Topological Data Analysis Reveals Robust Alterations in the Whole-Brain and Frontal Lobe Functional Connectomes in Attention-Deficit/Hyperactivity Disorder
Por:
Gracia-Tabuenca, Zeus, Carlos Diaz-Patino, Juan, Arelio, Isaac, ALCAUTER, SARAEL
Publicada:
1 may 2020
Resumen:
Attention-deficit/hyperactivity disorder (ADHD) is a developmental
disorder characterized by difficulty to control the own behavior.
Neuroimaging studies have related ADHD with the interplay of
fronto-parietal attention systems with the default mode network (DMN;
Castellanos and Aoki, 2016). However, some results have been
inconsistent, potentially due to methodological differences in the
analytical strategies when defining the brain functional network, i.e.,
the functional connectivity threshold and/or the brain parcellation
scheme. Here, we make use of topological data analysis (TDA) to explore
the brain connectome as a function of the filtration value (i.e., the
connectivity threshold), instead of using a static connectivity
threshold. Specifically, we characterized the transition from all nodes
being isolated to being connected into a single component as a function
of the filtration value. We explored the utility of such a method to
identify differences between 81 children with ADHD (45 male, age:
7.26-17.61 years old) and 96 typically developing children (TDC; 59
male, age: 7.17-17.96 years old), using a public dataset of resting
state (rs)fMRI in human subjects. Results were highly congruent when
using four different brain segmentations (atlases), and exhibited
significant differences for the brain topology of children with ADHD,
both at the whole-brain network and the functional subnetwork levels,
particularly involving the frontal lobe and the DMN. Therefore, this is
a solid approach that complements connectomics-related methods and may
contribute to identify the neurophysio-pathology of ADHD.
Filiaciones:
Gracia-Tabuenca, Zeus:
Univ Nacl Autonoma Mexico, Inst Neurobiol, Queretaro 76230, Mexico
Carlos Diaz-Patino, Juan:
Univ Nacl Autonoma Mexico, Inst Matemat, Queretaro 76230, Mexico
Arelio, Isaac:
Univ Nacl Autonoma Mexico, Inst Matemat, Queretaro 76230, Mexico
ALCAUTER, SARAEL:
Univ Nacl Autonoma Mexico, Inst Neurobiol, Queretaro 76230, Mexico
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