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Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders

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A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Psychiatric Diseases".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 16028

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Dr. Zhiyong Zhao

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Guest Editor

Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Sci-ence, Zhejiang University, Hangzhou 310027, China
Interests: neuroimaging; magnetic resonance imaging (MRI); neuroscience and clinical dis-ease

Dr. Weihao Zheng

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Guest Editor

Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Interests: brain network modeling; neuroimaging; artificial intelligence

Dr. Zhe Zhang

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Guest Editor Assistant

School of Physics, Hangzhou Normal University, Hangzhou 310030, China
Interests: neuropsychiatric imaging; children’s cognitive development; brain networks

Special Issue Information

Dear Colleagues,

Patients with neuropsychiatric disorders often suffer from functional impairments in cognition, emotion and social behaviors, which seriously affect their quality of life and bring a great burden to both their family and society. However, the neuropathology underlying dysfunctions in neuropsychiatric disorders remains unclear.

In the field of fMRI, functional connectivity (FC) has been demonstrated to be an effective and powerful way to study the neuropathology of neuropsychiatric disorders by examining alterations in neural circuitry functions. However, conventional FC measures the correlation of signals, and reflects the interaction and communication between regions. In recent years, studies have proposed several advanced connectivity methods, such as dynamic FC, effective connectivity, and distance-dependent and edge-based FC, which consider more details (e.g., direction, time, distance and communication between edges) for connectivity analysis and provide richer information than traditional FC in revealing the neuropathology underlying the clinical disorders.

Thus, this Special Issue focuses on the recent developments in the methods and models based on FC, and their applications in neuropsychiatric disorders. We solicit both reviews and original research articles on the use of advanced functional connectivity analysis. This Special Issue will also address important conceptual and methodological questions in understanding how FC characterizes the information flow of brain regions. We expect that this will be beneficial for clinicians in understanding the nature, origins and neuropathological mechanisms of clinical symptoms in neuropsychiatric disorders.

The research areas covered by this Special Issue include, but are not limited to:

Studies using advanced functional connectivity in neuropsychiatric disorders, such as schizophrenia, depressive disorder, bipolar disorder, somatization disorder, anxiety disorder, panic disorder, obsessive compulsive disorder, epilepsy, disorders of consciousness, eating disorders, cervical dystonia, and hypochondriasis;
The diagnosis, prognosis, and assessment of therapy using functional connectivity measures;
Novel algorithms, models and analytics frameworks for functional connectivity;
The clinical application of other functional connectivity methods (e.g., functional network connectivity or functional connectivity density);
The relationship between functional connectivity changes and behavioral changes;
Review articles on the developments of advanced functional connectivity.

Dr. Zhiyong Zhao
Dr. Weihao Zheng
Guest Editors

Dr. Zhe Zhang
Guest Editor Assistant

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Keywords

neuropsychiatric disorders
brain
functional connectivity
fMRI
neural circuitry
diagnosis

Published Papers (11 papers)

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Research

14 pages, 4076 KiB

Open AccessArticle

Enhanced Dynamic Laterality Based on Functional Subnetworks in Patients with Bipolar Disorder

by Dandan Li, Jiangping Hao, Jianchao Hao, Xiaohong Cui, Yan Niu, Jie Xiang and Bin Wang

Brain Sci. 2023, 13(12), 1646; https://doi.org/10.3390/brainsci13121646 - 27 Nov 2023

Viewed by 937

Abstract

An ocean of studies have pointed to abnormal brain laterality changes in patients with bipolar disorder (BD). Determining the altered brain lateralization will help us to explore the pathogenesis of BD. Our study will fill the gap in the study of the dynamic changes of brain laterality in BD patients and thus provide new insights into BD research. In this work, we used fMRI data from 48 BD patients and 48 normal controls (NC). We constructed the dynamic laterality time series by extracting the dynamic laterality index (DLI) at each sliding window. We then used k-means clustering to partition the laterality states and the Arenas–Fernandez–Gomez (AFG) community detection algorithm to determine the number of states. We characterized subjects’ laterality characteristics using the mean laterality index (MLI) and laterality fluctuation (LF). Compared with NC, in all windows and state 1, BD patients showed higher MLI in the attention network (AN) of the right hemisphere, and AN in the left hemisphere showed more frequent laterality fluctuations. AN in the left hemisphere of BD patients showed higher MLI in all windows and state 3 compared to NC. In addition, in the AN of the right hemisphere in state 1, higher MLI in BD patients was significantly associated with patient symptoms. Our study provides new insights into the understanding of BD neuropathology in terms of brain dynamic laterality. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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17 pages, 2855 KiB

