Forschung
Research Papers
New tools for diagnosis and modulation of brain dysfunction
Suppose a boy comes to your door. His behavior looks like typical ADHD: he is extremely inattentive, impulsive and hyperactive. He performs poorly in continuous performance tasks.
Recent research in neurophysiology of ADHD shows that there are several reasons why the boy behaves in this way:
- a patient may have a focus in his cortex , which without any overt symptoms of epilepsy impairs information processing and, consequently, mimics attention deficit (see Aldenkamp, Arends, 2004);
- a patient may have a lack of overall cortical activation due to dysfunction of the ascending reticular system of the brain stem (Sergeant , 2000);
- a patient may have genetically determined hyperactive frontal lobes (Clarke et al., 2003);
- a patient may have dysfunction of the prefrontal-striato-thalamic system due to structural abnormality (Silk et al., 2009; Busch et al., 2005; Castellanos et al., 1996); or increase of dopamine reuptake dopamine transporters in the striatum (Krause et al., 2003)
- a patient may have hypoactivation of the premotor cortex of the brain, which is compensated by increase of motoric activity (Simmonds et al., 2007);
- a patient may have dysfunctioning in the anterior gyrus cingulus which produces anxiety, emotional instability and hyperactivation (Albrecht et al., 2008).
Articles
Classification of ADHD patients on the basis of independent ERP components using a machine learning system
Nonlinear Biomedical Physics 2010, 4 (Suppl 1):S1
Background: In the context of sensory and cognitive-processing deficits in ADHD patients, there is considerable evidence of altered event related potentials (ERP). Most of the studies, however, were done on ADHD children. Using the independent component analysis (ICA) method, ERPs can be decomposed into functionally different components. Using the classification method of support vector machine, this study investigated whether features of independent ERP components can be used for discrimination of ADHD adults from healthy subjects.
Methods: Two groups of age- and sex-matched adults (74 ADHD, 74 controls) performed a visual two stimulus GO/NOGO task. ERP responses were decomposed into independent components by means of ICA. A feature selection algorithm defined a set of independent component features which was entered into a support vector machine.
Results: The feature set consisted of five latency measures in specific time windows, which were collected from four different independent components. The independent components involved were a novelty component, a sensory related and two executive function related components. Using a 10-fold cross-validation approach, classification accuracy was 92%.
Conclusions: This study was a first attempt to classify ADHD adults by means of support vector machine which indicates that classification by means of non-linear methods is feasible in the context of clinical groups. Further, independent ERP components have been shown to provide features that can be used for characterizing clinical populations.