Ultrafast method for mapping local functional connectivity hubs in the human brain

Baseline global brain structural and functional alterations at the time of symptom onset can predict subsequent cognitive deterioration in drug-naïve first-episode schizophrenia patients: Evidence from a follow-up study

Alterations in the global brain gray matter volume (gGMV) and global functional connectivity density (gFCD) play a pivotal role in the cognitive impairment and further deterioration in schizophrenia. This study aimed to assess the correlation between alterations in the gGMV and gFCD at baseline (ΔgGMV and ΔgFCD), and the subsequent alterations of cognitive function in schizophrenia patients after 2-year antipsychotic treatment. Global-brain magnetic resonance imaging scans were acquired from 877 drug-naïve, first-episode schizophrenia patients at baseline and after two years of antipsychotic treatment with adequate dosage and duration, and 200 healthy controls. According to ΔgGMV at baseline, schizophrenia patients were divided into mild, moderate, and severe alteration groups. The MATRICS consensus cognitive battery and Global Deficit Score (GDS) were used to assess cognitive impairment. We found that ΔgGMV and ΔgFCD at baseline were significantly correlated with the severity of the cognitive deterioration (ΔGDS). The correlation coefficient indicated a significant positive correlation between baseline ΔgFCD and subsequent cognitive deterioration, with a relatively stronger relation in the mild alteration group (r = 0.31). In addition, there was a significant positive correlation between baseline ΔgGMV and subsequent cognitive deterioration, with a stronger relation in the moderate and severe alteration groups (r = 0.303; r = 0.302, respectively). Our results showed that ΔgGMV and ΔgFCD are correlated with the severity of cognitive deterioration after completion of a 2-year antipsychotic treatment in schizophrenia patients. These findings suggest that baseline alterations in gGMV and gFCD hold potential for predicting subsequent cognitive decline in schizophrenia.

Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study

Although mounting neuroimaging studies have greatly improved our understanding of the neurobiological mechanism underlying internet addiction (IA), the results based on traditional group-level comparisons are insufficient in guiding individual clinical practice directly. Specific neuroimaging biomarkers are urgently needed for IA diagnosis and the evaluation of therapy efficacy. Therefore, this study aimed to develop support vector machine (SVM) models to identify IA and assess the efficacy of cognitive behavior therapy (CBT) based on unbiased functional connectivity density (FCD). Resting-state fMRI data were acquired from 27 individuals with IA before and after 8-week CBT sessions and 30 demographically matched healthy controls (HCs). The discriminative FCDs were computed as the features of the support vector classification (SVC) model to identify individuals with IA from HCs, and the changes in these discriminative FCDs after treatment were further used as features of the support vector regression (SVR) model to evaluate the efficacy of CBT. Based on the informative FCDs, our SVC model successfully differentiated individuals with IA from HCs with an accuracy of 82.5% and an area under the curve (AUC) of 0.91. Our SVR model successfully evaluated the efficacy of CBT using the FCD change ratio with a correlation efficient of 0.59. The brain regions contributing to IA classification and CBT efficacy assessment were the left inferior frontal cortex (IFC), middle frontal cortex (MFC) and angular gyrus (AG), the right premotor cortex (PMC) and middle cingulate cortex (MCC), and the bilateral cerebellum, orbitofrontal cortex (OFC) and superior frontal cortex (SFC). These findings confirmed the FCDs of hyperactive impulsive habit system, hypoactive reflecting system and sensitive interoceptive reward awareness system as potential neuroimaging biomarkers for IA, which might provide objective indexes for the diagnosis and efficacy evaluation of IA.

Functional Connectivity Density with Frequency-Dependent Changes in Patients with Diffuse Axonal Injury: A Resting-State Functional Magnetic Resonance Imaging Study

PURPOSE

We explored changes in spontaneous brain connectivity in patients with diffuse axonal injury (DAI), assessed via functional connectivity density (FCD) tests using different frequency bands.

