White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression

LncRNA NPTN-IT1-201 Ameliorates Depressive-like Behavior by Targeting miR-142-5p and Regulating Inflammation and Apoptosis via BDNF

OBJECTIVE

Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) are widely expressed in the brain and are associated with the development of neurological and neurodegenerative diseases. However, their roles and molecular mechanisms in major depressive disorder (MDD) remain largely unknown. This study aimed to identify lncRNAs and miRNAs involved in the development of MDD and elucidate their molecular mechanisms.

METHODS

Transcriptome and bioinformatic analyses were performed to identify miRNAs and lncRNAs related to MDD. C57 mice were subjected to chronic unpredictable mild stress (CUMS) to establish a depression model. Lentiviruses containing either lncRNA NPTN-IT1-201 or miR-142-5p were microinjected into the hippocampal region of these mice. Behavioral tests including the sucrose preference test (SPT), tail suspension test (TST), and forced swim test (FST) were conducted to evaluate depressive-like behaviors.

RESULTS

The results revealed that overexpression of lncRNA NPTN-IT1-201 or inhibition of miR-142-5p significantly ameliorated depressive-like behaviors in CUMS-treated mice. Dual-luciferase reporter assays confirmed interactions between miR-142-5p with both brain-derived neurotrophic factor (BDNF) and NPTN-IT1-201. ELISA analysis revealed significant alterations in relevant biomarkers in the blood samples of MDD patients compared to healthy controls. Histological analyses, including HE and Nissl staining, showed marked structural changes in brain tissues following CUMS treatment, which were partially reversed by lncRNA NPTN-IT1-201 overexpression or miR-142-5p inhibition. Immunofluorescence imaging demonstrated significant differences in the levels of BAX, Bcl2, p65, Iba1 among different treatment groups. TUNEL assays confirmed reduced apoptosis in brain tissues following these interventions. Western blotting showed the significant differences in BDNF, BAX, and Bcl2 protein levels among different treatment groups.

CONCLUSION

NPTN-IT1-201 regulates inflammation and apoptosis in MDD by targeting BDNF via miR-142-5p, making it a potential therapeutic target for MDD.

Altered White-Matter Functional Network in Children with Idiopathic Generalized Epilepsy

RATIONALE AND OBJECTIVES

The white matter (WM) functional network changes offers insights into the potential pathological mechanisms of certain diseases, the alterations of WM functional network in idiopathic generalized epilepsy (IGE) remain unclear. We aimed to explore the topological characteristics changes of WM functional network in childhood IGE using resting-state functional Magnetic resonance imaging (MRI) and T1-weighted images.

METHODS

A total of 84 children (42 IGE and 42 matched healthy controls) were included in this study. Functional and structural MRI data were acquired to construct a WM functional network. Group differences in the global and regional topological characteristics were assessed by graph theory and the correlations with clinical and neuropsychological scores were analyzed. A support vector machine algorithm model was employed to classify individuals with IGE using WM functional connectivity as features, and the model's accuracy was evaluated using leave-one-out cross-validation.

RESULTS

In IGE group, at the network level, the WM functional network exhibited increased assortativity; at the nodal level, 17 nodes presented nodal disturbances in WM functional network, and nodal disturbances of 11 nodes were correlated with cognitive performance scores, disease duration and age of onset. The classification model achieved the 72.6% accuracy, 0.746 area under the curve, 69.1% sensitivity, 76.2% specificity.

CONCLUSION

Our study demonstrated that the WM functional network topological properties changes in childhood IGE, which were associated with cognitive function, and WM functional network may help clinical classification for childhood IGE. These findings provide novel information for understanding the pathogenesis of IGE and suggest that the WM function network might be qualified as potential biomarkers.

Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.

Altered white matter functional pathways in Alzheimer's disease

Alzheimer's disease (AD) is associated with functional disruption in gray matter (GM) and structural damage to white matter (WM), but the relationship to functional signal in WM is unknown. We performed the functional connectivity (FC) and graph theory analysis to investigate abnormalities of WM and GM functional networks and corpus callosum among different stages of AD from a publicly available dataset. Compared to the controls, AD group showed significantly decreased FC between the deep WM functional network (WM-FN) and the splenium of corpus callosum, between the sensorimotor/occipital WM-FN and GM visual network, but increased FC between the deep WM-FN and the GM sensorimotor network. In the clinical groups, the global assortativity, modular interaction between occipital WM-FN and visual network, nodal betweenness centrality, degree centrality, and nodal clustering coefficient in WM- and GM-FNs were reduced. However, modular interaction between deep WM-FN and sensorimotor network, and participation coefficients of deep WM-FN and splenium of corpus callosum were increased. These findings revealed the abnormal integration of functional networks in different stages of AD from a novel WM-FNs perspective. The abnormalities of WM functional pathways connect downward to the corpus callosum and upward to the GM are correlated with AD.

