those that provided enough information (not all did).
Table 2 (continued)
MARLEY ET AL.
214
Weber et al. (2005). e indicated dierences in N-acetylasparate/creatine ratios in
the right prefrontal corticosubcortical regions (Fayed et al., 2007) suggest a chemi-
cal imbalance.
e majority of studies included utilised fMRI. e three studies that focused
on the frontal lobe reported general reduced activation in ADHD participants
(Kim et al., 2002; Silk et al., 2005; Vance et al., 2007). Li, He et al. (2014) found
interhemispheric dierences, with lower activation in the le orbitofrontal cortex
and the le ventral superior frontal gyrus and higher activation in the right dorsal
superior frontal gyrus for ADHD participants. e results of the Silk et al. (2005)
study indicate a lower activation in the le prefrontal cortex and superior and bilat-
eral inferior gyri and higher activation in the medial superior prefrontal cortex.
Table 3
Overview of Brain Regions Associated with ADHD
Brain Dierences in Physical Dierences in Electrical Studies
Regions Systems Activation
Frontal more myelination in right lower activation in frontal Fayed et al., 200 7
frontal lobe lobe Fernánd ez et al. , 2009
Kim et al., 2002
dierences in lower activation in right Lei et al., 2014
N-acetylaspartate/creatine lateral prefrontal cortex, Li He et al., 2014
ratios in right prefrontal bilateral orbito pref rontal Pueyo et al., 2003
corticosubcortical region cortex Silk et al., 2005
Silk et al., 2008
decreased rCBF in le dorso Spallett a et al., 200 1
lateral prefrontal cortex Van c e e t a l . , 2 0 0 7
compared to right We b e r e t a l . , 2 0 0 5
decreased radial and axial lower activation in inferior
diusivity in the le middle frontal gyrus
frontal gyrus in ADHD-I
imbalance between lower activation in le
oxygenated and deoxyge nated orbitofrontal cor tex and the
haemoglobin during the short- le ventral superior frontal
and extended-attent ion tasks gyrus and higher activation
compared to lateralized oxygen in the right dorsal superior
consumption in le prefrontal frontal gyrus
cortex in controls
lower activation in le prefrontal
cortex and in superior and
bilateral i nferior frontal g yri and
higher activation in medial superior
prefrontal cor tex
(continued on next page)
BROKEN BRAINS OR FLAWED STUDIES? 215
Parietal increased blood ow in parietal lower activation in bilateral Kim et al., 2002
lobe inferior parietal gyri Massat et al., 201 2
Silk et al., 2005
lower activation in right Silk et al., 2008
inferior parietal Van c e e t a l . , 2 0 0 7
lower activation in posterior
parietal regions: le supramarginal
gyrus, bilateral precuneus,
and inferior p arietal lobule
Occipital increased blood ow in decreased bilateral activation Kim et al., 2002
occipital lobe Lei et al., 2014
Massat et al., 2012
more myelination in le less activation in right Pueyo et al., 2003
posterior compared to right parieto-occipital areas Silk et al., 2008
(cuneus and precuneus) Van c e e t a l . , 2 00 7
increased radial diusivity in
le occipital (in ADHD-I)
Tem p ora l reduced axial diusiv ity in le temporal lobe less active Kim et al., 2002
middle temporal (in ADHD-I) Lei et al., 2014
and increased in the right middle higher activation in Li, Li et al., 2014
temporal (in ADHD-CT) right hippocampus Silk et al., 2005
Silk et al., 2008
increased radial diusivity in lower activation in right
le superior temporal gyrus middle temporal cortex
(in ADHD- I and ADHD-CT)
decreased fractional higher activation in the le
aniostrophy in le middle and superior
parahippocampal gyrus temporal gyri
(in ADHD- CT)
lower activation in bilateral
superior temporal gyr us
Striatum a nd increased axial diusivity in decreased activation in Fall et a l., 2015
Basal Ganglia the right caudate (in ADHD-I bilateral caudate nuclei Lei et al., 2014
and ADHD-CT) Li, He et al., 2014
Li, Li et al., 2014
lower activation in right Massat et a l., 2012
caudate nucleus Silk et al. , 2005
Silk et al., 2008
reduced vol ume and higher higher activation in bilateral Va n c e e t a l . , 2 0 0 7
diusivity in ADHD patients globus pallidus
compared to controls, especially
in caudate, thalamus, and putamen
Cerebellum decreased activation in Massat e t al., 2012
cerebellum Silk et al., 2008
Table 3 (continued)
MARLEY ET AL.