PATIENTS AND METHODS

In all, 23 patients with DAI (17 males and 6 females) and 23 healthy controls (HCs; 17 males and 6 females) were included. Functional magnetic resonance imaging scans were performed when the participants were in a resting state and the FCD levels in three frequency bands (slow-4: 0.027-0.073 Hz, slow-5: 0.01-0.027 Hz, and typical: 0.01-0.08 Hz) were measured. In addition, Pearson's correlation coefficient was used to explore the relationship between clinical indices and brain regions with abnormal FCD values.

RESULTS

Compared to HCs, DAI patients had significantly greater FCD values in the right extranuclear/limbic lobe/cingulate gyrus and left limbic lobe/hippocampus/parahippocampal gyrus, and significantly lower FCD values in the left precuneus/posterior cingulate gyrus, in the slow-4 band. In the slow-5 band, the DAI patients had higher FCD values in the left inferior temporal gyrus/superior temporal gyrus, left parahippocampal gyrus/limbic lobe, left extranuclear/cingulate gyrus, and right medial frontal gyrus, and lower values in the right inferior frontal gyrus, right inferior parietal lobule, and left cingulate gyrus/limbic lobe. Moreover, compared to HCs, the values in the typical band were higher in the right extranuclear/limbic lobe/hippocampus/parahippocampal gyrus, but were significantly lower in the right precuneus/posterior cingulate gyrus and right inferior parietal lobule/supramarginal gyrus. The abnormal FCD values of these brain regions were linearly correlated with different clinical scale scores.

CONCLUSION

DAI patients had abnormal FCD values in various brain regions, indicating disruption to the brain functional network. Moreover, the values were frequency dependent. Our results provide new evidence for the pathogenesis of functional impairment and may explain the neuropathological or compensatory mechanism of the disease.

Treatment of auditory verbal hallucinations with atypical antipsychotics in healthy individuals: an artificially controlled post-treatment report

OBJECTIVE

This study was performed to investigate the effects and associated global functional connectivity density (gFCD) alterations associated with the use of atypical antipsychotics in healthy individuals with auditory verbal hallucinations (Hi-AVHs) using gFCD mapping techniques.

METHODS

A magnetic resonance imaging database of 38 Hi-AVHs with chronic or severe AVH symptoms was generated. The Hi-AVHs were administered an atypical antipsychotic (risperidone) for 24 weeks and monitored for a treatment response. All patients underwent functional magnetic resonance imaging pre- and post-treatment.

RESULTS

gFCD alterations were found in the auditory-memory-language and visual circuit regions pre- and post-treatment. However, gFCD alterations differed between patients with strong and weak treatment responses.

CONCLUSION

This is the first report to show that atypical antipsychotics can improve the symptoms of AVHs and that the treatment effects are associated with gFCD alterations in the auditory-memory-language circuit. These findings provide a foundation for future exploration of new treatment strategies for Hi-AVHs.

Brain function differences in drug-naïve first-episode auditory verbal hallucination-schizophrenia patients with versus without insight

Background:

Few studies have reported brain function differences in drug-naïve first-episode schizophrenia patients who had auditory verbal hallucinations (AVH) with insight vs. those without insight. This study aimed to investigate brain function differences between drug-naïve first-episode AVH-schizophrenia patients with and without insight.

Methods:

Forty first-episode drug-naïve AVH-schizophrenia patients with or without insight and 40 healthy controls between December 2016 and December 2018 were recruited in this study. The auditory hallucinations rating scale (AHRS) was used to assess AVH severity, while the insight and treatment attitudes questionnaire was used to distinguish insight. The global functional connectivity density (gFCD) between different groups was compared using a voxel-wise one-way analysis of covariance. The relationship between gFCD and AHRS total scores were analyzed using voxel-wise multiple regression.

Results:

Finally, 13 first-episode drug-naïve AVH-schizophrenia patients with insight, 15 AVH-schizophrenia patients without insight, and 20 healthy controls were included for analysis. Except for global assessment of functioning scores, there were no significant differences in sociodemographic information between the AVH-schizophrenia and healthy groups (P > 0.05). Compared to the healthy controls, AVH-schizophrenia patients with insight demonstrated a decreased gFCD in the supra-marginal gyrus within the primary auditory cortex, while those without insight demonstrated an increased gFCD in the inferior frontal gyrus and superior temporal gyrus and decreased gFCD in the supplemental motor area. Compared to the AVH-schizophrenia patients with insight, those without insight demonstrated an increased gFCD in the supra-marginal gyrus and posterior superior temporal lobule and a decreased gFCD in the frontal lobe. No significant correlation between gFCD and AVH severity (AHRS total score: r = 0.23, P = 0.590; and frequency: r = 0.42, P = 0.820) was found in both AVH-schizophrenia groups.