Unraveling the link: white matter damage, gray matter atrophy and memory impairment in patients with subcortical ischemic vascular disease

Introduction

Prior MRI studies have shown that patients with subcortical ischemic vascular disease (SIVD) exhibited white matter damage, gray matter atrophy and memory impairment, but the specific characteristics and interrelationships of these abnormal changes have not been fully elucidated.

Materials and methods

We collected the MRI data and memory scores from 29 SIVD patients with cognitive impairment (SIVD-CI), 29 SIVD patients with cognitive unimpaired (SIVD-CU) and 32 normal controls (NC). Subsequently, the thicknesses and volumes of the gray matter regions that are closely related to memory function were automatically assessed using FreeSurfer software. Then, the volume, fractional anisotropy (FA), mean diffusivity (MD), amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) values of white matter hyperintensity (WMH) region and normal-appearing white matter (NAWM) were obtained using SPM, DPARSF, and FSL software. Finally, the analysis of covariance, spearman correlation and mediation analysis were used to analyze data.

Results

Compared with NC group, patients in SIVD-CI and SIVD-CU groups showed significantly abnormal volume, FA, MD, ALFF, and ReHo values of WMH region and NAWM, as well as significantly decreased volume and thickness values of gray matter regions, mainly including thalamus, middle temporal gyrus and hippocampal subfields such as cornu ammonis (CA) 1. These abnormal changes were significantly correlated with decreased visual, auditory and working memory scores. Compared with the SIVD-CU group, the significant reductions of the left CA2/3, right amygdala, right parasubiculum and NAWM volumes and the significant increases of the MD values in the WMH region and NAWM were found in the SIVD-CI group. And the increased MD values were significantly related to working memory scores. Moreover, the decreased CA1 and thalamus volumes mediated the correlations between the abnormal microstructure indicators in WMH region and the decreased memory scores in the SIVD-CI group.

Conclusion

Patients with SIVD had structural and functional damages in both WMH and NAWM, along with specific gray matter atrophy, which were closely related to memory impairment, especially CA1 atrophy and thalamic atrophy. More importantly, the volumes of some temporomesial regions and the MD values of WMH regions and NAWM may be potentially helpful neuroimaging indicators for distinguishing between SIVD-CI and SIVD-CU patients.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

A Study on the Effect of Executive Control Network Functional Connection on the Therapeutic Efficacy of Repetitive Transcranial Magnetic Stimulation in Alzheimer's Disease

Background

Alzheimer's disease (AD) is a neurodegenerative disease characterized by brain network dysfunction. Few studies have investigated whether the functional connections between executive control networks (ECN) and other brain regions can predict the therapeutic effect of repetitive transcranial magnetic stimulation (rTMS).

Objective

The purpose of this study is to examine the relationship between the functional connectivity (FC) within ECN networks and the efficacy of rTMS.

Methods

We recruited AD patients for rTMS treatment. We established an ECN using baseline period fMRI data and conducted an analysis of the ECN's FC throughout the brain. Concurrently, the support vector regression (SVR) method was employed to project post-rTMS cognitive scores, utilizing the connectional attributes of the ECN as predictive markers.

Results

The average age of the patients was 66.86±8.44 years, with 8 males and 13 females. Significant improvement on most cognitive measures. We use ECN connectivity and brain region functions in baseline patients as features for SVR model training and fitting. The SVR model could demonstrate significant predictability for changes in Montreal Cognitive Assessment scores among AD patients after rTMS treatment. The brain regions that contributed most to the prediction of the model (the top 10% of weights) were located in the medial temporal lobe, middle temporal gyrus, frontal lobe, parietal lobe and occipital lobe.

Conclusions

The stronger the antagonism between ECN and parieto-occipital lobe function, the better the prediction of cognitive improvement; the stronger the synergy between ECN and fronto-temporal lobe function, the better the prediction of cognitive improvement.

Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture

From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.

Multi-view graph network learning framework for identification of major depressive disorder

Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) exhibits non-Euclidean topological structures, which have pathological foundations and serve as ideal objective data for intelligent diagnosis of major depressive disorder (MDD) patients. Additionally, the fully connected FC demonstrates uniform spatial structures. To learn and integrate information from these two structural forms for a more comprehensive identification of MDD patients, we propose a novel hierarchical learning structure called Multi-View Graph Neural Network (MV-GNN). In MV-GNN, the collaborative FC of subjects is filtered and reconstructed from topological view to obtain the reconstructed FC, incorporating various threshold values to calculate the topological attributes of brain regions. ROC analysis is performed on the average scores of these attributes for MDD and healthy control (HC) groups to determine an efficient threshold. Group differences analysis is conducted on the efficient topological attributes of brain regions, followed by their selection. These efficient attributes, along with the reconstructed FC, are combined to construct a graph view using self-attention graph pooling and graph convolutional neural networks, enabling efficient embedding. To extract efficient FC pattern difference information from spatial view, a dual leave-one-out cross-feature selection method is proposed. It selects and extracts relevant information from uniformly sized FC structures' high-dimensional spatial features, constructing a relationship view between brain regions. This approach incorporates both the whole graph topological view and spatial relationship view in a multi-layered structure, fusing them using gating mechanisms. By incorporating multiple views, it enhances the inference of whether subjects suffer from MDD and reveals differential information between MDD and HC groups across different perspectives. The proposed model structure is evaluated through leave-one-site cross-validation and achieves an average accuracy of 65.61% in identifying MDD patients at a single-center site, surpassing state-of-the-art methods in MDD recognition. The model provides valuable discriminatory information for objective diagnosis of MDD and serves as a reference for pathological foundations.