216
Parietal, occipital, and temporal lobes. Kim et al. (2002) reported increased blood
ow in the parietal lobe, while reduced activation was found either in the right
inferior parietal lobe (Silk et al., 2005; Vance et al., 2007) or bilaterally in inferior
parietal gyri (Massat et al., 2012; Silk et al., 2008). Silk et al. (2008) reported an
overall reduction of activation in the posterior parietal regions. Less activation was
also displayed in the bilateral occipital (Massat et al., 2012; Silk et al., 2008) and the
right parieto-occipital areas (Vance et al., 2007). Interhemispheric dierences were
found in the posterior occipital region, with a more myelinated le side (Pueyo et
al., 2003). Lei et al. (2014) reported increased radial diusivity in the le occipital
region. e temporal lobe was also found to be less active (Silk et al., 2008). Lei et
al. (2014), who compared children with ADHD-I, ADHD-CT, and healthy controls,
found reduced axial diusivity in the le middle temporal in the ADHD-I group
and in the right middle temporal in the ADHD-CT group. Kim et al. (2002) found
lower activation in the right middle temporal cortex, while Silk et al. (2005) found
higher activation in the le middle and superior temporal cortex. Higher activation
was reported in the right hippocampus (Li, Li et al., 2014; Silk et al., 2008) and
decreased fractional anisotropy in the le parahippocampal gyrus (Lei et al., 2014).
Striatum, basal ganglia, and cerebellum. Decreased activation of white matter
was found in the bilateral caudate nuclei in one study (Massat et al., 2012) and
only in the right caudate nucleus in another (Vance et al., 2007). Two other studies
found higher activation in bilateral globus pallidus (Li, He et al., 2014; Li, Li et al.,
2014). Fall et al. (2015) reported reduced volume and higher diusivity in ADHD
patients compared to controls, especially in caudate, thalamus, and putamen.
Finally, decreased activation was noted in the cerebellum (Massat et al., 2012).
Discussion
In light of the criticisms raised at the beginning of this paper, our study aimed to
update the Leo and Cohen review of pre-medicated samples in ADHD neuroimag-
ing studies. Since the original review, use of pre-medicated samples has been fully
acknowledge within the neuroimaging literature as a confound (Smith et al., 2006),
meaning studies from this point onwards must control for this variable for ndings
to be considered empirically robust. From the initial papers deemed relevant, 78
included participants with comorbidities. Admittedly, considering the frequency
of comorbid conduct disorder or oppositional deant disorder (Burke, Loeber,
and Birmaher, 2002; Ollendick, Jarrett, Grills–Taquechel, Hovey, and Wol, 2008),
participant recruitment can be challenging; however, given these comorbidities are
behaviourally indistinguishable from ADHD (National Collaborating Centre for
Mental Health, 2009), it is imperative that studies locate “pure” ADHD in order to
provide a sound basis for treatment (Wang et al., 2012).
e most surprising nding from our review was the number of studies con-
tinuing to use pre-medicated samples. From the 62 studies that met the inclusion
BROKEN BRAINS OR FLAWED STUDIES? 217
criteria, 40 continued to use pre-medicated samples. In addition to the 40 stud-
ies that met the inclusion criteria, the 61 studies that excluded for comorbidities
continued to use pre-medicated samples. is nding is perplexing considering
the obviousness of the confound and its recognition as such in the neuroimaging
literature. e most frequently oered rationale for inclusion was that all par-
ticipants were withheld from medication treatment for a seemingly arbitrary
24, 48, or three-day period. However, there is no comprehensive study to date
that oers an authoritative time-period aer which a participant can be con-
sidered medication-free. Until then, studies claiming to include medication-free
participants, based on hours of withheld medication, introduce the possibility of
withdrawal symptoms as an additional problem (Leo, 2004).
