1. Biomarker for Brain Maturation
The authors have declared no conflicts of interest.
Biomarker for Brain Maturation
Halla Helgadóttir1, Ólafur Ó Guðmundsson2, Páll Magnússon2, Berglind Brynjólfsdóttir2, Gísli Baldursson2, Guðrún B Guðmundsdóttir2, Ásdís L Emilsdóttir1, Gísli H Jóhannesson1, Nicolas P Blin1, Paula Newman1 and Kristinn Johnsen1.
1) MentisCura Diagnostics, Laugavegi 176, 105 Reykjavík, Iceland.
2) Department of Child and Adolescent Psychiatry, Landspitali University Hospital , Dalbraut 12, 105 Reykjavík, Iceland.
The aim of this study is to develop a standardized tool for the evaluation of brain development differences in children using a simple EEG recording, thus bringing an objective aspect into the diagnostic of neurodevelopmental disorders. a) b)
EEG Age-Index
EEG ADHD-Index
Using Statistical Pattern Recognition (SPR) we find the EEG features that distinguish between ADHD and controls. The aim is to create a tool to diagnose ADHD. The classifier returns an ADHD index with a value between 0 (non-ADHD) and 1 (ADHD). EEGs of children in the age of show very good separation, while the accuracy of the diagnoses is lower in younger children.
NRM
Normal development in the EEG age index as a function of age follows the blue line as a person gets older. Each subject is represented by a blue dot. A blue development line gives an indication of how a normal development in the EEG age index should progress.
ADHD
Control
ADHD index
Age years
Age years
Age years
Age years
ADHD group is heterogeneous: large overlap. Accuracy = 78%
A part of controls is similar to ADHD. Accuracy = 76%
Small overlap, ADHD somewhat heterogeneous . Accuracy = 84%
Almost no overlap, very good separation. Accuracy = 92%
Incidence frequency
When the same index is calculated for ADHD subjects of the database (shown in red), it is evident that the development of individuals belonging to this group is quite different from the control group. The ADHD group shows a delay in the EEG brain maturity compared to controls, which increases with age. The EEG brain development in normal children can be used to screen for developmental differences in children.
ADHD
NRM
Background
There are significant changes in power spectral frequencies with development of children. MRI studies have demonstrated difference in brain development of children with Attention Deficit Hyperactivity Disorder (ADHD) and found a delay rather than a deviance of normal brain maturation (Shaw et al. Proc Natl Acad Sci. USA, 2007, 104(49): ).
Methods
The continuous scalp EEG was recorded on 216 control children and 150 ADHD children age 6-13, while the children rested with their eyes closed. The ADHD diagnostic was confirmed using the semi structured parent interview K-SADS. Statistical Pattern Recognition (SPR) was applied to the data in order to determine:
which features of the EEG signal change with age.
which features of the EEG signal separate the EEGs of the ADHD group and the control group the most.
Conclusion
The EEG Age-index can serve as a useful screening tool for differences in development in children. The ADHD-index can separate the EEGs of ADHD children and controls with 76%-92%accuracy, depending on age. EEG is easy to use, accessible and non-invasive and the recording takes only 5 minutes. It meets the need for objective diagnosis of neurodevelopmental disorders and has the potential of becoming an instrument measuring the effect of different treatment modalities.
Mentis Cura Module uses EEG to understand physical features and thereby bring a non-subjective aspect into the diagnostic of developmental disorders in children. EEG is the recording of the electrical activity from the brain, that is the result of electrochemical signalling between neurons. These electrochemical signals travel through the brain and skull, and are recorded by electrodes applied directly to the scalp.
Contact information: Halla Helgadóttir, MentisCura Diagnostics:

2. Biomarker for ADHD in Children
The authors have declared no conflicts of interest.
Biomarker for ADHD in Children
Ólafur Ó Guðmundsson2, Halla Helgadóttir1, Páll Magnússon2, Berglind Brynjólfsdóttir2, Gísli Baldursson2, Guðrún B Guðmundsdóttir2, Ásdís L Emilsdóttir1,, Nicolas P Blin1, Paula Newman1, Kristinn Johnsen1 and Gísli H Jóhannesson1.
1) Mentis Cura, Research and development company, Laugavegi 176, 105 Reykjavík, Iceland.
2) Department of Child and Adolescent Psychiatry, Landspitali University Hospital , Dalbraut 12, 105 Reykjavík, Iceland.
The aim of this study is to develop an electrophysiological biomarker for ADHD using EEG with the purpose of aiding clinicians in the diagnostic process of ADHD. Furthermore, the effect of methylphenidate (MPH) on EEG is investigated.
