The stage-specifically accelerated brain aging in never-treated first-episode patients with depression

Accelerated biological brain aging in major depressive disorder

Major depressive disorder (MDD) patients commonly encounter multiple types of functional disabilities, such as social, physical, and role functioning. MDD is related to an accreted risk of brain atrophy, aging-associated brain diseases, and mortality. Based on recently available studies, there are correlations between notable biological brain aging and MDD in adulthood. Despite several clinical and epidemiological studies that associate MDD with aging phenotypes, the underlying mechanisms in the brain remain unknown. The key areas in the study of biological brain aging in MDD are structural brain aging, impairment in functional connectivity, and the impact on cognitive function and age-related disorders. Various measurements have been used to determine the severity of brain aging, such as the brain age gap estimate (BrainAGE) or brain-predicted age difference (BrainPAD). This review summarized the current results of brain imaging data on the similarities between the manifestation of brain structural changes and the age-associated processes in MDD. This review also provided recent evidence of BrainPAD or BrainAGE scores in MDD, brain structural abnormalities, and functional connectivity, which are commonly observed between MDD and age-associated processes. It serves as a basis of current reference for future research on the potential areas of investigation for diagnostic, preventive, and potentially therapeutic purposes for brain aging in MDD.

Gray matter atrophy is constrained by normal structural brain network architecture in depression

BACKGROUND

There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression.

METHODS

In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis.

RESULTS

In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset.

CONCLUSIONS

These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

Functional and structural alterations in different durations of untreated illness in the frontal and parietal lobe in major depressive disorder

Major depressive disorder (MDD) is one of the most disabling illnesses that profoundly restricts psychosocial functions and impairs quality of life. However, the treatment rate of MDD is surprisingly low because the availability and acceptability of appropriate treatments are limited. Therefore, identifying whether and how treatment delay affects the brain and the initial time point of the alterations is imperative, but these changes have not been thoroughly explored. We investigated the functional and structural alterations of MDD for different durations of untreated illness (DUI) using regional homogeneity (ReHo) and voxel-based morphometry (VBM) with a sample of 125 treatment-naïve MDD patients and 100 healthy controls (HCs). The MDD patients were subgrouped based on the DUI, namely, DUI ≤ 1 M, 1 < DUI ≤ 6 M, 6 < DUI ≤ 12 M, and 12 < DUI ≤ 48 M. Subgroup comparison (MDD with different DUIs) was applied to compare ReHo and grey matter volume (GMV) extracted from clusters of regions with significant differences (the pooled MDD patients relative to HCs). Correlations and mediation effects were analysed to estimate the relationships between the functional and structural neuroimaging changes and clinical characteristics. MDD patients exhibited decreased ReHo in the left postcentral gyrus and precentral gyrus and reduced GMV in the left middle frontal gyrus and superior frontal gyrus relative to HCs. The initial functional abnormalities were detected after being untreated for 1 month, whereas this duration was 3 months for GMV reduction. Nevertheless, a transient increase in ReHo was observed after being untreated for 3 months. No significant differences were discovered between HCs and MDD patients with a DUI less than 1 month or among MDD patients with different DUIs in either ReHo or GMV. Longer DUI was related to reduced ReHo with GMV as mediator in MDD patients. We identified disassociated functional and anatomical alterations in treatment-naïve MDD patients at different time points in distinct brain regions at the early stage of the disease. Additionally, we also discovered that GMV mediated the relationship between a longer DUI and diminished ReHo in MDD patients, disclosing the latent deleterious and neuro-progressive implications of DUI on both the structure and function of the brain and indicating the necessity of early treatment of MDD.

BrainAGE, brain health, and mental disorders: A systematic review

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.

The Role of Puberty and Sex on Brain Structure in Adolescents With Anxiety Following Concussion

BACKGROUND

Adolescence represents a window of vulnerability for developing psychological symptoms following concussion, especially in girls. Concussion-related lesions in emotion regulation circuits may help explain these symptoms. However, the contribution of sex and pubertal maturation remains unclear. Using the neurite density index (NDI) in emotion regulation tracts (left/right cingulum bundle [CB], forceps minor [FMIN], and left/right uncinate fasciculus), we sought to elucidate these relationships.

