Decreased Resting-State Interhemispheric Functional Connectivity Correlated with Neurocognitive Deficits in Drug-Naive First-Episode Adolescent-Onset Schizophrenia

Abstract

BackgroundGiven that adolescence is a critical epoch in the onset of schizophrenia, studying aberrant brain changes in adolescent-onset schizophrenia, particularly in patients with drug-naive first-episode schizophrenia, is important to understand the biological mechanism of this disorder. Previous resting-state functional magnetic resonance imaging studies have shown abnormal functional connectivity in separate hemispheres in patients with adult-onset schizophrenia. Our aim to study adolescent-onset schizophrenia can provide clues for the early aetiology of schizophrenia.MethodA total of 48 drug-naïve, first-episode, adolescent-onset schizophrenia outpatients and 31 healthy controls underwent resting-state functional magnetic resonance imaging scans. Data were subjected to voxel-mirrored homotopic connectivity and support vector machine analyses.ResultsCompared with the healthy controls, the adolescent-onset schizophrenia group showed significantly lower voxel-mirrored homotopic connectivity values in different brain regions, including the fusiform gyrus, superior temporal gyrus/insula, precentral gyrus, and precuneus. Decreased voxel-mirrored homotopic connectivity values in the superior temporal gyrus/insula were significantly correlated with Trail-Making Test: Part A performance (r = −0.437, P = .002). A combination of the voxel-mirrored homotopic connectivity values in the precentral gyrus and precuneus may be used to discriminate patients with adolescent-onset schizophrenia from controls with satisfactory classification results, which showed sensitivity of 100%, specificity of 87.09%, and accuracy of 94.93%.ConclusionOur findings highlight resting-state interhemispheric FC abnormalities within the sensorimotor network of patients with adolescent-onset schizophrenia and confirm the relationship between adolescent-onset schizophrenia and adult-onset schizophrenia. These findings suggest that reduced interhemispheric connectivity within the sensorimotor network has a pivotal role in the pathogenesis of schizophrenia.

<p>Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review</p>

“…It was correlated with processing speed deficits, indicating that this dysfunction may contribute to cognitive deficits in patients. The SVM analysis showed sensitivity of 100%, specificity of 87.09%, and accuracy of 94.93% 42…”

“…They were regarded as meaningful brain areas and used as ROIs in the functional connectivity analysisDefault-mode network, salience networkThe two dataset were from two different imaging platformsPläschke, 201741rs-fMRI170 subjects:-86 SCZ-84 HCSVM61–72%Sn 65–77%Sp 46–69%AUC 0.61–0.79Authors investigated 12 functional networks. Only meta-analytic networks with a minimum of 10 nodes were included, since a lower number of features are uninformative for robust classificationEmotion-processing, empathy and cognitive action control networksYoung-old classification was based on all networks and outperformed clinical classificationLiu, 201842rs-fMRI79 subjects: −48 Drug-Naïve FES AOS-31 HCSVMVMHC94.93%Sn 100%Sp 87.09%For each subject, the fMRI scan lasted for 480 s, and 240 volumes were obtained.The first 10 volumes of each subject were discarded to certain steady-state conditions and for participants to acclimatize to a scanning environment during the analyzed portion of the dataFusiform gyrus, superior temporal gyrus, insula, precentral gyrus and precuneusauthors also used a battery of neurocognitive tests and they demonstrated deficits in multiple cognitive functions in patientsZeng, 201846rs-fMRI 1.5-3T734 subjects:-357 SCZ-377 HCRFE-SVMRFE-LDASANDANS81–85%Sn 75–83%Sp 81–86%Authors used multi-atlas based whole-brain fcMRI in the MVPA, which measures functional connectivity of the same image in different spaces. The three atlases used included 176, 160 and 116 ROIs respectivelyCortical-striatal-cerebellar circuit (default, salience, frontoparietal control, ventral attention, dorsal attention and somatomotor, visual).This paper provides fora multi-variate based whole-brain fcMRI pattern analysis to ensure the optimal use of the wealth of information present in fcMRI scans.Amin, 201847fMRI 3TT2-weighted298 subjects:-144 SCZ-154 HCTranslation-based multimodal fusion approachN.A.N.A.dFNC as the functional features and ICA-based sources from grey matter densities as the structural featuresPutamen, insular, precuneus, posterior cingulate cortex and temporal cortexThe deep learning approach has a potential for learning dynamic features from the fMRI data, and thus can offer a favorable framework for multimod...…”

Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review

“…It was associated with processing speed deficits, indicating the probable involvement with the neurocognitive alterations of these patients. The application of SVM ML technique analysis reached 100% sensitivity, 87.09% specificity, and 94.93% accuracy ( 34 ). Functional alterations could point to a role of DMN and SN in the SCZ psychopathology that is already known in first-psychotic episode patients and SVM seems to be able to discriminate with high accuracy patients from HC in research context.…”

Altered Sensorimotor-to-Transmodal Hierarchical Organization in Schizophrenia

“…Further complementing this view, neurophysiology findings indicate that the bottom-up propagation of deficits from early sensory to higher-level processes in schizophrenia occurs even when top-down processes remain intact (Dias et al, 2011). Similarly, some recent rsFC and structural studies found that the connectivity deficits of visual and sensorimotor pathway can be detected even when the associative regions, like FPN and DMN, failed to reach statistical significance in schizophrenia (Bordier et al, 2018;Chen et al, 2014;Guo et al, 2014;Jørgensen et al, 2016;Liu et al, 2018;Youxue Zhang et al, 2019). Our findings of prominent cortical network compression in the sensorimotor and visual systems therefore provides an integrative basis to support previous reports of impaired early sensory processing in schizophrenia.…”

“…Using resting-state functional connectivity (rsFC), researchers are able to observe abnormal FC within the high-order default, frontoparietal network as well as ventral attention network in schizophrenia (Dong et al, 2018b;Jiang et al, 2019;Liao et al, 2019;Pettersson-Yeo et al, 2011;Pläschke et al, 2017). Aside from these abnormalities, an increasing number of rsFC studies suggest dysfunctional intrinsic connectivity within visual and somatosensory systems in this condition (Bordier et al, 2018;Chen et al, 2016Chen et al, , 2014Dong et al, 2018a;Guo et al, 2014;Jiang et al, 2015;Liu et al, 2018;Yiwen Zhang et al, 2019). Up to this point, only a few studies have looked at how sensory networks pathologically interact with higher-order association systems in schizophrenia (Berman et al, 2017;Hoptman et al, 2018;Kaufmann et al, 2015).…”