Abstract:Purpose To identify cerebral radiomic features related to diagnosis and subtyping of attention deficit hyperactivity disorder (ADHD) and to build and evaluate classification models for ADHD diagnosis and subtyping on the basis of the identified features. Materials and Methods A consecutive cohort of 83 age- and sex-matched children with newly diagnosed and never-treated ADHD (mean age 10.83 years ± 2.30; range, 7-14 years; 71 boys, 40 with ADHD-inattentive [ADHD-I] and 43 with ADHD-combined [ADHD-C, or inatten… Show more
“…Present study particularly adds to psychoradiology, an evolving specialty as opposed to neuroradiology, mainly for psychiatric and psychological brain (Lui et al, 2016;Kressel et al, 2017;Sun et al, 2018;Port, 2018). …”
To psychoradiologically investigate the topological organization of single-subject gray matter networks in patients with PTSD. Eighty-nine adult PTSD patients and 88 trauma-exposed controls (TEC) underwent a structural T1 magnetic resonance imaging scan. The single-subject brain structural networks were constructed based on gray matter similarity of 90 brain regions. The area under the curve (AUC) of each network metric was calculated and both global and nodal network properties were measured in graph theory analysis. We used nonparametric permutation tests to identify group differences in topological metrics. Relationships between brain network measures and clinical symptom severity were analyzed in the PTSD group. Compared with TEC, brain networks of PTSD patients were characterized by decreased clustering coefficient (C ) (p = .04) and local efficiency (E ) (p = .04). Locally, patients with PTSD exhibited altered nodal centrality involving medial superior frontal (mSFG), inferior orbital frontal (iOFG), superior parietal (SPG), middle frontal (MFG), angular, and para-hippocampal gyri (p < .05, corrected). A negative correlation between the segregation (C ) of gray matter and functional networks was found in PTSD patients but not the TEC group. Analyses of topological brain gray matter networks indicate a more randomly organized brain network in PTSD. The reduced segregation in gray matter networks and its negative relation with increased segregation in the functional network indicate an inverse relation between gray matter and functional changes. The present psychoradiological findings may reflect a compensatory increase in functional network segregation following a loss of segregation in gray matter networks.
“…Present study particularly adds to psychoradiology, an evolving specialty as opposed to neuroradiology, mainly for psychiatric and psychological brain (Lui et al, 2016;Kressel et al, 2017;Sun et al, 2018;Port, 2018). …”
To psychoradiologically investigate the topological organization of single-subject gray matter networks in patients with PTSD. Eighty-nine adult PTSD patients and 88 trauma-exposed controls (TEC) underwent a structural T1 magnetic resonance imaging scan. The single-subject brain structural networks were constructed based on gray matter similarity of 90 brain regions. The area under the curve (AUC) of each network metric was calculated and both global and nodal network properties were measured in graph theory analysis. We used nonparametric permutation tests to identify group differences in topological metrics. Relationships between brain network measures and clinical symptom severity were analyzed in the PTSD group. Compared with TEC, brain networks of PTSD patients were characterized by decreased clustering coefficient (C ) (p = .04) and local efficiency (E ) (p = .04). Locally, patients with PTSD exhibited altered nodal centrality involving medial superior frontal (mSFG), inferior orbital frontal (iOFG), superior parietal (SPG), middle frontal (MFG), angular, and para-hippocampal gyri (p < .05, corrected). A negative correlation between the segregation (C ) of gray matter and functional networks was found in PTSD patients but not the TEC group. Analyses of topological brain gray matter networks indicate a more randomly organized brain network in PTSD. The reduced segregation in gray matter networks and its negative relation with increased segregation in the functional network indicate an inverse relation between gray matter and functional changes. The present psychoradiological findings may reflect a compensatory increase in functional network segregation following a loss of segregation in gray matter networks.