METHODS

No adolescent had a history of anxiety and/or depression. The Screen for Child Anxiety Related Emotional Disorders and Children's Depression Rating Scale were used at scan to assess anxiety and depressive symptoms in 55 concussed adolescents (41.8% girls) and 50 control adolescents with no current/history of concussion (44% girls). We evaluated if a mediation-moderation model including the NDI (mediation) and sex or pubertal status (moderation) could help explain this relationship.

RESULTS

Relative to control adolescents, concussed adolescents showed higher anxiety (p = .003) and lower NDI, with those at more advanced pubertal maturation showing greater abnormalities in 4 clusters: the left CB frontal (p = .002), right CB frontal (p = .011), FMIN left-sided (p = .003), and FMIN right-sided (p = .003). Across all concussed adolescents, lower NDI in the left CB frontal and FMIN left-sided clusters partially mediated the association between concussion and anxiety, with the CB being specific to female adolescents. These effects did not explain depressive symptoms.

CONCLUSIONS

Our findings indicate that lower NDI in the CB and FMIN may help explain anxiety following concussion and that adolescents at more advanced (vs less advanced) status of pubertal maturation may be more vulnerable to concussion-related injuries, especially in girls.

Resting-state cerebral blood flow and functional connectivity abnormalities in depressed patients with childhood maltreatment: Potential biomarkers of vulnerability?

AIM

Childhood maltreatment (CM) is an important risk factor for major depressive disorder (MDD). This study aimed to explore the specific effect of CM on cerebral blood flow (CBF) and brain functional connectivity (FC) in MDD patients.

METHODS

A total of 150 subjects were collected including 55 MDD patients with CM, 34 MDD patients without CM, 19 healthy controls (HC) with CM, and 42 HC without CM. All subjects completed MRI scans and neuropsychological tests. Two-way analysis of covariance was used to detect the main and interactive effects of disease and CM on CBF and FC across subjects. Then, partial correlation analyses were conducted to explore the behavioral significance of altered CBF and FC in MDD patients. Finally, a support vector classifier model was applied to differentiate MDD patients.

RESULTS

MDD patients represented increased CBF in bilateral temporal lobe and decreased CBF in right visual cortex. Importantly, significant depression-by-CM interactive effects on CBF were primarily located in the frontoparietal regions, including orbitofrontal cortex (OFC), lateral prefrontal cortex (PFC), and parietal cortex. Moreover, significant FC abnormalities were seen in OFC-PFC and frontoparietal-visual cortex. Notably, the abnormal CBF and FC were significantly associated with behavioral performance. Finally, a combination of altered CBF and FC behaved with a satisfactory classification ability to differentiate MDD patients.

CONCLUSIONS

These results highlight the importance of frontoparietal and visual cortices for MDD with CM experience, proposing a potential neuroimaging biomarker for MDD identification.

Two distinct neuroanatomical subtypes of migraine without aura revealed by heterogeneity through discriminative analysis

The neurobiological heterogeneity in migraine is poorly studied, resulting in conflicting neuroimaging findings. This study used a newly proposed method based on gray matter volumes (GMVs) to investigate objective neuroanatomical subtypes of migraine. Structural MRI and clinical measures of 31 migraine patients without aura and 33 matched healthy controls (HCs) were explored. Firstly, we investigated whether migraine patients exhibited higher interindividual variability than HCs in terms of GMVs. Then, heterogeneity through discriminative analysis (HYDRA) was applied to categorize migraine patients into distinct subtypes by regional volumetric measures of GMVs. Voxel-wise volume and clinical characteristics among different subtypes were also explored. Migraine patients without aura exhibited higher interindividual GMVs variability. Two distinct and reproducible neuroanatomical subtypes of migraine were revealed. These two subtypes exhibited opposite neuroanatomical aberrances compared to HCs. Subtype 1 showed widespread decreased GMVs, while Subtype 2 showed increased GMVs in limited regions. The total intracranial volume was significantly positively correlated with cognitive function in Subtype 2. Subtype 1 showed significantly longer illness duration and less cognitive scores compared to Subtype 2. The present study shows that migraine patients without aura have high structural heterogeneity and uncovers two distinct and robust neuroanatomical subtypes, which provide a possible explanation for conflicting neuroimaging findings.

