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Enregistrement W2791049188 · doi:10.4103/0366-6999.226905

Aberrant Functional Connectivity Patterns of Default Mode Network May Play a Key Role in the Interaction between Auditory Verbal Hallucinations and Insight

2018· article· en· W2791049188 sur OpenAlexaboutno aff
Rixing Jing, Shen Li, Xin‐Jun Zhang, Jing Long, Tian-Hong Zhou, Chuanjun Zhuo

Notice bibliographique

RevueChinese Medical Journal · 2018
Typearticle
Langueen
DomaineNeuroscience
ThématiqueHallucinations in medical conditions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDefault mode networkKey (lock)Functional connectivityPsychologyCognitive psychologyNeuroscienceComputer science

Résumé

récupéré en direct d'OpenAlex

Previous studies have reported that auditory verbal hallucinations (AVHs) may be caused by resting-state neuronal hyperactivity in the default mode network (DMN). Abnormally increased functional connectivity (FC) between the DMN and other cortical regions may cause disturbances in neuronal network architecture and interactions. Subsequently, disturbed neuronal network architecture and interactions may induce increased activation in auditory and speech perception areas in the absence of external auditory stimuli as well as reduced activation in the same areas in the presence of external auditory stimuli. These disturbances may be the neural basis of AVHs.[1] In addition, insight, which is another core characteristic of schizophrenia, has been associated with treatment compliance, relapses, long-term outcomes, and global functioning ability. Some studies have reported that insight is also related to disturbance of the DMN, especially self-monitoring: poor insight is associated with increased connectivity between the self-referential network and the left insula.[2] The self-referential network may be considered a component of the DMN.[23] Based on the aforementioned findings, we found that DMN alteration is associated both with AVHs and with insight. However, to the best of our knowledge, no one has yet studied FC patterns (FCPs) between the DMN and other brain regions associated with AVHs in schizophrenic patients with and without insight. Aberrant FCPs between DMN and other brain regions might play a key role in the interaction between AVHs and insight. Based on previous studies[4] we conducted an exploratory pilot study to investigate FCPs between the DMN and other brain regions related to schizophrenics’ experiences of AVHs with and without insight using the FC map technique. We hypothesized the existence of distinct FCPs between the DMN and other brain regions related to schizophrenics’ experiences of AVHs with and without insight. For this pilot study, we selected seven AVH-schizophrenic patients without insight, six AVH-schizophrenic patients with insight and 22 healthy controls (HCs) from our database for inclusion in FCP analysis. All subjects provided written informed consent prior for this study, and the protocol was approved by the Committee on Studies Involving Human Beings at Tianjin Anding Hospital. The mean antipsychotic dosage (chlorpromazine equivalents) was 374.0 ± 163.8 mg/d in AVH-schizophrenic patients without insight and 375.2 ± 190.5 mg/d in AVH-schizophrenic patients with insight (P = 0.98 for a contrast between the two patient groups). There were no significant differences in age, gender, or education level among the three groups. Preprocessing was performed with Statistical Parametric Mapping software (SPM8, http://www.fil.ion.ucl.ac.uk/spm/software/spm8/), the Analysis of Functional NeuroImage (AFNI, http://afni.nimh.nih.gov/afni) package, and the FMRIB Software Library (FSL, http://fsl.fmrib.ox.ac.uk/fsl). The first 10 images were discarded. Images were then corrected for slice-timing and motion. Each functional magnetic resonance imaging (fMRI) scan was intensity-scaled to have a whole brain mean value of 10,000. Temporal bandpass filtering (0.01–0.08 Hz) was performed, and 6 affine motion parameters from the data were also regressed. Removal of linear and quadratic trends was also performed. Single-subject images were spatially normalized to the Montreal Neurological Institute space using the DARTEL algorithm in SPM8, resampled to 3 mm × 3 mm × 3 mm during normalization, and smoothed with a 6-mm full-width at half-maximum Gaussian kernel. The maximum frame-wise displacement (FD) between time points was computed for each subject, and the subjects excessive movement (FD >3 mm) were excluded from the study. The posterior cingulate cortex (PCC) forms a central node of the DMN and has an important role in schizophrenia. In this study, the PCC was used as the seed region for FC map analysis to assess FCPs s in DMN related to schizophrenics’ experiences of AVHs with and without insight. We chose the bilateral PCC as a whole-brain seed region from an Anatomical Automatic Labelling template. For each scan, the mean time series of voxels in the PCC was applied as a seed to produce whole-brain voxel-wise FC maps using Pearson's correlation. Then, Fisher's z transformation was used to transform Pearson's correlation to z-values. Considering the limited sample size, we investigated group differences in fMRI results between each pair of groups (AVH-schizophrenic patients without insight vs. HCs, AVH-schizophrenic patients with insight vs. HCs, and AVH-schizophrenic patients without insight vs. AVH-schizophrenic patients with insight) using Statistical nonParametric Mapping (SnPM13, http://warwick.ac.uk/snpm), which provides an extensible framework for nonparametric permutation tests based on the general linear model as well as pseudo t-statistics for independent observations. A two-tailed, pseudo voxel-level one-sample t-test was first performed on the whole-brain FC maps of HCs with age and sex as covariates (n = 1000 permutations, family-wise error (FWE)-corrected, P < 0.05). Then, between-group analyses were conducted using pseudo voxel-level two-sample t-tests within a mask including brain regions in which HCs showed statistically significant FC measures, with age, sex, and illness duration as covariates (n = 1000 permutations, cluster size >30 for AVH-schizophrenic patients without insight vs. AVH-schizophrenic patients with insight, P < 0.01; cluster size >50 for comparisons of AVH-schizophrenic patients without insight vs. HCs and AVH-schizophrenic patients with insight vs. HCs, P < 0.01). The primary finding of this pilot study is that when compared with the AVH-schizophrenic patients with insight, patients without insight demonstrated increased FC in the PCC-frontal lobe-cingulate cortex circuit; in addition, decreased FC was observed in the PCC-right cerebrum circuit [Figure 1a]. When compared to HCs, AVH-schizophrenic patients without insight did not display any differences in FC [Figure 1b]. However, more interestingly, AVH-schizophrenic patients with insight demonstrated decreased FC relative to HCs in the PCC-frontal lobe-angular gyrus circuit [Figure 1c].Figure 1: DMN functional connectivity patterns differences between AVH-schizophrenic patients without insight, AVH-schizophrenic patients with insight and healthy controls. (a) When compared with the AVH-schizophrenic patients with insight, patients without insight demonstrated increased FC in the PCC-frontal lobe-cingulate cortex circuit; in addition, decreased FC was observed in the PCC-right cerebrum circuit. (b) When compared with HCs, AVH-schizophrenic patients without insight did not display any differences in FC. (c) AVH-schizophrenic patients with insight demonstrated decreased FC relative to HCs in the PCC-frontal lobe-angular gyrus circuit. AVH: Auditory verbal hallucination; DMN: Default mode network. FC: Functional connectivity; PCC: Posterior cingulate cortex; HCs: Healthy controls.The preliminary findings indicated that AVH-schizophrenic patients without insight demonstrated an increased FCP in the PCC-frontal lobe-cingulate cortex circuit. This circuit is a key component of the DMN; thus, this finding strongly supports the hypothesis that increased spontaneous neural activity of DMN is the neural basis of AVHs as well as the hypothesis of increased spontaneous neural activity in the self-referential network. In combination, the above findings suggest that DMN hyperactivity might play a key role in the interaction between AVHs and poor insight. In contrast, we found decreased FC in the PCC-frontal lobe-angular gyrus circuit. This finding provides converging support for our suggestion that DMN hyperactivity may play a key role in the interaction between AVHs and poor insight.[12345] However, there are several limitations to the pilot study. First, the small sample size may affect the strength of our findings. These findings provide only initial evidence for further study. In future studies, we will consider this flaw and address this gap. Second, in this pilot study, we only enrolled chronic patients, and selection bias may reduce the strength of our findings. In further studies, we will conduct a well-designed long-term follow-up study enrolling a large sample to further explore mechanisms of AVHs, insight, and the interaction between AVHs and insight. We will acquire baseline MRI data from these drug-naïve first-episode patients, and MRI data will then be acquired once every 6 months for 2–3 years. Through these data, we can characterize the dynamic trajectory of patients’ symptoms and brain characteristics (such as the specific trajectory of neural markers of AVHs in schizophrenic patients and the specific trajectory of neural markers of insight in schizophrenic patients). Although such a study will be extremely time-consuming and laborious, it can precisely identify specific pathological targets and provide an objective index for precise treatment strategies, thereby elevating the early remission rate. Although many flaws exist in this preliminary study and future studies are needed to clarify the results, our preliminary findings tend to support the hypothesis that aberrant FCPs in DMN might play a key role in the interaction between AVHs and insight. Although the findings of this pilot study are limited by the small sample size and other flaws, they may nevertheless provide useful clues for further study. Financial support and sponsorship This work was supported by grants from the Tianjin Health Bureau Foundation (No. 2014KR02) and Key Projects of the Natural Science Foundation of Tianjin, China (No. 17JCZDJC35700). Conflicts of interest There are no conflicts of interest.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,006
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,188
Score d'incertitude au seuil0,935

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,318
Écart entre enseignants0,292 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2018
Routes d'admission1
Résumé présentoui

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