A further issue was the number of studies that ignored this serious confound,
with eleven studies not mentioning participant medication status (Cheng, Ji,
Zhang, and Feng, 2012; Colby et al., 2012; Eloyan et al., 2012; Kessler, Angstadt,
We ls h , an d Sr i p a da , 2 01 4 ; K i m et a l ., 2 01 0 ; Li e t a l . , 2 0 0 7; S i qu e i r a, B ia z o l i, C o m-
fort, Rohde, and Sato, 2014; Sripada Kessler, and Angstadt, 2014; Wang et al.,
2007, 2009; Wellington, Semrud–Clikeman, Gregory, Murphy, and Lancaster,
2006). Six of these studies used their application of the ADHD 200 database as
a rationale for insucient demographic details (including medication informa-
tion) [Cheng et al., 2012; Colby et al., 2012; Eloyan et al., 2012; Kessler et al.,
2014; Siqueira et al., 2014; Sripada et al. 2014], despite the ADHD 200 agreement
stating that the specic datasets included in analyses be specied appropriately”
(Milham, Fair, Mennes, and Motofsky, 2012), placing the onus on researchers
to provide the demographic information for their sample. Given the problem
of using a pre-medicated sample, and the resulting impact on the reliability and
validity of the ndings, we did not feel these papers warranted further analysis,
leaving only 14 study ndings to be extrapolated and considered in more detail.
A further major methodological limitation that aected the reviewed stud-
ies was small sample sizes, with more than half the studies (8/14) using sample
sizes below 15, which limits power, and increases the likelihood of type I errors
(Murphy and Garavan, 2005). Jennings and Van Horn (2012) raised this issue in
their review of publication bias in neuroimaging research. Due to the number of
studies with small sample sizes, Jennings and Van Horn expected to nd a large
number of studies supporting the null hypothesis or reporting non-signicant
ndings due to lack of power, but this was not the case. e conclusion oered
by Jennings and Van Horn was that this indicated publication bias towards posi-
tive results. However, given the lack of power of these studies, how these positive
results were achieved in the rst-place warrants consideration. To answer this, one
needs to look to the inuence of confounding variables, such as age dierences
between samples (Ioannidis, 2011), undierentiated disorder subtypes in the
experimental group (Fair et al., 2013), the considerable age range of the sample,
multiple comparison statistical correction procedures (Bennett, Wolford, and
MARLEY ET AL.
218
Miller, 2009), and untested statistical procedures within fMRI soware (Eklund,
Nichols, and Knutsson, 2016).
e inuence of age range on results in neuroimaging studies was highlighted
in the original Leo and Cohen review as problematic, as results could indicate the
inuence of maturation on the brain. Our review took this important point fur-
ther, noting that age range varied considerably, with some studies having as large
as a ten-year gap between the youngest and oldest child. As neuroimaging studies
of typical development have indicated age-specic dierences in gray and white
matter (Sowell et al., 1999; Sowell, Trauner, Gamst, and Jernigan, 2002), the large
variance in ages between and within participant groups raises the possibility that
the positive results represent the considerable neurological growth that occurs
during childhood rather than ADHD related abnormalities (Giedd et al., 1999;
Samanez–Larkin and D’Esposito, 2008).