Dynamic evolution of brain structural patterns in liver transplantation recipients: a longitudinal study based on 3D convolutional neuronal network model

OBJECTIVES

To evaluate the dynamic evolution process of overall brain health in liver transplantation (LT) recipients, we employed a deep learning-based neuroanatomic biomarker to measure longitudinal changes of brain structural patterns before and 1, 3, and 6 months after surgery.

METHODS

Because of the ability to capture patterns across all voxels from a brain scan, the brain age prediction method was adopted. We constructed a 3D-CNN model through T1-weighted MRI of 3609 healthy individuals from 8 public datasets and further applied it to a local dataset of 60 LT recipients and 134 controls. The predicted age difference (PAD) was calculated to estimate brain changes before and after LT, and the network occlusion sensitivity analysis was used to determine the importance of each network in age prediction.

RESULTS

The PAD of patients with cirrhosis increased markedly at baseline (+ 5.74 years) and continued to increase within one month after LT (+ 9.18 years). After that, the brain age began to decrease gradually, but it was still higher than the chronological age. The PAD values of the OHE subgroup were higher than those of the no-OHE, and the discrepancy was more obvious at 1-month post-LT. High-level cognition-related networks were more important in predicting the brain age of patients with cirrhosis at baseline, while the importance of primary sensory networks increased temporarily within 6-month post-LT.

CONCLUSIONS

The brain structural patterns of LT recipients showed inverted U-shaped dynamic change in the early stage after transplantation, and the change in primary sensory networks may be the main contributor.

KEY POINTS

• The recipients' brain structural pattern showed an inverted U-shaped dynamic change after LT. • The patients' brain aging aggravated within 1 month after surgery, and the subset of patients with a history of OHE was particularly affected. • The change of primary sensory networks is the main contributor to the change in brain structural patterns.

Resolving heterogeneity in depression using individualized structural covariance network analysis

BACKGROUND

Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis.

METHODS

T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges.

RESULTS

As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms.

CONCLUSIONS

In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

Altered topology of individual brain structural covariance networks in major depressive disorder

BACKGROUND

The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks.

METHODS

We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics.

RESULTS

Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex.

CONCLUSIONS

The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.

Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization

The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.

Exploring New Mechanism of Depression from the Effects of Virus on Nerve Cells

Depression is a common neuropsychiatric disorder with long-term recurrent depressed mood, pain and despair, pessimism and anxiety, and even suicidal tendencies as the main symptoms. Depression usually induces or aggravates the development of other related diseases, such as sleep disorders and endocrine disorders. In today's society, the incidence of depression is increasing worldwide, and its pathogenesis is complex and generally believed to be related to genetic, psychological, environmental, and biological factors. Current studies have shown the key role of glial cells in the development of depression, and it is noteworthy that some recent evidence suggests that the development of depression may be closely related to viral infections, such as SARS-CoV-2, BoDV-1, ZIKV, HIV, and HHV6, which infect the organism and cause some degree of glial cells, such as astrocytes, oligodendrocytes, and microglia. This can affect the transmission of related proteins, neurotransmitters, and cytokines, which in turn leads to neuroinflammation and depression. Based on the close relationship between viruses and depression, this paper provides an in-depth analysis of the new mechanism of virus-induced depression, which is expected to provide a new perspective on the mechanism of depression and a new idea for the diagnosis of depression in the future.