Intracranial electrophysiological and structural basis of BOLD functional connectivity in human brain white matter

While functional MRI (fMRI) studies have mainly focused on gray matter, recent studies have consistently found that blood-oxygenation-level-dependent (BOLD) signals can be reliably detected in white matter, and functional connectivity (FC) has been organized into distributed networks in white matter. Nevertheless, it remains unclear whether this white matter FC reflects underlying electrophysiological synchronization. To address this question, we employ intracranial stereotactic-electroencephalography (SEEG) and resting-state fMRI data from a group of 16 patients with drug-resistant epilepsy. We find that BOLD FC is correlated with SEEG FC in white matter, and this result is consistent across a wide range of frequency bands for each participant. By including diffusion spectrum imaging data, we also find that white matter FC from both SEEG and fMRI are correlated with white matter structural connectivity, suggesting that anatomical fiber tracts underlie the functional synchronization in white matter. These results provide evidence for the electrophysiological and structural basis of white matter BOLD FC, which could be a potential biomarker for psychiatric and neurological disorders.

Biochemical metabolism in the anterior cingulate cortex and cognitive function in major depressive disorder with or without insomnia syndrome

BACKGROUND

Major depressive disorder (MDD) and insomnia have been linked to deficiencies in cognitive performance. However, the underlying mechanism of cognitive impairment in MDD patients with insomnia symptoms (IS) remains unclear. This study aimed to explore the effects of IS in patients with MDD by comparing cognitive function indices among those with IS, those without insomnia symptoms (NIS), and healthy controls (HCs). In addition, we assessed whether the dysfunction of central nervous system (CNS) is one of the important pathophysiologic mechanisms of IS in patients with MDD by comparing the biochemical metabolism ratios in the anterior cingulate cortex (ACC).

METHOD

Fifty-five MDD with IS, 39 MDD without IS, and 47 demographically matched HCs underwent the MATRICS Consensus Cognitive Battery (MCCB) assessment and proton magnetic resonance spectroscopy (¹H-MRS). MCCB cognitive scores and biochemical metabolism in ACC were assessed and compared between groups.

RESULTS

Compared to the HCs group, IS and NIS groups scored significantly lower in seven MCCB cognitive domains (speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning problem solving and social cognition). IS group showed a lower speed of processing and lower Cho/Cr ratio in the left ACC vs. NIS group and HCs. Also, in IS group, the Cho/Cr ratio in the left ACC was positively correlated with the composite T-score.

CONCLUSION

Patients with comorbidity of MDD with IS may exhibit more common MCCB cognitive impairments than those without IS, particularly speed of processing. Also, dysfunction of ACC may underlie the neural substrate of cognitive impairment in MDD with IS.

Disrupted functional networks within white-matter served as neural features in adolescent patients with conduct disorder

BACKGROUND

Conduct disorder (CD) has been conceptualized as a psychiatric disorder associated with white-matter (WM) structural abnormalities. Although diffusion tensor imaging could identify WM structural architecture changes, it cannot characterize functional connectivity (FC) within WM. Few studies have focused on disentangling the WM dysfunctions in CD patients by using functional magnetic resonance imaging (fMRI).

METHODS

The resting-state fMRI data were first obtained from both adolescent CD and typically developing (TD) controls. A voxel-based clustering analysis was utilized to identify the large-scale WM FC networks. Then, we examined the disrupted WM network features in CD, and further investigated whether these features could predict the impulsive symptoms in CD using support vector regression prediction model.

RESULTS

We identified 11 WM functional networks. Compared with TDs, CD patients showed increased FCs between occipital network (ON) and superior temporal network (STN), between orbitofrontal network (OFN) and corona radiate network (CRN), as well as between deep network and CRN. Further, the disrupted FCs between ON and STN and between OFN and CRN were significantly negatively associated with non-planning impulsivity scores in CD. Moreover, the disrupted WM networks could be served as features to predict the motor impulsivity scores in CD.

CONCLUSIONS

Our results provided further support on the existence of WM functional networks and could extended our knowledge about the WM functional abnormalities related with emotional and perception processing in CD patients from the view of WM dysfunction.

Altered white matter functional network in nicotine addiction

Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.