Conclusions:

The gFCD-aberrant brain regions in the AVH-schizophrenia patients without insight were wider compared to those with insight, although the AHRS scores were not significantly different. The AVH-schizophrenia patients without insight had wide functional impairment in the frontal lobule, which may underlie the lack of insight and the abnormal hyperactivity in the inferior frontal gurus and temporal lobe related to the AVH symptoms.

Abnormal Functional Connectivity in Cognitive Control Network, Default Mode Network, and Visual Attention Network in Internet Addiction: A Resting-State fMRI Study

Internet addiction (IA) has become a global mental and social problem, which may lead to a series of psychiatric symptoms including uncontrolled use of internet, and lack of concentration. However, the exact pathophysiology of IA remains unclear. Most of functional connectivity studies were based on pre-selected regions of interest (ROI), which could not provide a comprehensive picture of the communication abnormalities in IA, and might lead to limited or bias observations. Using local functional connectivity density (lFCD), this study aimed to explore the whole-brain abnormalities of functional connectivity in IA. We evaluated the whole-brain lFCD resulting from resting-state fMRI data in 28 IA individuals and 30 demographically matched healthy control subjects (HCs). The correlations between clinical characteristics and aberrant lFCD were also assessed. Compared with HCs, subjects with IA exhibited heightened lFCD values in the right dorsolateral prefrontal cortex (DLPFC), left parahippocampal gyrus (PHG), and cerebellum, and the bilateral middle cingulate cortex (MCC) and superior temporal pole (STP), as well as decreased lFCD values in the right inferior parietal lobe (IPL), and bilateral calcarine and lingual gyrus. Voxel-based correlation analysis revealed the significant correlations between the Young's Internet Addiction Test (IAT) score and altered lFCD values in the left PHG and bilateral STP. These findings revealed the hyper-connectivity in cognitive control network and default mode network as well as the hypo-connectivity in visual attention network, verifying the common mechanism in IA and substance addiction, and the underlying association between IA, and attention deficit/hyperactivity disorder in terms of neurobiology.

Brain structural, functional, and cognitive correlates of recent versus remote autobiographical memories in amnestic Mild Cognitive Impairment

Deficits in autobiographical memory appear earlier for recent than for remote life periods over the course of Alzheimer's disease (AD). The present study aims to further our understanding of this graded effect by investigating the cognitive and neural substrates of recent versus remote autobiographical memories in patients with amnestic Mild Cognitive Impairment (aMCI) thanks to an autobiographical fluency task. 20 aMCI patients and 25 Healthy elderly Controls (HC) underwent neuropsychological tests assessing remote (20-to-30 years old) and recent (the ten last years) autobiographical memory as well as episodic and semantic memory, executive function and global cognition. All patients also had a structural MRI and an FDG-PET scan. Correlations were assessed between each autobiographical memory score and the other tests as well as grey matter volume and metabolism. Within the aMCI, performances for the remote period correlated with personal semantic memory and episodic memory retrieval whereas performances for the recent period only correlated with episodic memory retrieval. Neuroimaging analyses revealed significant correlations between performances for the remote period and temporal pole and temporo-parietal cortex volumes and anterior cingulate gyrus metabolism, while performances for the recent period correlated with hippocampal volume and posterior cingulate, medial prefrontal and hippocampus metabolism. The brain regions related with the retrieval of events from the recent period showed greater atrophy/hypometabolism in aMCI patients compared to HC than those involved in remote memories. Recall of recent memories essentially relies on episodic memory processes and brain network while remote memories also involve other processes such as semantic memory. This is consistent with the semanticization of memories with time and may explain the better resistance of remote memory in AD.

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We studied cognitive correlates of recent and remote autobiographical memories in aMCI.