Accelerated Brain Aging in Adults With Major Depressive Disorder Predicts Poorer Outcome With Sertraline: Findings From the EMBARC Study

BACKGROUND

Major depressive disorder (MDD) may be associated with accelerated brain aging (higher brain age than chronological age). This report evaluated whether brain age is a clinically useful biomarker by checking its test-retest reliability using magnetic resonance imaging scans acquired 1 week apart and by evaluating the association of accelerated brain aging with symptom severity and antidepressant treatment outcomes.

METHODS

Brain age was estimated in participants of the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study using T1-weighted structural magnetic resonance imaging (MDD n = 290; female n = 192; healthy control participants n = 39; female n = 24). Intraclass correlation coefficient was used for baseline-to-week-1 test-retest reliability. Association of baseline Δ brain age (brain age minus chronological age) with Hamilton Depression Rating Scale-17 and Concise Health Risk Tracking Self-Report domains (impulsivity, suicide propensity [measures: pessimism, helplessness, perceived lack of social support, and despair], and suicidal thoughts) were assessed at baseline (linear regression) and during 8-week-long treatment with either sertraline or placebo (repeated-measures mixed models).

RESULTS

Mean ± SD baseline chronological age, brain age, and Δ brain age were 37.1 ± 13.3, 40.6 ± 13.1, and 3.1 ± 6.1 years in MDD and 37.1 ± 14.7, 38.4 ± 12.9, and 0.6 ± 5.5 years in healthy control groups, respectively. Test-retest reliability was high (intraclass correlation coefficient = 0.98-1.00). Higher baseline Δ brain age in the MDD group was associated with higher baseline impulsivity and suicide propensity and predicted smaller baseline-to-week-8 reductions in Hamilton Depression Rating Scale-17, impulsivity, and suicide propensity with sertraline but not with placebo.

CONCLUSIONS

Brain age is a reliable and potentially clinically useful biomarker that can prognosticate antidepressant treatment outcomes.

Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults

Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.

Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies

Evidence suggests that psychopathology is associated with an advanced brain ageing process, typically mapped using machine learning models that predict an individual's age based on structural neuroimaging data. The brain predicted age difference (brain-PAD) captures the deviation of brain age from chronological age. Substantial heterogeneity between studies has introduced uncertainty regarding the magnitude of the brain-PAD in adult psychopathology. The present meta-analysis aimed to quantify structural MRI-based brain-PAD in adult psychotic and mood disorders, while addressing possible sources of heterogeneity related to diagnosis subtypes, segmentation method, age and sex. Clinical factors influencing brain ageing in axis 1 psychiatric disorders were systematically reviewed. Thirty-three studies were included for review. A random-effects meta-analysis revealed a brain-PAD of +3.12 (standard error = 0.49) years in psychotic disorders (n = 16 studies), +2.04 (0.10) years in bipolar disorder (n = 5), and +0.90 (0.20) years in major depression (n = 7). An exploratory meta-analysis found a brain-PAD of +1.57 (0.67) in first episode psychosis (n = 4), which was smaller than that observed in psychosis and schizophrenia (n = 10, +3.87 (0.61)). Patient mean age significantly explained heterogeneity in effect size estimates in psychotic disorders, but not mood disorders. The systematic review determined that clinical factors, such as higher symptom severity, may be associated with a larger brain-PAD in psychopathology. In conclusion, larger structural MRI-based brain-PAD was confirmed in adult psychopathology. Preliminary evidence was obtained that brain ageing is greater in those with prolonged duration of psychotic disorders. Accentuated brain ageing may underlie the cognitive difficulties experienced by some patients, and may be progressive in nature.

Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants

Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies suggest that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject's chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a +4.43 years (p < 0.0001, Cohen's d = 0.31, 95% CI: 2.23-3.88) higher brain-predicted age difference (brain-PAD) compared to controls. In the MDD subgroup, we observe a statistically significant +2.09 years (p < 0.05, Cohen's d = 0.134525) brain-PAD in antidepressant users compared to medication-free patients. The statistical relationship observed is further checked by three different machine learning algorithms. The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients. The utilization of functional brain connectivity for age estimation verifies existing findings from a new dimension.