Classification of major depression disorder via using minimum spanning tree of individual high-order morphological brain network

BACKGROUND

Major depressive disorder (MDD) is an overbroad and heterogeneous diagnosis with no reliable or quantifiable markers. We aim to combine machine-learning techniques with the individual minimum spanning tree of the morphological brain network (MST-MBN) to determine whether the network properties can provide neuroimaging biomarkers to identify patients with MDD.

METHOD

Eight morphometric features of each region of interest (ROI) were extracted from 3D T1 structural images of 106 patients with MDD and 97 healthy controls. Six feature distances of the eight morphometric features were calculated to generate a feature distance matrix, which was defined as low-order MBN. Further linear correlations of feature distances between ROIs were calculated on the basis of low-order MBN to generate individual high-order MBN. The Kruskal's algorithm was used to generate the MST to obtain the core framework of individual low-order and high-order MBN. The regional and global properties of the individual MSTs were defined as the feature. The support vector machine and back-propagation neural network was used to diagnose MDD and assess its severity, respectively.

RESULT

The low-order and high-order MST-MBN constructed by cityblock distance had the excellent classification performance. The high-order MST-MBN significantly improved almost 20 % diagnostic accuracy compared with the low-order MST-MBN, and had a maximum R² value of 0.939 between the predictive and true Hamilton Depression Scale score. The different group-level connectivity strength mainly involves the central executive network and default mode network (no statistical significance after FDR correction).

CONCLUSION

We proposed an innovative individual high-order MST-MBN to capture the cortical high-order morphological correlation and make an excellent performance for individualized diagnosis and assessment of MDD.

Localized activity alternations in periventricular nodular heterotopia-related epilepsy

OBJECTIVE

Periventricular nodular heterotopia (PNH) is a common type of heterotopia usually characterized by epilepsy. Previous studies have identified alterations in structural and functional connectivity related to this disorder, but its local functional neural basis has received less attention. The purpose of this study was to combine univariate analysis and a Gaussian process classifier (GPC) to assess local activity and further explore neuropathological mechanisms in PNH-related epilepsy.

METHODS

We used a 3.0-T scanner to acquire resting-state data and measure local regional homogeneity (ReHo) alterations in 38 patients with PNH-related epilepsy and 38 healthy controls (HCs). We first assessed ReHo alterations by comparing the PNH group to the HC group using traditional univariate analysis. Next, we applied a GPC to explore whether ReHo could be used to differentiate PNH patients from healthy patients at an individual level.

RESULTS

Compared to HCs, PNH-related epilepsy patients exhibited lower ReHo in the left insula extending to the putamen as well as in the subgenual anterior cingulate cortex (sgACC) extending to the orbitofrontal cortex (OFC) [p < 0.05, family-wise error corrected]. Both of these regions were also correlated with epilepsy duration. Furthermore, the ReHo GPC classification yielded a 76.32% accuracy (sensitivity = 71.05% and specificity = 81.58%) with p < 0.001 after permutation testing.

INTERPRETATION

Using the resting-state approach, we identified localized activity alterations in the left insula extending to the putamen and the sgACC extending to the OFC, providing pathophysiological evidence of PNH. These local connectivity patterns may provide a means to differentiate PNH patients from HCs.

Multivariate associations between behavioural dimensions and white matter across children and adolescents with and without attention-deficit/hyperactivity disorder

BACKGROUND

Attention deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder. Integrity of white matter microstructure plays a key role in the neural mechanism of ADHD presentations. However, the relationships between specific behavioural dimensions and white matter microstructure are less well known. This study aimed to identify associations between white matter and a broad set of clinical features across children and adolescent with and without ADHD using a data-driven multivariate approach.

METHOD

We recruited a total of 130 children (62 controls and 68 ADHD) and employed regularized generalized canonical correlation analysis to characterize the associations between white matter and a comprehensive set of clinical measures covering three domains, including symptom, cognition and behaviour. We further applied linear discriminant analysis to integrate these associations to explore potential developmental effects.

RESULTS

We delineated two brain-behaviour dimensional associations in each domain resulting a total of six multivariate patterns of white matter microstructural alterations linked to hyperactivity-impulsivity and mild affected; executive functions and working memory; externalizing behaviour and social withdrawal, respectively. Apart from executive function and externalizing behaviour sharing similar white matter patterns, all other dimensions linked to a specific pattern of white matter microstructural alterations. The multivariate dimensional association scores showed an overall increase and normalization with age in ADHD group while remained stable in controls.

CONCLUSIONS

We found multivariate neurobehavioral associations exist across ADHD and controls, which suggested that multiple white matter patterns underlie ADHD heterogeneity and provided neural bases for more precise diagnosis and individualized treatment.

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.

