Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jun 28:16:952067.
doi: 10.3389/fnins.2022.952067. eCollection 2022.

Individual Brain Morphological Connectome Indicator Based on Jensen-Shannon Divergence Similarity Estimation for Autism Spectrum Disorder Identification

Ting Yi  1 , Weian Wei  1 , Di Ma  2 , Yali Wu  3 , Qifang Cai  1 , Ke Jin  1 , Xin Gao  4
Affiliations

Individual Brain Morphological Connectome Indicator Based on Jensen-Shannon Divergence Similarity Estimation for Autism Spectrum Disorder Identification

Ting Yi et al. Front Neurosci. .

Abstract

Background: Structural magnetic resonance imaging (sMRI) reveals abnormalities in patients with autism spectrum syndrome (ASD). Previous connectome studies of ASD have failed to identify the individual neuroanatomical details in preschool-age individuals. This paper aims to establish an individual morphological connectome method to characterize the connectivity patterns and topological alterations of the individual-level brain connectome and their diagnostic value in patients with ASD.

Methods: Brain sMRI data from 24 patients with ASD and 17 normal controls (NCs) were collected; participants in both groups were aged 24-47 months. By using the Jensen-Shannon Divergence Similarity Estimation (JSSE) method, all participants's morphological brain network were ascertained. Student's t-tests were used to extract the most significant features in morphological connection values, global graph measurement, and node graph measurement.

Results: The results of global metrics' analysis showed no statistical significance in the difference between two groups. Brain regions with meaningful properties for consensus connections and nodal metric features are mostly distributed in are predominantly distributed in the basal ganglia, thalamus, and cortical regions spanning the frontal, temporal, and parietal lobes. Consensus connectivity results showed an increase in most of the consensus connections in the frontal, parietal, and thalamic regions of patients with ASD, while there was a decrease in consensus connectivity in the occipital, prefrontal lobe, temporal lobe, and pale regions. The model that combined morphological connectivity, global metrics, and node metric features had optimal performance in identifying patients with ASD, with an accuracy rate of 94.59%.

Conclusion: The individual brain network indicator based on the JSSE method is an effective indicator for identifying individual-level brain network abnormalities in patients with ASD. The proposed classification method can contribute to the early clinical diagnosis of ASD.

Keywords: autism spectrum disorder; global metric; identification; individual brain morphological connectome; nodal metric.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor ZW declared a past co-authorship with the author XG.

Figures

FIGURE 1
FIGURE 1
Data processing and analysis.
FIGURE 2
FIGURE 2
The most consensus connections. The arc thickness indicates the discriminative power of an edge, which is inversely proportional to the estimated P-values.
FIGURE 3
FIGURE 3
The ROC results of different methods. C, morphological connectivity; G, global metric; N, nodal metric; TPR, true positive rate; FPR, false positive rate.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Abbott A. E., Linke A. C., Nair A., Jahedi A., Alba L. A., Keown C. L., et al. (2018). Repetitive behaviors in autism are linked to imbalance of corticostriatal connectivity: a functional connectivity MRI study. Soc. Cogn. Affect. Neurosci. 13 32–42. 10.1093/scan/nsx129 - DOI - PMC - PubMed
    1. Belmonte M. K., Allen G., Beckel-Mitchener A., Boulanger L. M., Carper R. A., Webb S. J. (2004). Autism and abnormal development of brain connectivity. J. Neurosci. 24 9228–9231. 10.1523/JNEUROSCI.3340-04.2004 - DOI - PMC - PubMed
    1. Ben-Sasson A., Podoly T. Y. (2017). Sensory over responsivity and obsessive compulsive symptoms: a cluster analysis. Compr. Psychiatry 73 151–159. 10.1016/j.comppsych.2016.10.013 - DOI - PubMed
    1. Busler J. N., Yanes J. A., Bird R. T., Reid M. A., Robinson J. L. (2019). Differential functional patterns of the human posterior cingulate cortex during activation and deactivation: a meta-analytic connectivity model. Exp. Brain Res. 237 2367–2385. 10.1007/s00221-019-05595-y - DOI - PMC - PubMed
    1. Carper R. A., Courchesne E. (2005). Localized enlargement of the frontal cortex in early autism. Biol. Psychiatry 57 126–133. 10.1016/j.biopsych.2004.11.005 - DOI - PubMed

LinkOut - more resources