Two areas of increasing concern in neuroimaging studies are the ination
of false positive results through fMRI soware increasing false positive rates
(Eklund et al., 2016) and the lack of application of methods for correcting for
multiple comparisons (Bennett et al., 2009). In the Eklund et al. study, the authors
examined three soware packages containing applied procedures for correcting
multiple comparisons when using real data. Prior to this point, imaging soware
was validated using simulated data. Eklund et al. found that the Familywise Error
(FEW) corrected cluster p-values approach — the most commonly used approach
for controlling the chance false positives results — inated statistical signicance.
e application of a correction for multiple comparisons is a necessity in imaging
studies due to the mass univariate approach used to create activation maps result-
ing in numerous statistical comparisons (Woo, Krishnan, and Wager, 2014). e
FEW approach corrects at the level of the voxel, the unit of measurement used
to indicate a collection of brain cells, with each voxel p-value measured against
an arbitrarily set threshold of signicance, before being combined as a cluster of
voxels that form anatomical areas of interest. Woo et al. argued that the uncor-
rected FEW approach inates the chances of “physiological noise” being included
as voxels, rendering positive ndings “useless” due to a lack of spatial specicity
(2014, p. 418). Similarly, Bennett et al. (2009) raised concern about the use of
arbitrary, uncorrected statistical thresholds in many fMRI studies, citing the voxel
clustering correction as particularly problematic. Bennett et al. oered several
approaches for managing the risk of inated false positive results from multiple
comparisons. From the 14 studies reviewed in our study, we located only four
studies (Fernández et al., 2009; Li, Li et al., 2014; Massat et al., 2012; Spalletta et
al., 2001) applying one of the recommended approaches. We were also unable to
nd information on the statistical soware packages used across the 14 reviewed
studies. Two of the studies (Silk et al., 2008; Vance et al., 2007) reported corrected
cluster p-values, however, which could indicate use of the problematic soware
highlighted by Eklund et al. (2016). Taken together, eight of our reviewed studies
BROKEN BRAINS OR FLAWED STUDIES? 219
did not mention the correction procedure applied, two applied procedures known
to inate false positive results, with only four applying recommended correction
procedures. ree of the four studies applying recommended correction pro-
cedures are below the recommended sample size to be considered adequately
powered (Fernández et al., 2009; Massat et al., 2012; Spalletta et al., 2001) with
the nal study (Li, Li et al., 2014) displaying a discrepancy between experimental
group and control group sample size.
A nal area of weakness found in our reviewed studies was the range of methodo-
logy and technology utilised and the huge disparity in regions of the brain impli-
cated in the disorder’s expression. For instance, across the articles there were
considerable variations in the statistical thresholds or the strength of the imaging
magnet (Paloyelis, Mehta, Kuntsi, and Asherson, 2007). A further example is the
application of the region of interest (ROI) approach. e ROI approach encourages
the investigation of localised brain regions through statistical mapping that high-
lights voxels that are more strongly activated during one condition over another
(Kriegeskorte, Simmons, Bellgowan, and Baker, 2009). In this approach, neuro-
imaging data are rst analysed to select a subset, the region of interest, followed
by a selective analysis of the subset. In most cases, the region of interest is dened
by statistical mapping and the subsequent analysis is based on the same data. e
approach, termed double-dipping, has been criticised for increasing the risk of
distorted results through violating the assumptions of random sampling, distort-
ing descriptive statistics, and invalidating statistical inferences (Kriegeskorte et al.,
2009; Vul, Harris, Winkielman, and Pashler, 2009). e approach also appears to
be wide-spread and tacitly accepted in the neuroimaging community. In one anal-
ysis of 134 studies published in ve prestigious journals, the practice was found to
aect 42% of studies, with a further 14% not providing enough information to rule
out the use of the practice (Kriegeskorte et al., 2009).
