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Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards.

ABSTRACT: OBJECTIVES:Autism spectrum disorders (ASD) are diagnosed based on early-manifesting clinical symptoms, including markedly impaired social communication. We assessed the viability of resting-state functional MRI (rs-fMRI) connectivity measures as diagnostic biomarkers for ASD and investigated which connectivity features are predictive of a diagnosis. METHODS:Rs-fMRI scans from 59 high functioning males with ASD and 59 age- and IQ-matched typically developing (TD) males were used to build a series of machine learning classifiers. Classification features were obtained using 3 sets of brain regions. Another set of classifiers was built from participants' scores on behavioral metrics. An additional age and IQ-matched cohort of 178 individuals (89 ASD; 89 TD) from the Autism Brain Imaging Data Exchange (ABIDE) open-access dataset (http://fcon_1000.projects.nitrc.org/indi/abide/) were included for replication. RESULTS:High classification accuracy was achieved through several rs-fMRI methods (peak accuracy 76.67%). However, classification via behavioral measures consistently surpassed rs-fMRI classifiers (peak accuracy 95.19%). The class probability estimates, P(ASD|fMRI data), from brain-based classifiers significantly correlated with scores on a measure of social functioning, the Social Responsiveness Scale (SRS), as did the most informative features from 2 of the 3 sets of brain-based features. The most informative connections predominantly originated from regions strongly associated with social functioning. CONCLUSIONS:While individuals can be classified as having ASD with statistically significant accuracy from their rs-fMRI scans alone, this method falls short of biomarker standards. Classification methods provided further evidence that ASD functional connectivity is characterized by dysfunction of large-scale functional networks, particularly those involved in social information processing.

SUBMITTER: Plitt M

PROVIDER: S-EPMC4309950 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards.

Plitt Mark M Barnes Kelly Anne KA Martin Alex A

NeuroImage. Clinical 20141224

<h4>Objectives</h4>Autism spectrum disorders (ASD) are diagnosed based on early-manifesting clinical symptoms, including markedly impaired social communication. We assessed the viability of resting-state functional MRI (rs-fMRI) connectivity measures as diagnostic biomarkers for ASD and investigated which connectivity features are predictive of a diagnosis.<h4>Methods</h4>Rs-fMRI scans from 59 high functioning males with ASD and 59 age- and IQ-matched typically developing (TD) males were used to ...[more]

PMID: 25685703

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Project description:Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75% specificity for a total accuracy of 79% (P = 1.1 × 10(-7)). In subjects <20 years of age, the classifier performed at 89% accuracy (P = 5.4 × 10(-7)). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71% accuracy (91% accuracy for subjects <20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, particularly for long connections (Euclidean distance >10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.

Project description:Background: Nuclear factor interleukin 3 (NFIL3) mainly focuses on the regulation of the circadian rhythm and immune system. However, the potential role of NFIL3 in human cancers has not been studied extensively. Methods: We retrieved original data from the TCGA, TARGET, and GTEx datasets via the UCSC Xena browser (http://genome.ucsc.edu/) and integrated them using R version 3.6.4. NFIL3 expression was assessed using resources such as UCSC, GEPIA (http://gepia.cancer-pku.cn/), Kaplan-Meier Plotter (KM Plotter; https://kmplot.com/), and the Human Protein Atlas (HPA; https://www.proteinatlas.org/) databases. To investigate the prognostic implications of NFIL3, we utilized GEPIA, Kaplan-Meier Plotter, and PrognoScan (http://www.abren.net/PrognoScan/) datasets. For a comprehensive analysis across multiple cancer types, we employed pan-cancer data from UCSC, examining associations between NFIL3 expression and genomic heterogeneity, tumor mutational burden (TMB), microsatellite instability (MSI), tumor purity, and neoantigens. Furthermore, we explored the relationships between NFIL3 expression and the infiltration of immune cells and the expression of immune checkpoint genes. In the context of ovarian cancer, we validated the expression and functional relevance of NFIL3. Cell Counting Kit 8 (CCK8) assays were conducted to assess cell proliferation, while scratch and transwell assays were employed to evaluate cell migration capabilities. We further examined the interaction between NFIL3 and the p53 signaling pathway through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot analysis, immunofluorescence confocal, and Coimmunoprecipitation (Co-IP) assays. Results: In general, NFIL3 expression in cancerous tissues exhibited diminished levels when compared to normal tissue samples. Notably, NFIL3 expression demonstrated a robust correlation with several pivotal aspects, including prognosis, immune cell infiltration, immune checkpoint-related genes, TMB, MSI, tumor purity, and the presence of neoantigens. Experimental investigations involving scratch assays, transwell assays, and assessments of cell proliferation in ovarian cancer cells have provided indications that NFIL3 may exert influence over cell migration and proliferation processes. Moreover, a substantial association between NFIL3 and the p53 signaling pathway was discerned through Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, with subsequent validation through qRT-PCR, Western blot analysis, immunofluorescence confocal, and co-immunoprecipitation (Co-IP) assays. Conclusions: Therefore, we concluded NFIL3 may serve as a possible prognostic and immunological pan-cancer biomarker.

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Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards.

Publications

Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards.

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