Open AccessArticle

Effects of Damage to the Integrity of the Left Dual-Stream Frontotemporal Network Mediated by the Arcuate Fasciculus and Uncinate Fasciculus on Acute/Subacute Post-Stroke Aphasia

by Qiwei Yu, Yuer Jiang, Yan Sun, Xiaowen Ju, Tianfen Ye, Na Liu, Surong Qian and Kefu Liu

Brain Sci. 2023, 13(9), 1324; https://doi.org/10.3390/brainsci13091324 - 14 Sep 2023

Cited by 1 | Viewed by 830

Abstract

(1) Background: To investigate the correlation between the integrity of the left dual-stream frontotemporal network mediated by the arcuate fasciculus (AF) and uncinate fasciculus (UF), and acute/subacute post-stroke aphasia (PSA). (2) Methods: Thirty-six patients were recruited and received both a language assessment and a diffusion tensor imaging (DTI) scan. Correlations between diffusion indices in the bilateral LSAF/UF and language performance assessment were analyzed with correlation analyses. Multiple linear regression analysis was also implemented to investigate the effects of the integrity of the left LSAF/UF on language performance. (3) Results: Correlation analyses showed that the diffusion indices, including mean fractional anisotropy (FA) values and the fiber number of the left LSAF rather than the left UF was significantly positively associated with language domain scores (p < 0.05). Multiple linear regression analysis revealed an independent and positive association between the mean FA value of the left LSAF and the percentage score of language subsets. In addition, no interaction effect of the integrity of the left LSAF and UF on language performance was found (p > 0.05). (4) Conclusions: The integrity of the left LSAF, but not the UF, might play important roles in supporting residual language ability in individuals with acute/subacute PSA; simultaneous disruption of the dual-stream frontotemporal network mediated by the left LSAF and UF would not result in more severe aphasia than damage to either pathway alone. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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14 pages, 2098 KiB

Open AccessArticle

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder

by Xuchen Qi, Wanxiu Xu and Gang Li

Brain Sci. 2023, 13(9), 1282; https://doi.org/10.3390/brainsci13091282 - 04 Sep 2023

Cited by 2 | Viewed by 1230

Abstract

Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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13 pages, 2854 KiB

Open AccessArticle

Common and Distinct Functional Connectivity of the Orbitofrontal Cortex in Depression and Schizophrenia

by Huan Huang, Bei Rong, Cheng Chen, Qirong Wan, Zhongchun Liu, Yuan Zhou, Gaohua Wang and Huiling Wang

Brain Sci. 2023, 13(7), 997; https://doi.org/10.3390/brainsci13070997 - 27 Jun 2023

Cited by 1 | Viewed by 955

Abstract

Schizophrenia and depression are psychiatric disorders with overlapping clinical and biological features. This study aimed to identify common and distinct neuropathological mechanisms in schizophrenia and depression patients using resting-state functional magnetic resonance imaging (fMRI). The study included 28 patients with depression (DEP), 29 patients with schizophrenia (SCH), and 30 healthy control subjects (HC). Intrinsic connectivity contrast (ICC) was used to identify functional connectivity (FC) changes at the whole-brain level, and significant ICC differences were found in the bilateral orbitofrontal cortex (OFC) across all three groups. Further seed-based FC analysis indicated that compared to the DEP and HC groups, the FC between bilateral OFC and medial prefrontal cortex (MPFC), right anterior insula, and right middle frontal gyrus were significantly lower in the SCH group. Additionally, the FC between right OFC and left thalamus was decreased in both patient groups compared to the HC group. Correlation analysis showed that the FC between OFC and MPFC was positively correlated with cognitive function in the SCH group. These findings suggest that OFC connectivity plays a critical role in the pathophysiology of schizophrenia and depression and may provide new insights into the potential neural mechanisms underlying these two disorders. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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16 pages, 1761 KiB