Despite the large disparity in brain regions cited across the literature, the
region most frequently suggested as connected to ADHD expression was the
frontal lobe or, more specically, the prefrontal cortex (Kim et al., 2002; Silk et
al., 2005; Spalletta et al., 2001). Dierences in activation observed in fMRI studies
would seem to support the abnormal functioning of the frontal lobe, yet there
was great variation in the more specic areas reported as well as in the lateral-
ity (e.g., lower activation in the inferior frontal gyrus and higher activation in
the medial superior prefrontal cortex). us, it is possible that the inconsistent
results indicate inter-sample variance rather than ADHD markers per se. Another
region implicated by the reviewed studies was the prefrontal cortex. Consider-
ing the localisation of executive functioning, which is widely acknowledged as
problematic in those diagnosed with ADHD (Barkley, 2010; Koer, Rapport,
Bolden, Sarver, and Raiker, 2010; Scheres et al., 2004), a potential dysfunction in
this region is plausible. While this explanation seems straightforward and com-
prehensive, issues of conrmation bias urge caution, as the majority of studies
MARLEY ET AL.
220
demonstrating frontal lobe dysfunction conned their focus to this region during
scanning, in line with the behavioural evidence linking ADHD with executive
functioning. However, by limiting scan areas, according to Murphy and Garavan
(2005), the studies are increasing the risk of achieving results due to Type I errors,
possible through the problematic procedure of double-dipping outlined above.
In the more recent trend of whole-head imaging, increasing areas of brain
regions of those diagnosed with ADHD are considered to display volumetric or
functional irregularities, complicating the understanding of the underlying neuro-
biology. As a result, multiple alternative explanations have been formed, with
dysfunction being linked to an array of interconnected brain regions and net-
works; for example, the fronto-striatal network (Castellanos, 1997; Durston, 2003;
Giedd et al., 2001), which, incidentally, appears also to be oered as a neurological
explanation for obsessive–compulsive behaviours (Melloni, Urbistondo, Sedeño,
Gelormini, Kichic, and Ibanez, 2012). However, with no set image of a baseline
“normal” brain, what are considered volumetric abnormalities may represent
individual dierences, or the impact on the brain of multiple environmental
variables, rather than holistic dierences between populations. For example, the
impact of previous drug treatments, previous inpatient status, or dierences in
age and/or gender, parameters which are dicult to control eciently, have been
connected to volumetric dierences (Ioannidis, 2011).
A major individual dierence that appears not to have been considered by
any of our reviewed studies was temperament, which has been shown to produce
dierent activation maps across temperamental styles (Bierzynska et al., 2016;
Crone and Elzinga, 2015; Fox, Henderson, and Marshall, 2001; Henderson and
Wa ch s , 2 0 0 7 ; N i gg , 2 0 06 ; St a d l e r e t a l . , 2 0 0 7) . As s u ch , t h e i m pa c t o f t em p e r a -
ment on activation maps has been indicated as an important consideration for
future imaging studies (Bierzynska et al., 2016). Additionally, the wide range of
analytical techniques utilised across imaging studies make the consolidation of
these multiple ndings more dicult. Even if there was consistency in the abnor-
malities found, directionality cannot be assumed, as these measures may simply
reect reactivity variations (Bierzynska et al., 2016).
Conclusion
It appe ars t hat pr emature conc lusions re gardi ng t he ne urobi olog ical unde r-
pinning of ADHD are still being reported as hard scientic evidence. e original
review by Leo and Cohen (2003) asked important questions of the neuroimaging
evidence for ADHD, thus the continued presence of the same methodological limi-
tations and the continued lack of consistent ndings in recent imaging studies is
rather concerning. It may be that the technical nature of neuroimaging studies, and
the highly scientic language associated with the techniques, deects critical conside-
ration. is specic issue has led one critic to coin the term “biobabble” to describe
BROKEN BRAINS OR FLAWED STUDIES? 221
the language of ADHD neuroimaging studies (Timimi, 2005). Given the side eects
associated with the ADHD treatment sanctioned by neurobiological explanations,
it is of utmost importance that neuroimaging studies consider their methodological
limitations as pointed out in the original Leo and Cohen review, and that future
neuroimaging studies continue to be critically examined to ensure their adequacy.
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