Structural and Functional Brain Changes in Patients With Classic Trigeminal Neuralgia: A Combination of Voxel-Based Morphometry and Resting-State Functional MRI Study

Objectives

Brain structural and functional abnormalities have been separately reported in patients with classic trigeminal neuralgia (CTN). However, whether and how the functional deficits are related to the structural alterations remains unclear. This study aims to investigate the anatomical and functional deficits in patients with CTN and explore their association.

Methods

A total of 34 patients with CTN and 29 healthy controls (HCs) with age- and gender-matched were recruited. All subjects underwent structural and resting-state functional magnetic resonance imaging (fMRI) scanning and neuropsychological assessments. Voxel-based morphometry (VBM) was applied to characterize the alterations of gray matter volume (GMV). The amplitude of low-frequency fluctuation (ALFF) method was used to evaluate regional intrinsic spontaneous neural activity. Further correlation analyses were performed between the structural and functional changes and neuropsychological assessments.

Results

Compared to the HCs, significantly reduced GMV was revealed in the right hippocampus, right fusiform gyrus (FFG), and temporal-parietal regions (the left superior/middle temporal gyrus, left operculo-insular gyrus, left inferior parietal lobule, and right inferior temporal gyrus) in patients with CTN. Increased functional activity measured by zALFF was observed mainly in the limbic system (the bilateral hippocampus and bilateral parahippocampal gyrus), bilateral FFG, basal ganglia system (the bilateral putamen, bilateral caudate, and right pallidum), left thalamus, left cerebellum, midbrain, and pons. Moreover, the right hippocampus and FFG were the overlapped regions with both functional and anatomical deficits. Furthermore, GMV in the right hippocampus was negatively correlated with pain intensity, anxiety, and depression. GMV in the right FFG was negatively correlated with illness duration. The zALFF value in the right FFG was positively correlated with anxiety.

Conclusion

Our results revealed concurrent structural and functional changes in patients with CTN, indicating that the CTN is a brain disorder with structural and functional abnormalities. Moreover, the overlapping structural and functional changes in the right hippocampus and FFG suggested that anatomical and functional changes might alter dependently in patients with CTN. These findings highlight the vital role of hippocampus and FFG in the pathophysiology of CTN.

Two distinct subtypes of obsessive compulsive disorder revealed by a framework integrating multimodal neuroimaging information

Patients with obsessive compulsive disorder (OCD) exhibit tremendous heterogeneity in structural and functional neuroimaging aberrance. However, most previous studies just focus on group‐level aberrance of a single modality ignoring heterogeneity and multimodal features. On that account, we aimed to uncover OCD subtypes integrating structural and functional neuroimaging features with the help of a multiview learning method and examined multimodal aberrance for each subtype. Ninety‐nine first‐episode untreated patients with OCD and 104 matched healthy controls (HCs) undergoing structural and functional MRI were included in this study. Voxel‐based morphometric and amplitude of low‐frequency fluctuation (ALFF) were adopted to assess gray matter volumes (GMVs) and the spontaneous neuronal fluctuations respectively. Structural/functional distance network was obtained by calculating Euclidean distance between pairs of regional GMVs/ALFF values across patients. Similarity network fusion, one of multiview learning methods capturing shared and complementary information from multimodal data sources, was used to fuse multimodal distance networks into one fused network. Then spectral clustering was adopted to categorize patients into subtypes. As a result, two robust subtypes were identified. These two subtypes presented opposite GMV aberrance and distinct ALFF aberrance compared with HCs while shared indistinguishable clinical and demographic features. In addition, these two subtypes exhibited opposite structure–function difference correlation reflecting distinct adaptive modifications between multimodal aberrance. Altogether, these results uncover two objective subtypes with distinct multimodal aberrance and provide a new insight into taxonomy of OCD.