Open AccessArticle

Aberrant Resting-State Functional Connectivity in MDD and the Antidepressant Treatment Effect—A 6-Month Follow-Up Study

by Kangning Li, Xiaowen Lu, Chuman Xiao, Kangning Zheng, Jinrong Sun, Qiangli Dong, Mi Wang, Liang Zhang, Bangshan Liu, Jin Liu, Yan Zhang, Hua Guo, Futao Zhao, Yumeng Ju and Lingjiang Li

Brain Sci. 2023, 13(5), 705; https://doi.org/10.3390/brainsci13050705 - 23 Apr 2023

Viewed by 1574

Abstract

Background: The mechanism by which antidepressants normalizing aberrant resting-state functional connectivity (rsFC) in patients with major depressive disorder (MDD) is still a matter of debate. The current study aimed to investigate aberrant rsFC and whether antidepressants would restore the aberrant rsFC in patients with MDD. Methods: A total of 196 patients with MDD and 143 healthy controls (HCs) received the resting-state functional magnetic resonance imaging and clinical assessments at baseline. Patients with MDD received antidepressant treatment after baseline assessment and were re-scanned at the 6-month follow-up. Network-based statistics were employed to identify aberrant rsFC and rsFC changes in patients with MDD and to compare the rsFC differences between remitters and non-remitters. Results: We identified a significantly decreased sub-network and a significantly increased sub-network in MDD at baseline. Approximately half of the aberrant rsFC remained significantly different from HCs after 6-month treatment. Significant overlaps were found between baseline reduced sub-network and follow-up increased sub-network, and between baseline increased sub-network and follow-up decreased sub-network. Besides, rsFC at baseline and rsFC changes between baseline and follow-up in remitters were not different from non-remitters. Conclusions: Most aberrant rsFC in patients with MDD showed state-independence. Although antidepressants may modulate aberrant rsFC, they may not specifically target these aberrations to achieve therapeutic effects, with only a few having been directly linked to treatment efficacy. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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9 pages, 1299 KiB

Open AccessArticle

Entorhinal Cortex Functional Connectivity during Item Long-Term Memory and the Role of Sex

by Dylan S. Spets and Scott D. Slotnick

Brain Sci. 2023, 13(3), 446; https://doi.org/10.3390/brainsci13030446 - 04 Mar 2023

Viewed by 1270

Abstract

A growing body of literature shows there are sex differences in the patterns of brain activity during long-term memory. However, there is a paucity of evidence on sex differences in functional brain connectivity. We previously identified sex differences in the patterns of connections with the hippocampus, a medial temporal lobe (MTL) subregion, during spatial long-term memory. The perirhinal/entorhinal cortex, another MTL subregion, plays a critical role in item memory. In the current functional magnetic resonance imaging (fMRI) study, we investigated perirhinal/entorhinal functional connectivity and the role of sex during item memory. During the study phase, abstract shapes were presented to the left or right of fixation. During the test phase, abstract shapes were presented at fixation, and the participants classified each item as previously “old” or “new”. An entorhinal region of interest (ROI) was identified by contrasting item memory hits and misses. This ROI was connected to regions generally associated with visual memory, including the right inferior frontal gyrus (IFG) and visual-processing regions (the bilateral V1, bilateral cuneus, and left lingual gyrus). Males produced greater connectivity than females with the right IFG/insula and the right V1/bilateral cuneus. Broadly, these results contribute to a growing body of literature supporting sex differences in the brain. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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15 pages, 4633 KiB

Open AccessArticle

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

by Gang Li, Hongyang Zhong, Jie Wang, Yixin Yang, Huayun Li, Sujie Wang, Yu Sun and Xuchen Qi