Driving brain state transitions in major depressive disorder through external stimulation

Major depressive disorder (MDD) as a dysfunction of neural circuits and brain networks has been established in modern neuroimaging sciences. However, the brain state transitions between MDD and health through external stimulation remain unclear, which limits translation to clinical contexts and demonstrable clinical utility. We propose a framework of the large-scale whole-brain network model for MDD linking the underlying anatomical connectivity with functional dynamics obtained from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Then, we further explored the optimal brain regions to promote the transition of brain states between MDD and health through external stimulation of the model. Based on the whole-brain model successfully fitting the brain state space in MDD and the health, we demonstrated that the transition from MDD to health is achieved by the excitatory activation of the limbic system and from health to MDD by the inhibitory stimulation of the reward circuit. Our finding provides novel biophysical evidence for the neural mechanism of MDD and its recovery and allows the discovery of new stimulation targets for MDD recovery.

The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach

Background

Transient ischemic attack (TIA) is a known risk factor for stroke. Abnormal alterations in the low-frequency range of the gray matter (GM) of the brain have been studied in patients with TIA. However, whether there are abnormal neural activities in the low-frequency range of the white matter (WM) in patients with TIA remains unknown. The current study applied two resting-state metrics to explore functional abnormalities in the low-frequency range of WM in patients with TIA. Furthermore, a reinforcement learning method was used to investigate whether altered WM function could be a diagnostic indicator of TIA.

Methods

We enrolled 48 patients with TIA and 41 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) and clinical/physiological/biochemical data were collected from each participant. We compared the group differences between patients with TIA and HCs in the low-frequency range of WM using two resting-state metrics: amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). The altered ALFF and fALFF values were defined as features of the reinforcement learning method involving a Q-learning algorithm.

Results

Compared with HCs, patients with TIA showed decreased ALFF in the right cingulate gyrus/right superior longitudinal fasciculus/left superior corona radiata and decreased fALFF in the right cerebral peduncle/right cingulate gyrus/middle cerebellar peduncle. Based on these two rs-fMRI metrics, an optimal Q-learning model was obtained with an accuracy of 82.02%, sensitivity of 85.42%, specificity of 78.05%, precision of 82.00%, and area under the curve (AUC) of 0.87.

Conclusion

The present study revealed abnormal WM functional alterations in the low-frequency range in patients with TIA. These results support the role of WM functional neural activity as a potential neuromarker in classifying patients with TIA and offer novel insights into the underlying mechanisms in patients with TIA from the perspective of WM function.

Investigation of white matter functional networks underlying different behavioral profiles in attention-deficit/hyperactivity disorder

Background

Cortical functional network alterations have been widely accepted as the neural basis of attention-deficit/hyperactivity disorder (ADHD). Recently, white matter has also been recognized as a novel neuroimaging marker of psychopathology and has been used as a complement to cortical functional networks to investigate brain-behavior relationships. However, disorder-specific features of white matter functional networks (WMFNs) are less well understood than those of gray matter functional networks. In the current study, we constructed WMFNs using a new strategy to characterize behavior-related network features in ADHD.

Methods

We recruited 46 drug-naïve boys with ADHD and 46 typically developing (TD) boys, and used clustering analysis on resting-state functional magnetic resonance imaging data to generate WMFNs in each group. Intrinsic activity within each network was extracted, and the associations between network activity and behavior measures were assessed using correlation analysis.

Results

Nine WMFNs were identified for both ADHD and TD participants. However, boys with ADHD showed a splitting of the inferior corticospinal-cerebellar network and lacked a cognitive control network. In addition, boys with ADHD showed increased activity in the dorsal attention network and somatomotor network, which correlated positively with attention problems and hyperactivity symptom scores, respectively, while they presented decreased activity in the frontoparietal network and frontostriatal network in association with poorer performance in response inhibition, working memory, and verbal fluency.

Conclusions

We discovered a dual pattern of white matter network activity in drug-naïve ADHD boys, with hyperactive symptom-related networks and hypoactive cognitive networks. These findings characterize two distinct types of WMFN in ADHD psychopathology.

Increased functional connectivity of white-matter in myotonic dystrophy type 1

Background

Myotonic dystrophy type 1 (DM1) is the most common and dominant inherited neuromuscular dystrophy disease in adults, involving multiple organs, including the brain. Although structural measurements showed that DM1 is predominantly associated with white-matter damage, they failed to reveal the dysfunction of the white-matter. Recent studies have demonstrated that the functional activity of white-matter is of great significance and has given us insights into revealing the mechanisms of brain disorders.

Materials and methods

Using resting-state fMRI data, we adopted a clustering analysis to identify the white-matter functional networks and calculated functional connectivity between these networks in 16 DM1 patients and 18 healthy controls (HCs). A two-sample t-test was conducted between the two groups. Partial correlation analyzes were performed between the altered white-matter FC and clinical MMSE or HAMD scores.