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We evaluated brain structural and functional correlates of each period of life.

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Recent memories rely on episodic memory processes and brain network.

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Remote memories also involve other processes such as semantic memory.

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These findings contribute to explain the better resistance of remote memory in aMCI.

Sub-hubs of baseline functional brain networks are related to early improvement following two-week pharmacological therapy for major depressive disorder

Accumulating evidence suggests that early improvement after two-week antidepressant treatment is predictive of later outcomes of patients with major depressive disorder (MDD); however, whether this early improvement is associated with baseline neural architecture remains largely unknown. Utilizing resting-state functional MRI data and graph-based network approaches, this study calculated voxel-wise degree centrality maps for 24 MDD patients at baseline and linked them with changes in the Hamilton Rating Scale for Depression (HAMD) scores after two weeks of medication. Six clusters exhibited significant correlations of their baseline degree centrality with treatment-induced HAMD changes for the patients, which were mainly categorized into the posterior default-mode network (i.e., the left precuneus, supramarginal gyrus, middle temporal gyrus, and right angular gyrus) and frontal regions. Receiver operating characteristic curve and logistic regression analyses convergently revealed excellent performance of these regions in discriminating the early improvement status for the patients, especially the angular gyrus (sensitivity and specificity of 100%). Moreover, the angular gyrus was identified as the optimal regressor as determined by stepwise regression. Interestingly, these regions possessed higher centrality than others in the brain (P < 10(-3)) although they were not the most highly connected hubs. Finally, we demonstrate a high reproducibility of our findings across several factors (e.g., threshold choice, anatomical distance, and temporal cutting) in our analyses. Together, these preliminary exploratory analyses demonstrate the potential of neuroimaging-based network analysis in predicting the early therapeutic improvement of MDD patients and have important implications in guiding earlier personalized therapeutic regimens for possible treatment-refractory depression.

DISC1 Ser704Cys impacts thalamic-prefrontal connectivity

The Disrupted-in-Schizophrenia 1 (DISC1) gene has been thought as a putative susceptibility gene for various psychiatric disorders, and DISC1 Ser704Cys is associated with variations of brain morphology and function. Moreover, our recent diffusion magnetic resonance imaging (dMRI) study reported that DISC1 Ser704Cys was associated with information transfer efficiency in the brain anatomical network. However, the effects of the DISC1 gene on functional brain connectivity and networks, especially for thalamic-prefrontal circuit, which are disrupted in various psychiatric disorders, are largely unknown. Using a functional connectivity density (FCD) mapping method based on functional magnetic resonance imaging data in a large sample of healthy Han Chinese subjects, we first investigated the association between DISC1 Ser704Cys and short- and long-range FCD hubs. Compared with Ser homozygotes, Cys-allele individuals had increased long-range FCD hubs in the bilateral thalami. The functional and anatomical connectivity of the thalamus to the prefrontal cortex was further analyzed. Significantly increased thalamic-prefrontal functional connectivity and decreased thalamic-prefrontal anatomical connectivity were found in DISC1 Cys-allele carriers. Our findings provide consistent evidence that the DISC1 Ser704Cys polymorphism influences the thalamic-prefrontal circuits in humans and may provide new insights into the neural mechanisms that link DISC1 and the risk for psychiatric disorders.

Changes in brain functional network connectivity after stroke

Studies have shown that functional network connection models can be used to study brain network changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlated to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea-lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.

Would the field of cognitive neuroscience be advanced by sharing functional MRI data?

During the past two decades, the advent of functional magnetic resonance imaging (fMRI) has fundamentally changed our understanding of brain-behavior relationships. However, the data from any one study add only incrementally to the big picture. This fact raises important questions about the dominant practice of performing studies in isolation. To what extent are the findings from any single study reproducible? Are researchers who lack the resources to conduct a fMRI study being needlessly excluded? Is pre-existing fMRI data being used effectively to train new students in the field? Here, we will argue that greater sharing and synthesis of raw fMRI data among researchers would make the answers to all of these questions more favorable to scientific discovery than they are today and that such sharing is an important next step for advancing the field of cognitive neuroscience.