“…Prior ADHD neuroimaging studies have shown that the inhibitory behavioral control impairments in ADHD are related to regional abnormalities in the inferior frontal gyrus (IFG), anterior cingulate cortex (ACC), temporal/parietal areas, and basal ganglia (Chen et al, ; Chen et al, ; Lei et al, ; Lei, Du, et al, ; Lei, Li, et al, ; Li, He, et al, ; Li, Li, et al, ; Sun et al, ). Using a seed‐based functional connectivity analysis, we found that alterations in frontostriatal circuitry correlated with the degree of inhibitory executive dysfunction in ADHD (Li, He, et al, ; Li, Li, et al, ).…”
Section: Introductionmentioning
confidence: 99%
“…Prior ADHD neuroimaging studies have shown that the inhibitory behavioral control impairments in ADHD are related to regional abnormalities in the inferior frontal gyrus (IFG), anterior cingulate cortex (ACC), temporal/parietal areas, and basal ganglia Chen et al, 2016;Lei et al, 2014;Lei, Du, et al, 2015;Lei, Li, et al, 2015;Sun et al, 2018).…”
Neuroimaging studies have revealed functional brain network abnormalities in attention deficit hyperactivity disorder (ADHD), but the results have been inconsistent, potentially related to confounding medication effects. Furthermore, specific topological alterations in functional networks and their role in behavioral inhibition dysfunction remain to be established. Resting‐state functional magnetic resonance imaging was performed on 51 drug‐naïve children with ADHD and 55 age‐matched healthy controls. Brain functional networks were constructed by thresholding the partial correlation matrices of 90 brain regions, and graph theory was used to analyze network topological properties. The Stroop test was used to assess cognitive inhibitory abilities. Nonparametric permutation tests were used to compare the topological architectures in the two groups. Compared with healthy subjects, brain networks in ADHD patients demonstrated altered topological characteristics, including lower global (FDR q = 0.01) and local efficiency (p = 0.032, uncorrected) and a longer path length (FDR q = 0.01). Lower nodal efficiencies were found in the left inferior frontal gyrus and anterior cingulate cortex in the ADHD group (FDR both q < 0.05). Altered global and nodal topological efficiencies were associated with the severity of inhibitory cognitive control deficits and hyperactivity symptoms in ADHD (p <0 .05). Alterations in network topologies in drug‐naïve ADHD patients indicate weaker small‐worldization with decreased segregation and integration of functional brain networks. Deficits in the cingulo‐fronto‐parietal attention network were associated with inhibitory control deficits.
“…With advances in quantitative neuroimaging techniques, radiomics may provide a promising modality for predicting WMH progression . Functional MRI has revealed that the destruction of microstructure integrity in cerebral white matter occurs prior to the conventional neuroimaging visibility of WMH, suggesting the existence of a white matter penumbra in which the parenchyma is visually normal but the microstructure has been damaged .…”
mentioning
confidence: 99%
“…12 With advances in quantitative neuroimaging techniques, [13][14][15] radiomics may provide a promising modality for predicting WMH progression. [16][17][18] Functional MRI has revealed that the destruction of microstructure integrity in cerebral white matter occurs prior to the conventional neuroimaging visibility of WMH, suggesting the existence of a white matter penumbra in which the parenchyma is visually normal but the microstructure has been damaged. 12,19 In our previous cross-sectional study, 20 the WMH penumbra could be discriminated by a radiomics textural analysis, indicating that radiomics is a feasible technique to analyze microstructural changes in WMH.…”
Background
White matter hyperintensity (WMH) is widely observed in aging brain and is associated with various diseases. A pragmatic and handy method in the clinic to assess and follow up white matter disease is strongly in need.
Purpose
To develop and validate a radiomics nomogram for the prediction of WMH progression.
Study Type
Retrospective.
Population
Brain images of 193 WMH patients from the Picture Archiving and Communication Systems (PACS) database in the A Medical Center (Zhejiang Provincial People's Hospital). MRI data of 127 WMH patients from the PACS database in the B Medical Center (Zhejiang Lishui People's Hospital) were included for external validation. All of the patients were at least 60 years old.
Field Strength/Sequence
T1‐fluid attenuated inversion recovery images were acquired using a 3T scanner.
Assessment
WMH was evaluated utilizing the Fazekas scale based on MRI. WMH progression was assessed with a follow‐up MRI using a visual rating scale. Three neuroradiologists, who were blinded to the clinical data, assessed the images independently. Moreover, interobserver and intraobserver reproducibility were performed for the regions of interest for segmentation and feature extraction.
Statistical Tests
A receiver operating characteristic (ROC) curve, the area under the curve (AUC) of the ROC was calculated, along with sensitivity and specificity. Also, a Hosmer–Lemeshow test was performed.
Results
The AUC of radiomics signature in the primary, internal validation cohort, external validation cohort were 0.886, 0.816, and 0.787, respectively; the specificity were 71.79%, 72.22%, and 81%, respectively; the sensitivity were 92.68%, 87.94% and 78.3%, respectively. The radiomics nomogram in the primary cohort (AUC = 0.899) and the internal validation cohort (AUC = 0.84). The Hosmer–Lemeshow test showed no significant difference between the primary cohort and the internal validation cohort (P > 0.05). The AUC of the radiomics nomogram, radiomics signature, and hyperlipidemia in all patients from the primary and internal validation cohort was 0.878, 0.848, and 0.626, respectively.
Data Conclusion
This multicenter study demonstrated the use of a radiomics nomogram in predicting the progression of WMH with elderly adults (an age of at least 60 years) based on conventional MRI.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2020;51:535–546.
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