Two distinct subtypes of obsessive compulsive disorder revealed by heterogeneity through discriminative analysis

Neurobiological heterogeneity in obsessive compulsive disorder (OCD) is understudied leading to conflicting neuroimaging findings. Therefore, we investigated objective neuroanatomical subtypes of OCD by adopting a newly proposed method based on gray matter volumes (GMVs). GMVs were derived from T1‐weighted anatomical images of patients with OCD (n = 100) and matched healthy controls (HCs; n = 106). We first inquired whether patients with OCD presented higher interindividual variability HCs in terms of GMVs. Then, we identified distinct subtypes of OCD by adopting heterogeneity through discriminative analysis (HYDRA), where regional GMVs were treated as features. Patients with OCD presented higher interindividual variability than HCs, suggesting a high structural heterogeneity of OCD. HYDRA identified two distinct robust subtypes of OCD presenting opposite neuroanatomical aberrances compared with HCs, while sharing indistinguishable clinical and demographic features. Specifically, Subtype 1 exhibited widespread increased GMVs in cortical and subcortical regions, including the orbitofrontal gyrus, right anterior insula, bilateral hippocampus, and bilateral parahippocampus and cerebellum. Subtype 2 demonstrated overall decreased GMVs in regions such as the orbitofrontal gyrus, right anterior insula, and precuneus. When mixed together, none of patients presented significant differences compared with HCs. In addition, the total intracranial volume of Subtype 2 was significantly correlated with the total score of the Yale–Brown Obsessive Compulsive Scale while that of Subtype 1 was not. These results identified two distinct neuroanatomical subtypes, providing a possible explanation for conflicting neuroimaging findings, and proposed a potential objective taxonomy contributing to precise clinical diagnosis and treatment in OCD.

Alteration in Functional Magnetic Resonance Imaging Signal Complexity Across Multiple Time Scales in Patients With Migraine Without Aura

Background

Migraine is a primary neurological disorder associated with complex brain activity. Recently, mounting evidence has suggested that migraine is underpinned by aberrant dynamic brain activity characterized by linear and non-linear changes across a variety of time scales. However, the abnormal dynamic brain activity at different time scales is still unknown in patients with migraine without aura (MWoA). This study aimed to assess the altered patterns of brain activity dynamics over different time scales and the potential pathophysiological mechanisms of alterations in patients with MWoA.

Methods

Multiscale entropy in 50 patients and 20 healthy controls (HCs) was calculated to investigate the patterns and altered brain complexity (BC) across five different time scales. Spearman rank correlation analysis between BC in regions showing significant intergroup differences and clinical scores (i.e., frequency of migraine attacks, duration, headache impact test) was conducted in patients with MWoA.

Results

The spatial distribution of BC varied across different time scales. At time scale1, BC was higher in the posterior default mode network (DMN) across participants. Compared with HCs, patients with MWoA had higher BC in the DMN and sensorimotor network. At time scale2, BC was mainly higher in the anterior DMN across participants. Patients with MWoA had higher BC in the sensorimotor network. At time scale3, BC was mainly higher in the frontoparietal network across participants. Patients with MWoA had increased BC in the parietal gyrus. At time scale4, BC is mainly higher in the sensorimotor network. Patients with MWoA had higher BC in the postcentral gyrus. At time scale5, BC was mainly higher in the DMN. Patients with MWoA had lower BC in the posterior DMN. In particular, BC values in the precuneus and paracentral lobule significantly correlated with clinical symptoms.

Conclusion

Migraine is associated with alterations in dynamic brain activity in the sensorimotor network and DMN over multiple time scales. Time-varying BC within these regions could be linked to instability in pain transmission and modulation. Our findings provide new evidence for the hypothesis of abnormal dynamic brain activity in migraine.