Results

We identified 13 white-matter functional networks by clustering analysis. These white-matter functional networks can be divided into a three-layer network (superficial, middle, and deep) according to their spatial distribution. Compared to HCs, DM1 patients showed increased FC within intra-layer white-matter and inter-layer white-matter networks. For intra-layer networks, the increased FC was mainly located in the inferior longitudinal fasciculus, prefrontal cortex, and corpus callosum networks. For inter-layer networks, the increased FC of DM1 patients is mainly located in the superior corona radiata and deep networks.

Conclusion

Results demonstrated the abnormalities of white-matter functional connectivity in DM1 located in both intra-layer and inter-layer white-matter networks and suggested that the pathophysiology mechanism of DM1 may be related to the white-matter functional dysconnectivity. Furthermore, it may facilitate the treatment development of DM1.

Serotonin-norepinephrine reuptake inhibitor antidepressant effects on regional connectivity of the thalamus in persistent depressive disorder: evidence from two randomized, double-blind, placebo-controlled clinical trials

Previous neuroimaging studies have shown that serotonin-norepinephrine reuptake inhibitor antidepressants alter functional activity in large expanses of brain regions. However, it is not clear how these regions are systemically organized on a connectome level with specific topological properties, which may be crucial to revealing neural mechanisms underlying serotonin-norepinephrine reuptake inhibitor treatment of persistent depressive disorder. To investigate the effect of serotonin-norepinephrine reuptake inhibitor antidepressants on brain functional connectome reconfiguration in persistent depressive disorder and whether this reconfiguration promotes the improvement of clinical symptoms, we combined resting-state functional magnetic resonance imaging (fMRI) scans acquired in two randomized, double-blind, placebo-controlled trial studies of serotonin-norepinephrine reuptake inhibitor antidepressant treatment of patients with persistent depressive disorder. One was a randomized, double-blind, placebo-controlled trial of 10-week duloxetine medication treatment, which included 17 patients in duloxetine group and 17 patients in placebo group (ClinicalTrials.gov Identifier: NCT00360724); the other one was a randomized, double-blind, placebo-controlled trial of 12-week desvenlafaxine medication treatment, which included 16 patients in desvenlafaxine group and 15 patients in placebo group (ClinicalTrials.gov Identifier: NCT01537068). The 24-item Hamilton Depression Rating Scale was used to measure clinical symptoms, and graph theory was employed to examine serotonin-norepinephrine reuptake inhibitor antidepressant treatment effects on the topological properties of whole-brain functional connectome of patients with persistent depressive disorder. We adopted a hierarchical strategy to examine the topological property changes caused by serotonin-norepinephrine reuptake inhibitor antidepressant treatment, calculated their small-worldness, global integration, local segregation and nodal clustering coefficient in turn. Linear regression analysis was used to test associations of treatment, graph properties changes and clinical symptom response. Symptom scores were more significantly reduced after antidepressant than placebo administration (η 2 = 0.18). There was a treatment-by-time effect that optimized the functional connectome in a small-world manner, with increased global integration and increased nodal clustering coefficient in the bilateral thalamus (left thalamus η 2 = 0.21; right thalamus η 2 = 0.23). The nodal clustering coefficient increment of the right thalamus (ratio = 29.86; 95% confidence interval, -4.007 to -0.207) partially mediated the relationship between treatment and symptom improvement, and symptom improvement partially mediated (ratio = 21.21; 95% confidence interval, 0.0243-0.444) the relationship between treatment and nodal clustering coefficient increments of the right thalamus. Our study may indicate a putative mutually reinforcing association between nodal clustering coefficient increment of the right thalamus and symptom improvement from serotonin-norepinephrine reuptake inhibitor antidepressant treatments with duloxetine or desvenlafaxine.

Use of machine learning in predicting the efficacy of repetitive transcranial magnetic stimulation on treating depression based on functional and structural thalamo-prefrontal connectivity: A pilot study

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive, safe, and efficacious treatment for major depressive disorder (MDD). However, the antidepressant efficacy of rTMS greatly varies across individual patients. Thus, markers that can be used to predict the outcome of rTMS treatment at the individual level must be identified. Thalamo-cortical connectivity was abnormal in patients with MDD, and was normalized after rTMS treatment. In the current study, we investigated whether the resting-state functional and structural thalamo-cortical connectivity could be utilized to predict the rTMS treatment efficacy by employing support vector machine regression analysis. Results showed that the Hamilton Depression Scale scores of patients with MDD decreased after rTMS treatment. The functional connectivity of mediodorsal nucleus with prefrontal cortex predicted the rTMS treatment improvement, whereas the functional connectivity of other thalamic nuclei with cerebral cortex did not predict the treatment efficacy. The brain areas that contributed the most to the prediction were dorsal lateral prefrontal cortex, ventral lateral, and orbital and medial prefrontal areas. The improvement in the outcome of rTMS treatment could also be predicted by the thalamo-prefrontal structural connectivity. No statistically significantly difference in thalamo-cortical connectivity was observed between early improvers and early non-improvers. These results suggested that the thalamo-prefrontal connectivity can predict the rTMS treatment improvement. This study highlighted the crucial role of the thalamo-prefrontal connectivity as a neuroimaging marker in the treatment of depression via rTMS.