Brain Sci. 2023, 13(3), 384; https://doi.org/10.3390/brainsci13030384 - 22 Feb 2023

Cited by 3 | Viewed by 1629

Abstract

Depression has become one of the most common mental illnesses, causing serious physical and mental harm. However, there remain unclear and uniform physiological indicators to support the diagnosis of clinical depression. This study aimed to use machine learning techniques to investigate the abnormal multidimensional EEG features in patients with depression. Resting-state EEG signals were recorded from 41 patients with depression and 34 healthy controls. Multiple dimensional characteristics were extracted, including power spectral density (PSD), fuzzy entropy (FE), and phase lag index (PLI). These three different dimensional characteristics with statistical differences between two groups were ranked by three machine learning algorithms. Then, the ranked characteristics were placed into the classifiers according to the importance of features to obtain the optimal feature subset with the highest classification accuracy. The results showed that the optimal feature subset contained 86 features with the highest classification accuracy of 98.54% ± 0.21%. According to the statistics of the optimal feature subset, PLI had the largest number of features among the three categories, and the number of beta features was bigger than other rhythms. Moreover, compared to the healthy controls, the PLI values in the depression group increased in theta and beta rhythms, but decreased in alpha1 and alpha2 rhythms. The PSD of theta and beta rhythms were significantly greater in depression group than that in healthy controls, and the FE of beta rhythm showed the same trend. These findings indicate that the distribution of abnormal multidimensional features is potentially useful for the diagnosis of depression and understanding of neural mechanisms. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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12 pages, 2345 KiB

Open AccessArticle

Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder

by Xinling Geng, Xiwang Fan, Yiwen Zhong, Manuel F. Casanova, Estate M. Sokhadze, Xiaoli Li and Jiannan Kang

Brain Sci. 2023, 13(1), 130; https://doi.org/10.3390/brainsci13010130 - 12 Jan 2023

Cited by 6 | Viewed by 2082

Abstract

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that interferes with normal brain development. Brain connectivity may serve as a biomarker for ASD in this respect. This study enrolled a total of 179 children aged 3−10 years (90 typically developed (TD) and 89 with ASD). We used a weighted phase lag index and a directed transfer function to investigate the functional and effective connectivity in children with ASD and TD. Our findings indicated that patients with ASD had local hyper-connectivity of brain regions in functional connectivity and simultaneous significant decrease in effective connectivity across hemispheres. These connectivity abnormalities may help to find biomarkers of ASD. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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13 pages, 2513 KiB

Open AccessArticle

Altered Functional Connectivity and Complexity in Major Depressive Disorder after Musical Stimulation

by Pintao Qiu, Jinxiao Dai, Ting Wang, Hangcheng Li, Cunbin Ma and Xugang Xi

Brain Sci. 2022, 12(12), 1680; https://doi.org/10.3390/brainsci12121680 - 07 Dec 2022

Cited by 3 | Viewed by 1489

Abstract

Major depressive disorder (MDD) is a common mental illness. This study used electroencephalography (EEG) to explore the effects of music therapy on brain networks in MDD patients and to elucidate changes in functional brain connectivity in subjects before and after musical stimulation. EEG signals were collected from eight MDD patients and eight healthy controls. The phase locking value was adopted to calculate the EEG correlation of different channels in different frequency bands. Correlation matrices and network topologies were studied to analyze changes in functional connectivity between brain regions. The results of the experimental analysis found that the connectivity of the delta and beta bands decreased, while the connectivity of the alpha band increased. Regarding the characteristics of the EEG functional network, the average clustering coefficient, characteristic path length and degree of each node in the delta band decreased significantly after musical stimulation, while the characteristic path length in the beta band increased significantly. Characterized by the average clustering coefficient and characteristic path length, the classification of depression and healthy controls reached 93.75% using a support vector machine. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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16 pages, 1583 KiB

Open AccessArticle

Altered Cerebro-Cerebellar Effective Connectivity in New-Onset Juvenile Myoclonic Epilepsy

by Laiyang Ma, Guangyao Liu, Pengfei Zhang, Jun Wang, Wenjing Huang, Yanli Jiang, Yu Zheng, Na Han, Zhe Zhang and Jing Zhang