Integrative Functional, Molecular, and Transcriptomic Analyses of Altered Intrinsic Timescale Gradient in Depression

The pathophysiology and pharmacology of depression are hypothesized to be related to the imbalance of excitation–inhibition that gives rise to hierarchical dynamics (or intrinsic timescale gradient), further supporting a hierarchy of cortical functions. On this assumption, intrinsic timescale gradient is theoretically altered in depression. However, it remains unknown. We investigated altered intrinsic timescale gradient recently developed to measure hierarchical brain dynamics gradient and its underlying molecular architecture and brain-wide gene expression in depression. We first presented replicable intrinsic timescale gradient in two independent Chinese Han datasets and then investigated altered intrinsic timescale gradient and its possible underlying molecular and transcriptional bases in patients with depression. As a result, patients with depression showed stage-specifically shorter timescales compared with healthy controls according to illness duration. The shorter timescales were spatially correlated with monoamine receptor/transporter densities, suggesting the underlying molecular basis of timescale aberrance and providing clues to treatment. In addition, we identified that timescale aberrance-related genes ontologically enriched for synapse-related and neurotransmitter (receptor) terms, elaborating the underlying transcriptional basis of timescale aberrance. These findings revealed atypical timescale gradient in depression and built a link between neuroimaging, transcriptome, and neurotransmitter information, facilitating an integrative understanding of depression.

Accelerated Brain Aging in Patients With Obsessive-Compulsive Disorder

Obsessive-compulsive disorder (OCD) may be accompanied by an accelerated structural decline of the brain with age compared to healthy controls (HCs); however, this has yet to be proven. To answer this question, we built a brain age prediction model using mean gray matter volumes of each brain region as features, which were obtained by voxel-based morphometry derived from T1-weighted MRI scans. The prediction model was built using two Chinese Han datasets (dataset 1, N = 106 for HCs and N = 90 for patients with OCD; dataset 2, N = 270 for HCs) to evaluate its performance. Then, a new prediction model was trained using data for HCs in dataset 1 and applied to patients with OCD to investigate the brain aging trajectory. The brain-predicted age difference (brain-PAD) scores, defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with matched HCs in dataset 1. It was demonstrated that the prediction model performs consistently across different datasets. Patients with OCD presented higher brain-PAD scores than matched HCs, suggesting that patients with OCD presented accelerated brain aging. In addition, brain-PAD scores were negatively correlated with the duration of illness, suggesting that brain-PAD scores might capture progressive structural brain changes. These results identified accelerated brain aging in patients with OCD for the first time and deepened our understanding of the pathogenesis of OCD.

Distinct profiles of functional connectivity density aberrance in Alzheimer's disease and mild cognitive impairment

INTRODUCTION

Investigating the neuroimaging changes from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of great significance. However, the details about the distinct functional characteristics of AD and MCI remain unknown.

METHODS

In this study, we investigated distinct profiles of functional connectivity density (FCD) differences between AD and MCI compared with the normal population, aiming to depict the progressive brain changes from MCI to AD. As a data-driven method, FCD measures the profiles of FC for the given voxel at different scales. Resting-state functional magnetic resonance imaging (fMRI) images were obtained from patients with AD and MCI and matched healthy controls (HCs). One-way ANCOVA was used to investigate (global, long-range, and local) FCD differences among the three groups followed by post-hoc analysis controlling age, sex, and head motion.

RESULTS

The three groups exhibited significant global FCD differences in the superior frontal gyrus. The post-hoc results further showed that patients with AD had a significant increase in global FCD values than those with MCI and HCs. Patients with MCI exhibited an increased trend compared with HCs. We further identified brain regions contributing to the observed global FCD differences by conducting seed-based FC analysis. We also identified that the observed global FCD differences were the additive effects of altered FC between the superior frontal gyrus and the posterior default model network.

DISCUSSION

These results depicted the global information communication capability impairment in AD and MCI providing a new insight into the progressive brain changes from MCI to AD.