Abnormal regional spontaneous brain activities in white matter in patients with autism spectrum disorder

Previous studies have demonstrated patients with autism spectrum disorder (ASD) are accompanied by alterations of spontaneous brain activity in gray matter. However, whether the alterations of spontaneous brain activity exist in white matter remains largely unclear. In this study, 88 ASD patients and 87 typical controls (TCs) were included and regional homogeneity (ReHo) was calculated to characterize spontaneous brain activity in white matter. Voxel-wise two-sample t-tests were performed to investigate ReHo alterations, and cluster-level analyses were conducted to examine structural-functional coupling changes. Compared with TCs, the ASD group showed significantly decreased ReHo in the left superior corona radiata and left posterior limb of internal capsule, and decreased ReHo in the left anterior corona radiata with a trend level of significance. In addition, significantly weaker structural-functional coupling was observed in the left superior corona radiata and left posterior limb of internal capsule in ASD patients. Taken together, these findings highlighted abnormalities of white matter's regional spontaneous brain activity in ASD, which may provide new insights into the pathophysiological mechanisms of this disorder.

Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter

Intersubject variability is a fundamental characteristic of brain organizations, and not just "noise". Although intrinsic functional connectivity (FC) is unique to each individual and varies across brain gray-matter, the underlying mechanisms of intersubject functional variability in white-matter (WM) remain unknown. This study identified WMFC variabilities and determined the genetic basis and macroscale imaging in 45 healthy subjects. The functional localization pattern of intersubject variability across WM is heterogeneous, with most variability observed in the heteromodal cortex. The variabilities of heteromodal regions in expression profiles of genes are related to neuronal cells, involved in synapse-related and glutamic pathways, and associated with psychiatric disorders. In contrast, genes overexpressed in unimodal regions are mostly expressed in glial cells and were related to neurological diseases. Macroscopic variability recapitulates the functional and structural specializations and behavioral phenotypes. Together, our results provide clues to intersubject variabilities of the WMFC with convergent transcriptomic and cellular signatures, which relate to macroscale brain specialization.

AUTOMATED RECOGNITION OF DEPRESSION FROM FEWER-SHOT LEANING IN RESTING-STATE fMRI WITH ReHo USING DEEP CONVOLUTIONAL NEURAL NETWORK

Up to now, there is still the absence of research about depression recognition using resting-state functional magnetic resonance imaging (rest_fMRI) and deep learning. Previous studies have shown that regional homogeneity (ReHo) of rest_fMRI (rest_ReHo_fMRI) is a characterization of the functional synchronization of adjacent voxels in brain regions, and the mental and behavioral abnormalities in depression are due to an imbalance of ReHo synchronization in some brain functional areas. Accordingly, this paper presents a method for depression recognition using rest_ReHo_fMRI. First, the rest_ReHo_fMRI is extracted from the preprocessed rest-fMRI by calculation. Then, deep convolutional networks (such as VGG16) pretrained on ImageNet are used to automatically complete extracting the classification features from rest_ReHo_fMRI. Finally, the Kernel Extreme Learning Machine (KELM) was used to classify the depression. The results of the test set show that the proposed method achieves 89.07% in sensitivity and 89.74% in specificity. This study suggests that features of rest_ReHo_fMRI can be used as biomarkers to distinguish depression from normal people.

Altered Variability and Concordance of Dynamic Resting-State fMRI Indices in Patients With Attention Deficit Hyperactivity Disorder

Objective: Attention deficit hyperactivity disorder (ADHD) is a commonly diagnosed neuropsychiatric disorder in children, which is characterized by inattention, hyperactivity and impulsivity. Using resting-state functional magnetic resonance imaging (R-fMRI), the alterations of static and dynamic characteristics of intrinsic brain activity have been identified in patients with ADHD. Yet, it remains unclear whether the concordance among indices of dynamic R-fMRI is altered in ADHD. Methods: R-fMRI scans obtained from 50 patients with ADHD and 28 healthy controls (HC) were used for the current study. We calculated the regional dynamic changes in brain activity indices using the sliding-window method and compared the differences in variability of these indices between ADHD patients and HCs. Further, the concordance among these dynamic indices was calculated and compared. Finally, the relationship between variability/concordance of these indices and ADHD-relevant clinical test scores was investigated. Results: Patients with ADHD showed decreased variability of dynamic amplitude of low-frequency fluctuation (dALFF) in the left middle frontal gyrus and increased one in right middle occipital gyrus, as compared with the HCs. Besides, ADHD patients showed decreased voxel-wise concordance in the left middle frontal gyrus. Further, lower voxel-wise concordance in ADHD's left middle frontal gyrus was associated with more non-perseverative errors in Wisconsin Card Sorting Test, which reflects worse cognitive control. Conclusion: Our findings suggest that variability and concordance in dynamic brain activity may serve as biomarkers for the diagnosis of ADHD. Further, the decreased voxel-wise concordance is associated with deficit in cognitive control in ADHD patients.

