2020
DOI: 10.1371/journal.pone.0241991
|View full text |Cite
|
Sign up to set email alerts
|

Diurnal variation of motor activity in adult ADHD patients analyzed with methods from graph theory

Abstract: Attention-deficit /hyperactivity disorder (ADHD) is a common neurodevelopmental syndrome characterized by age-inappropriate levels of motor activity, impulsivity and attention. The aim of the present study was to study diurnal variation of motor activity in adult ADHD patients, compared to healthy controls and clinical controls with mood and anxiety disorders. Wrist-worn actigraphs were used to record motor activity in a sample of 81 patients and 30 healthy controls. Time series from registrations in the morni… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

2
23
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
4
1

Relationship

4
1

Authors

Journals

citations
Cited by 6 publications
(25 citation statements)
references
References 68 publications
2
23
0
Order By: Relevance
“…When changing the values of k , different similarity graphs are obtained, and different results are to be expected. 23 However, the results of the k = 5 neighborhood analyses were quite similar to the k = 2 results for both the 20-hour and the 120-minute evening sequences. The morning sequences only resulted in differences in edges and missing edges.…”
Section: Discussionsupporting
confidence: 52%
See 4 more Smart Citations
“…When changing the values of k , different similarity graphs are obtained, and different results are to be expected. 23 However, the results of the k = 5 neighborhood analyses were quite similar to the k = 2 results for both the 20-hour and the 120-minute evening sequences. The morning sequences only resulted in differences in edges and missing edges.…”
Section: Discussionsupporting
confidence: 52%
“…In this paper, we apply the nonlinear similarity graph algorithm which is based on work done by Lacasa et al, 34 and has been comprehensively described. 22,23 This algorithm transforms a time series S=(x 1 , x 2 , … x n ) into an undirected similarity graph G. Each element of time series S corresponds to a node u in V= {1, 2, … n} and each node u is assigned a weight equal to the value of x u. The distance between two nodes u and v is |u-v| and when the distance is 1, the two nodes u and v are defined as direct neighbors.…”
Section: Methodsmentioning
confidence: 99%
See 3 more Smart Citations