The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional Signatures

Recent studies combining neuroimaging with machine learning methods successfully infer an individual’s brain age, and its discrepancy with the chronological age is used to identify age-related diseases. However, which brain networks play decisive roles in brain age prediction and the underlying biological basis of brain age remain unknown. To answer these questions, we estimated an individual’s brain age in the Southwest University Adult Lifespan Dataset (N = 492) from the gray matter volumes (GMV) derived from T1-weighted MRI scans by means of Gaussian process regression. Computational lesion analysis was performed to determine the importance of each brain network in brain age prediction. Then, we identified brain age-related genes by using prior brain-wide gene expression data, followed by gene enrichment analysis using Metascape. As a result, the prediction model successfully inferred an individual’s brain age and the computational lesion prediction results identified the central executive network as a vital network in brain age prediction (Steiger’s Z = 2.114, p = 0.035). In addition, the brain age-related genes were enriched in Gene Ontology (GO) processes/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways grouped into numbers of clusters, such as regulation of iron transmembrane transport, synaptic signaling, synapse organization, retrograde endocannabinoid signaling (e.g., dopaminergic synapse), behavior (e.g., memory and associative learning), neurotransmitter secretion, and dendrite development. In all, these results reveal that the GMV of the central executive network played a vital role in predicting brain age and bridged the gap between transcriptome and neuroimaging promoting an integrative understanding of the pathophysiology of brain age.

Tracking positive and negative symptom improvement in first-episode schizophrenia treated with risperidone using individual-level functional connectivity

BACKGROUND

To improve the treatment outcomes of patients with schizophrenia, research efforts have focused on identifying brain-based markers of treatment response. Personal characteristics regarding disease-related behaviors likely stem from inter-individual variability in the organization of brain functional systems. This study aimed to track dimension-specific changes in psychotic symptoms following risperidone treatment using individual-level functional connectivity (FC).

METHODS

A reliable cortical parcellation approach that accounts for individual heterogeneity in cortical functional anatomy was used to localize functional regions in a longitudinal cohort, consisting of 42 drug-naive first-episodes schizophrenia (FES) patients at baseline and after 8 weeks of risperidone treatment. FC was calculated in individually specified brain regions and used to predict the baseline severity and improvement of positive and negative symptoms in FES.

RESULTS

Distinct sets of individual-specific FC were separately associated with the positive and negative symptom burden at baseline, which could be used to track the corresponding symptom resolution in FES patients following risperidone treatment. Between-network connections of the fronto-parietal network (FPN) contributed the most to predicting the positive symptom domain. A combination of between-network connections of the default mode network, FPN, and within-network connections of the FPN contributed markedly to the prediction model of negative symptom.

CONCLUSION

This novel study, which accounts for individual brain variation, take a step toward establishing individual-specific theranostic biomarkers in schizophrenia.

The stage-specifically accelerated brain aging in never-treated first-episode patients with depression

Depression associated with structural brain abnormalities is hypothesized to be related with accelerated brain aging. However, there is far from a unified conclusion because of clinical variations such as medication status, cumulative illness burden. To explore whether brain age is accelerated in never‐treated first‐episode patients with depression and its association with clinical characteristics, we constructed a prediction model where gray matter volumes measured by voxel‐based morphometry derived from T1‐weighted MRI scans were treated as features. The prediction model was first validated using healthy controls (HCs) in two Chinese Han datasets (Dataset 1, N = 130 for HCs and N = 195 for patients with depression; Dataset 2, N = 270 for HCs) separately or jointly, then the trained prediction model using HCs (N = 400) was applied to never‐treated first‐episode patients with depression (N = 195). The brain‐predicted age difference (brain‐PAD) scores defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with age‐, gender‐, educational level‐matched HCs in Dataset 1. Overall, patients presented higher brain‐PAD scores suggesting patients with depression having an “older” brain than expected. More specially, this difference occurred at illness onset (illness duration <3 months) and following 2 years then disappeared as the illness further advanced (>2 years) in patients. This phenomenon was verified by another data‐driven method and significant correlation between brain‐PAD scores and illness duration in patients. Our results reveal that accelerated brain aging occurs at illness onset and suggest it is a stage‐dependent phenomenon in depression.