Dimensional Analysis of Atypical Functional Connectivity of Major Depression Disorder and Bipolar Disorder

Literatures have reported considerable heterogeneity with atypical functional connectivity (FC) pattern of psychiatric disorders. However, traditional statistical methods are hard to explore this heterogeneity pattern. We proposed a "brain dimension" method to describe the atypical FC patterns of major depressive disorder and bipolar disorder (BD). The approach was firstly applied to a simulation dataset. It was then utilized to a real resting-state functional magnetic resonance imaging dataset of 47 individuals with major depressive disorder, 32 individuals with BD, and 52 well matched health controls. Our method showed a better ability to extract the FC dimensions than traditional methods. The results of the real dataset revealed atypical FC dimensions for major depressive disorder and BD. Especially, an atypical FC dimension which exhibited decreased FC strength of thalamus and basal ganglia was found with higher severity level of individuals with BD than the ones with major depressive disorder. This study provided a novel "brain dimension" method to view the atypical FC patterns of major depressive disorder and BD and revealed shared and specific atypical FC patterns between major depressive disorder and BD.

Distributed Functional Connectome of White Matter in Patients With Functional Dyspepsia

Purpose: We aimed to find out the distributed functional connectome of white matter in patients with functional dyspepsia (FD). Methods: 20 patients with FD and 24 age- and gender-matched healthy controls were included into the study. The functional connectome of white matter and graph theory were used to these participants. Two-sample t-test was used for the detection the abnormal graph properties in FD. Pearson correlation was used for the relationship between properties and the clinical and neuropshychological information. Results: Patients with FD and healthy controls showed small-world properties in functional connectome of white matter. Compared with healthy controls, the FD group showed decreased global properties (Cp, S, Eglobal, and Elocal). Four pairs of fiber bundles that are connected to the frontal lobe, insula, and thalamus were affected in the FD group. Duration and Pittsburgh Sleep Quality Index positively correlated with the betweenness centrality of white matter regions of interest. Conclusion: FD patients turned to a non-optimized functional organization of WM brain network. Frontal lobe, insula, and thalamus were key regions in brain information exchange of FD. It provided some novel imaging evidences for the mechanism of FD.

Outcome Value and Task Aversiveness Impact Task Procrastination through Separate Neural Pathways

The temporal decision model of procrastination has proposed that outcome value and task aversiveness are two separate aspects accounting for procrastination. If true, the human brain is likely to implicate separate neural pathways to mediate the effect of outcome value and task aversiveness on procrastination. Outcome value is plausibly constructed via a hippocampus-based pathway because of the hippocampus's unique role in episodic prospection. In contrast, task aversiveness might be represented through an amygdala-involved pathway. In the current study, participants underwent fMRI scanning when viewing both tasks and future outcomes, without any experimental instruction imposed. The results revealed that outcome value increased activations in the caudate, and suppressed procrastination through a hippocampus-caudate pathway. In contrast, task aversiveness increased activations in the anterior insula, and increased procrastination via an amygdala-insula pathway. In sum, this study demonstrates that people can incorporate both outcome value and task aversiveness into task valuation to decide whether to procrastinate or not; and it elucidates the separate neural pathways via which this occurs.

The functional connectome predicts feeling of stress on regular days and during the COVID-19 pandemic

Although many studies have explored the neural mechanism of the feeling of stress, to date, no effort has been made to establish a model capable of predicting the feeling of stress at the individual level using the resting-state functional connectome. Although individuals may be confronted with multidimensional stressors during the coronavirus disease 2019 (COVID-19) pandemic, their appraisal of the impact and severity of these events might vary. In this study, connectome-based predictive modeling (CPM) with leave-one-out cross-validation was conducted to predict individual perceived stress (PS) from whole-brain functional connectivity data from 817 participants. The results showed that the feeling of stress could be predicted by the interaction between the default model network and salience network, which are involved in emotion regulation and salience attribution, respectively. Key nodes that contributed to the prediction model comprised regions mainly located in the limbic systems and temporal lobe. Critically, the CPM model of PS based on regular days can be generalized to predict individual PS levels during the COVID-19 pandemic, which is a multidimensional, uncontrollable stressful situation. The stability of the results was demonstrated by two independent datasets. The present work not only expands existing knowledge regarding the neural mechanism of PS but also may help identify high-risk individuals in healthy populations.

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Perceived stress (PS) can be predicated by resting-state functional connectome.

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PS can be predicated by interaction between default model and salience network.

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Key nodes of the prediction model located in limbic systems and temporal lobe.

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psCPM of regular days generalized to predict PS level in the COVID-19 pandemic.