Brain Sci. 2022, 12(12), 1658; https://doi.org/10.3390/brainsci12121658 - 03 Dec 2022

Cited by 3 | Viewed by 1425

Abstract

(1) Objective: Resting-state fMRI studies have indicated that juvenile myoclonic epilepsy (JME) could cause widespread functional connectivity disruptions between the cerebrum and cerebellum. However, the directed influences or effective connectivities (ECs) between these brain regions are poorly understood. In the current study, we aimed to evaluate the ECs between the cerebrum and cerebellum in patients with new-onset JME. (2) Methods: Thirty-four new-onset JME patients and thirty-four age-, sex-, and education-matched healthy controls (HCs) were included in this study. We compared the degree centrality (DC) between the two groups to identify intergroup differences in whole-brain functional connectivity. Then, we used a Granger causality analysis (GCA) to explore JME-caused changes in EC between cerebrum regions and cerebellum regions. Furthermore, we applied a correlation analysis to identify associations between aberrant EC and disease severity in patients with JME. (3) Results: Compared to HCs, patients with JME showed significantly increased DC in the left cerebellum posterior lobe (CePL.L), the right inferior temporal gyrus (ITG.R) and the right superior frontal gyrus (SFG.R), and decreased DC in the left inferior frontal gyrus (IFG.L) and the left superior temporal gyrus (STG.L). The patients also showed unidirectionally increased ECs from cerebellum regions to the cerebrum regions, including from the CePL.L to the right precuneus (PreCU.R), from the left cerebellum anterior lobe (CeAL.L) to the ITG.R, from the right cerebellum posterior lobe (CePL.R) to the IFG.L, and from the left inferior semi-lunar lobule of the cerebellum (CeISL.L) to the SFG.R. Additionally, the EC from the CeISL.L to the SFG.R was negatively correlated with the disease severity. (4) Conclusions: JME patients showed unidirectional EC disruptions from the cerebellum to the cerebrum, and the negative correlation between EC and disease severity provides a new perspective for understanding the cerebro-cerebellar neural circuit mechanisms in JME. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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10 pages, 795 KiB

Open AccessArticle

Sex Differences of the Functional Brain Activity in Treatment-Resistant Depression: A Resting-State Functional Magnetic Resonance Study

by Jifei Sun, Yi Luo, Yue Ma, Chunlei Guo, Zhongming Du, Shanshan Gao, Limei Chen, Zhi Wang, Xiaojiao Li, Ke Xu, Yang Hong, Xue Yu, Xue Xiao and Jiliang Fang

Brain Sci. 2022, 12(12), 1604; https://doi.org/10.3390/brainsci12121604 - 23 Nov 2022

Cited by 3 | Viewed by 1530

Abstract

The presence of different clinical symptoms in patients with treatment-resistant depression (TRD) of different sexes may be related to different neuropathological mechanisms. A total of 16 male patients with TRD, 18 female patients with TRD, 18 male healthy controls (HCs) and 19 female HCs completed this study. We used the amplitude of low frequency fluctuations (ALFF) method to analyze the results. Moreover, the correlation between abnormal brain areas and clinical symptoms in different sexes of the TRD groups was also analyzed. The effects of the sex-by-group interaction difference in ALFF among the four groups was located in the left middle frontal gyrus, left precentral gyrus and left precuneus. Post hoc comparisons revealed that the male TRD group had lower ALFF in the left middle frontal gyrus and left precentral gyrus compared with the female TRD group. There was a positive correlation between the left middle frontal gyrus, the left precuneus and the 17-item Hamilton Rating Scale for Depression scale (HAMD-17) scores, and a negative correlation between the left precentral gyrus and the HAMD-17 scores in the female TRD group. This study will provide some clinical reference value for the sex differences in neuropathological mechanisms of TRD. Full article

(This article belongs to the Special Issue Advanced Functional Connectivity Analysis in Neuropsychiatric Disorders)

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