ADHD(IA) vs. ADHD(C)
NRM vs. ADHD Index
Accuracy: 80%;
Sensitivity: 0.81 ± 0.03; Specificity: 0.80 ± 0.04
Good separation between ADHD and normal group
Accuracy: 80%
Sensitivity: 0.76 ± 0.06; specificity: 0.85 ± 0.06
Good separation between ADHD(IA) and ADHD(C)
AUC=0.85
AUC=0.86
NRM-ADHD EEG Index
# Individuals
Figure 1a. A ROC curves representing one of the ten classifiers in the NRM-ADHD system. According to ten-fold cross validation there is good separation between the NRM group and the ADHD group, the average AUC is 0.85 using the data from ten ROC curves. Classifying the NRM and ADHD groups using this system illustrates the separation of the two groups.
Figure 2. A ROC curve representing one of the ten classifiers in the ADHD(IA/predominatly inattentive) and ADHD(C/combined type) system. According to ten-fold cross validation there is good separation between the ADHD(IA) group and ADHD(C) group the average AUC is 0.86 using the data from ten ROC curves.
Figure 1b. The results of the classification of the training sets in the NRM-ADHD classification system.
<50: EEG is more simular to the ADHD group
>50: EEG is more simular to the Normal group
7 Norm-like ADHD individuals
MPH effect on ADHD-NRM Index
If the index value is in the ADHD region (<50) before MPH administration it is more likely that MPH will increase the index. If the value is in the NRM region (>50) it more likely that will decrease the index.
An average shift in the EEG towards normal is observed after methylphenidate administration.
All follow-up data indicates that normlike ADHD individuals do not respond to methylphenidate treatment.
NRM-ADHD EEG index
NRM
# Individuals
3 weeks follow up
6 years follow up
significant
increase
decrease
insignificant
change
Subjects
No Med 4 3 1 2
No Med 7 6 5
No Med
ADHD/Norm Index
???
No Med
Atomoxetine
Atomoxetine
Figure 3a. The individual effect of Methylphenidate administration on the NRM-ADHD EEG index in the ADHD group.
Figure 3b. The ADHD group effect of MPH administration on the NRM-ADHD EEG index. There is a general shift towards higher index values.
Figure 4. Seven norm-like ADHD individuals (>50 on the ADHD-NRM Index). All the ADHD children started methylphenidate treatment at the beginning of the trial. Follow up showed that 2 of the 7 individuals stopped medication treatment within 3 weeks. In 6 years follow-up two more had stopped medication treatment, two had switched from methylphenidate to atomoxetine and there is no information about one individual.
Introduction
Current diagnostic assessment for ADHD relies primarily on observations of the child’s behaviour, as reported by parents and schoolteachers. By the discovery of an underlying CNS dysfunction in individuals with ADHD, the need for a biomarker screening test became well recognized. Mentis Cura is a research and development company in Iceland, which in collaboration with the Icelandic National University Hospital, is developing a diagnostic biomarker for ADHD and other developmental disorders.
Methods
33 medication naïve ADHD boys in the age of 6-8, and 27 healthy age matched healthy control boys, were recruited in this study. A 3 minute resting EEG was recorded. A total of 520 EEG features are extracted from each EEG. Statistical pattern recognition (SPR) was applied to the data in order to determine which features of the EEG signal best separate the groups. In addition, the ADHD group was divided into ADHD(IA, inattentive) (n=17) and ADHD(C, combined) (n=16) groups to investigate the difference between the groups with respect to EEG using SPR. Finally, the same technique is applied to the ADHD group before and after MPH administration, i.e. ADHD vs. ADHD(MPH).
Conclusion
The results suggest that EEG can become an important part in the clinical workup of ADHD. Not only to aid in the diagnosis of ADHD but also to determine which of the subgroups the corresponding ADHD individual belongs to, inattentive or combined. In addition this methodology may be applied to predict which ADHD individuals are likely to respond favorably to MPH treatment.
EEG is easy to use, accessible and non-invasive and the recording takes only 5 minutes. It meets the need for objective diagnosis of neurodevelopmental disorders and has the potential of becoming an instrument measuring the effect of different treatment modalities.
Mentis Cura Module uses EEG to understand physical features and thereby bring a non-subjective aspect into the diagnostic of developmental disorders in children. EEG is the recording of the electrical activity from the brain, that is the result of electrochemical signalling between neurons. These electrochemical signals travel through the brain and skull, and are recorded by electrodes applied directly to the scalp.
Contact information: Halla Helgadóttir, MentisCura Diagnostics: