The triple network and temporal experience: a phenomenologically informed resting-state fMRI study

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.

Background: Subjective cognitive decline and amnestic mild cognitive impairment (aMCI) were widely thought to be preclinical AD spectrum disorders, characterized by aberrant functional connectivity (FC) within the triple networks of the default mode network (DMN), the salience network (SN), and the executive control network (ECN). Dynamic FC (DFC) analysis can capture temporal fluctuations in brain FC during the scan, which static FC analysis cannot. The purpose of the current study was to explore the changes in dynamic FC within the triple networks of the preclinical AD spectrum and further reveal their potential diagnostic value in diagnosing preclinical AD spectrum disorders.Methods: We collected resting-state functional magnetic resonance imaging data from 44 patients with subjective cognitive decline (SCD), 49 with aMCI, and 58 healthy controls (HCs). DFC analysis based on the sliding time-window correlation method was used to analyze DFC variability within the triple networks in the three groups. Then, correlation analysis was conducted to reveal the relationship between altered DFC variability within the triple networks and a decline in cognitive function. Furthermore, logistic regression analysis was used to assess the diagnostic accuracy of altered DFC variability within the triple networks in patients with SCD and aMCI.Results: Compared with the HC group, the groups with SCD and aMCI both showed altered DFC variability within the triple networks. DFC variability in the right middle temporal gyrus and left inferior frontal gyrus (IFG) within the ECN were significantly different between patients with SCD and aMCI. Moreover, the altered DFC variability in the left IFG within the ECN was obviously associated with a decline in episodic memory and executive function. The logistic regression analysis showed that multivariable analysis had high sensitivity and specificity for diagnosing SCD and aMCI.Conclusions: Subjective cognitive decline and aMCI showed varying degrees of change in DFC variability within the triple networks and altered DFC variability within the ECN involved episodic memory and executive function. More importantly, altered DFC variability and the triple-network model proved to be important biomarkers for diagnosing and identifying patients with preclinical AD spectrum disorders.

Schizophrenic Patients and Their Unaffected Siblings Show Similar Abnormalities in Resting-State Functional Connectivity

Aberrant thalamocortical connectivity and large-scale network interactions among the default mode network (DMN), salience network (SN), and executive control network (ECN) (ie, triple networks) have been regarded as critical in schizophrenia pathophysiology. Despite the importance of network properties and the role of the thalamus as an integrative hub, large-scale thalamocortical triple network functional connectivities (FCs) in different stages of the psychotic disorder have not yet been reported. Thirty-nine first-episode psychosis (FEP) patients, 75 individuals at clinical high risk (CHR) for psychosis, 46 unaffected relatives (URs) of schizophrenia patients with high genetic loading, and 110 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Modular community detection was used to identify cortical and thalamic resting-state networks, and thalamocortical network interactions were compared across the groups. Thalamic triple networks included higher-order thalamic nuclei. Thalamic SN-cortical ECN FC was greater in the FEP group than in the CHR, UR, and HC groups. Thalamic DMN-cortical DMN and thalamic SN-cortical DMN FCs were greater in FEP and CHR participants. Thalamic ECN-cortical DMN and thalamic ECN-cortical SN FCs were greater in FEP patients and URs. These results highlight critical modulatory functions of thalamic triple networks and the shared and distinct patterns of thalamocortical triple network dysconnectivities across different stages of psychotic disorders. The current study findings suggest that large-scale thalamocortical triple network dysconnectivities may be used as an integrative biomarker for extending our understanding of the psychosis pathophysiology and for targeting network-based neuromodulation therapeutics.

The cortico-hippocampal network is an emerging neural framework with striking evidence that it supports cognition in humans, especially memory; this network includes the anterior temporal (AT) system, the posterior medial (PM) system, the anterior hippocampus (aHIPPO), and the posterior hippocampus (pHIPPO). This study aimed to detect aberrant patterns of functional connectivity within and between large-scale cortico-hippocampal networks in first-episode schizophrenia patients compared with a healthy control group via resting-state functional magnetic resonance imaging (rs-fMRI) and to explore the correlations of these aberrant patterns with cognition. A total of 86 first-episode, drug-naïve schizophrenia patients and 102 healthy controls (HC) were recruited to undergo rs-fMRI examinations and clinical evaluations. We conducted large-scale edge-based network analysis to characterize the functional architecture of the cortico-hippocampus network and investigate between-group differences in within/between-network functional connectivity. Additionally, we explored the associations of functional connectivity (FC) abnormalities with clinical characteristics, including scores on the Positive and Negative Syndrome Scale (PANSS) and cognitive scores. Compared with the HC group, schizophrenia patients exhibited widespread alterations to within-network FC of the cortico-hippocampal network, with decreases in FC involving the precuneus (PREC), amygdala (AMYG), parahippocampal cortex (PHC), orbitofrontal cortex (OFC), perirhinal cortex (PRC), retrosplenial cortex (RSC), posterior cingulate cortex (PCC), angular gyrus (ANG), aHIPPO, and pHIPPO. Schizophrenia patients also showed abnormalities in large-scale between-network FC of the cortico-hippocampal network, in the form of significantly decreased FC between the AT and the PM, the AT and the aHIPPO, the PM and the aHIPPO, and the aHIPPO and the pHIPPO. A number of these signatures of aberrant FC were correlated with PANSS score (positive, negative, and total score) and with scores on cognitive test battery items, including attention/vigilance (AV), working memory (WM), verbal learning and memory (Verb_Lrng), visual learning and memory (Vis_Lrng), reasoning and problem-solving (RPS), and social cognition (SC). Schizophrenia patients show distinct patterns of functional integration and separation both within and between large-scale cortico-hippocampal networks, reflecting a network imbalance of the hippocampal long axis with the AT and PM systems, which regulate cognitive domains (mainly Vis_Lrng, Verb_Lrng, WM, and RPS), and particularly involving alterations to FC of the AT system and the aHIPPO. These findings provide new insights into the neurofunctional markers of schizophrenia.

To examine the functional and anatomical connectivity of the cerebellum and their relationship in schizophrenia through resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). Ten subjects with schizophrenia and 10 healthy controls underwent resting-state fMRI and DTI. Left and right cerebellar seed regions were used in a voxel-wise functional connectivity analysis of the cerebellum. Fractional anisotropy (FA) was measured to assess white matter integrity of the middle cerebellar peduncle (MCP) and superior cerebellar peduncle (SCP). Differences in functional connectivity and FA values between the two groups were determined by two-sample t-tests. The relationship between functional connectivity and FA values in both groups were assessed using Pearson s correlation. Decreased functional connectivity to the left middle temporal gyrus, bilateral middle cingulate cortex, right paracentral lobule, right thalamus, and bilateral cerebellum were found in subjects with schizophrenia when compared to healthy controls; while decreased FA values in the left SCP were found in subjects with schizophrenia compared to healthy controls. Significant correlation was observed between the functional connectivity of the left cerebellum-right paracentral lobule and right cerebellum-right thalamus and the FA values of the MCP in healthy controls. Findings of this multi-modal imaging study support functional and anatomical connectivity abnormalities and the role of the cerebellum in schizophrenia. They also indicate the need for further investigation regarding the relationship between functional and anatomical connectivity and its role in neuropathophysiology.

BackgroundUsing regional homogeneity (ReHo) blood oxygen level-dependent functional MR (BOLD-fMRI), we investigated the structural and functional alterations of brain regions among patients with methamphetamine-associated psychosis (MAP).Material/MethodsThis retrospective study included 17 MAP patients, 16 schizophrenia (SCZ) patients, and 18 healthy controls. Informed consent was obtained from all patients before the clinical assessment, the severity of clinical symptoms was evaluated prior to the fMRI scanning, and then images were acquired and preprocessed after each participant received 6-min fRMI scanning. The participants all underwent BOLD-fMRI scanning. Voxel-based morphometry was used to measure gray matter density (GMD). Resting-state fMRI (rs-fMRI) was conducted to analyze functional MR, ReHo, and functional connectivity (FC).ResultsGMD analysis results suggest that MAP patients, SCZ patients, and healthy volunteers show different GMDs within different brain regions. Similarly, the ReHo analysis results suggest that MAP patients, SCZ patients, and healthy volunteers have different GMDs within different brain regions. Negative correlations were found between ReHo- and the PANSS-positive scores within the left orbital interior frontal gyrus (L-orb-IFG) of MAP patients. ReHo- and PANSS-negative scores of R-SFG were negatively correlated among SCZ patients. The abnormal FC of R-MFG showed a negative correlation with the PANSS score among MAP patients.ConclusionsThe abnormalities in brain structure and FC were associated with the development of MAP.

Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) in clinical practice, especially during depressive episodes. A unifying triple-network model, involving the default mode network (DMN), central executive network (CEN) and salience network (SN), has been proposed to explain the neural physiopathology of psychiatric and neurological disorders. Although several studies revealed shared and specific alterations between BD and MDD in key regions of DMN, CEN, and SN, and a few studies used different measures to detect detailed alterations in the triple networks in BD and MDD, their shared and specific patterns of altered functional connectivity (FC) in the triple networks has remained unclear. In this study, we acquired resting-state fMRI (R-fMRI) data from 38 unmedicated BD and 35 unmedicated MDD patients during depressive episodes along with 47 healthy controls. We first determined the spatially independent components of the DMN, SN, and CEN by using independent component analysis (ICA); then we estimated the inter-ROI and inter-network FC for each group. By comparing the differences between the three groups, we obtained the following results: (1) both the BD and MDD patients showed shared weaker intra-network FC in the left mPFC and right precuneus within the DMN as well as weaker inter-ROI FC between the left AI and right AI compared with the healthy controls; (2) the BD had weaker while the MDD had stronger intra-network FC in the right dlPFC within the rCEN as well as stronger inter-ROI FC between the right dlPFC and right ANG compared with the healthy controls; (3) the BD showed specific, stronger inter-ROI FC between the left PPC and right AI as well as stronger inter-network FC between the lCEN and SN compared with either the MDD or the control group. Our findings provide new information for understanding the neural physiopathology and clinical symptoms of depressed BD and MDD patients.

A triple network model consisting of a default network, a salience network, and a central executive network has recently been used to understand connectivity patterns in cognitively normal versus dysfunctional brains. This study aimed to explore changes in the dynamic connectivity of triplet network in mild traumatic brain injury (mTBI) and its relationship to cognitive performance. In this work, we acquired resting-state functional magnetic resonance imaging (fMRI) data from 30 mTBI patients and 30 healthy controls (HCs). Independent component analysis, sliding time window correlation, and k-means clustering were applied to resting-state fMRI data. Further, we analyzed the relationship between changes in dynamic functional connectivity (FC) parameters and clinical variables in mTBI patients. The results showed that the dynamic functional connectivity of the brain triple network was clustered into five states. Compared with HC, mTBI patients spent longer in state 1, which is characterized by weakened dorsal default mode network (DMN) and anterior salience network (SN) connectivity, and state 3, which is characterized by a positive correlation between DMN and SN internal connectivity. Mild TBI patients had fewer metastases in different states than HC patients. In addition, the mean residence time in state 1 correlated with Montreal Cognitive Assessment scores in mTBI patients; the number of transitions between states correlated with Glasgow Coma Score in mTBI patients. Taken together, our findings suggest that the dynamic properties of FC in the triple network of mTBI patients are abnormal, and provide a new perspective on the pathophysiological mechanism of cognitive impairment from the perspective of dynamic FC.

BackgroundPatients with schizophrenia exhibit widespread disruptions in large-scale brain network communication, particularly within the default mode network (DMN), central executive network (CEN), and salience network (SN) — collectively termed the triple networks. While existing research has established the association between functional abnormalities in these networks and the pathophysiological mechanisms of schizophrenia, investigations into white matter’s functional role remain limited. Specifically, the involvement of white matter in dynamic interactions among the triple networks remains unclear. Dynamic functional connectivity (DFC), capable of capturing time-varying characteristics of brain activity, may offer novel insights into both triple network dysfunction and white matter abnormalities in schizophrenia.MethodsThis study enrolled 93 schizophrenia patients and 92 healthy controls. To assess white matter function, we extracted 48 white matter networks based on the Johns Hopkins University (JHU) white matter atlas and employed sliding window analysis to examine DFC patterns and global coupling properties within both triple networks and white matter networks. Additionally, a longitudinal observational follow-up study (approximately 5 months) was conducted with 39 patients to evaluate treatment-related changes in DFC and coupling dynamics.ResultsCompared with healthy controls, schizophrenia patients exhibited significant alterations in both intra-network DFC and global coupling across the triple networks and white matter networks. Longitudinal observations revealed DFC changes and temporal characteristics, particularly within white matter networks. Patients exhibited higher baseline scores for fractional window and mean dwell time than healthy individuals, which decreased during treatment follow-up. Additionally, the PANSS scores of the patients were significantly lower compared to before treatment. Brain regions showing significant global coupling changes included anterior/posterior DMN, corpus callosum, and the left crus of cerebellum.ConclusionOur findings highlight the pathophysiological significance of functional and coupling abnormalities between triple networks and white matter networks in schizophrenia. These results provide novel insights into dynamic alterations within brain networks of schizophrenia patients and may suggest potential neuroimaging biomarkers for future therapeutic strategies.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12888-025-07455-2.

Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients’ social lives. As a result, accurate diagnosis of SZ has attracted researchers’ interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients’ and 74 healthy controls’ (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample t-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease.

Short-term antipsychotic treatment reduces functional connectivity of the striatum in first-episode drug-naïve early-onset schizophrenia

Major depressive disorder (MDD) is a severe mental disorder and is lacking in biomarkers for clinical diagnosis. Previous studies have demonstrated that functional abnormalities of the unifying triple networks are the underlying basis of the neuropathology of depression. However, whether the functional properties of the triple network are effective biomarkers for the diagnosis of depression remains unclear. In our study, we used independent component analysis to define the triple networks, and resting-state functional connectivities (RSFCs), effective connectivities (EC) measured with dynamic causal modeling (DCM), and dynamic functional connectivity (dFC) measured with the sliding window method were applied to map the functional interactions between subcomponents of triple networks. Two-sample t-tests with p < 0.05 with Bonferroni correction were used to identify the significant differences between healthy controls (HCs) and MDD. Compared with HCs, the MDD showed significantly increased intrinsic FC between the left central executive network (CEN) and salience network (SAL), increased EC from the right CEN to left CEN, decreased EC from the right CEN to the default mode network (DMN), and decreased dFC between the right CEN and SAL, DMN. Moreover, by fusion of the changed RSFC, EC, and dFC as features, support vector classification could effectively distinguish the MDD from HCs. Our results demonstrated that fusion of the multiple functional connectivities measures of the triple networks is an effective way to reveal functional disruptions for MDD, which may facilitate establishing the clinical diagnosis biomarkers for depression.

Disrupted Functional Connectivity in Schizophrenia: a Resting State Fmri Study

Directed Functional Connectivity Changes of Triple Networks for Stable and Progressive Mild Cognitive Impairment