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Major depressive disorder (MDD) has been shown to involve widespread changes in low‐level sensorimotor and higher‐level cognitive functions. Recent research found that a primary‐to‐transmodal gradient could capture a cortical hierarchical organization ranging from perception and action to cognition in healthy subjects, but a prominent gradient dysfunction in MDD patients. However, whether and how this cortical gradient is linked to subcortical impairments and whether it is reflected in the microscale neurotransmitter systems and cell type‐specific transcriptional signatures remain largely unknown. Data were acquired from 323 MDD patients and 328 sex‐ and age‐matched healthy controls derived from the REST‐meta‐MDD project, and the human brain neurotransmitter systems density maps and gene expression data were drawn from two publicly available datasets. We investigated alterations of the primary‐to‐transmodal gradient in MDD patients and their correlations with clinical symptoms of depression and anxiety, as well as  their paralleled subcortical impairments. The correlations between MDD‐related gradient alterations and densities of the neurotransmitter systems and gene expression information were assessed, respectively. The results demonstrated that MDD patients had a compressed primary‐to‐transmodal gradient accompanied by paralleled alterations in subcortical regions including the caudate, amygdala, and thalamus. The case–control gradient differences were spatially correlated with the densities of the neurotransmitter systems including the serotonin and dopamine receptors, and meanwhile with gene expression enriched in astrocytes, excitatory and inhibitory neuronal cells. These findings mapped the paralleled subcortical impairments in cortical hierarchical organization and also helped us understand the possible molecular and cellular substrates of the co‐occurrence of high‐level cognitive impairments with low‐level sensorimotor abnormalities in MDD.

The human brain presents ongoing temporal fluctuations whose dynamic range indicates the capacity of information processing and can be approximately quantified with entropy. Using functional magnetic resonance imaging (fMRI), recent studies have shown a stable distribution pattern of temporal brain entropy (tBEN) in healthy subjects, which may be affected by neuropsychiatric diseases such as schizophrenia. Assessing tBEN may reciprocally provide a new tool to characterize those disorders. The current study aimed to identify tBEN changes in schizophrenia patients using publicly available data from the Centers of Biomedical Research Excellence (COBRE) project. Forty-three schizophrenia patients and 59 sex- and age-matched healthy control subjects were included, and tBEN was calculated from their resting-state fMRI scans. Compared with healthy controls, patients showed decreased tBEN in the right middle prefrontal cortex, bilateral thalamus, right hippocampus and bilateral caudate and increased tBEN in the left lingual gyrus, left precuneus, right fusiform face area and right superior occipital gyrus. In schizophrenia patients, tBEN in the left cuneus and middle occipital gyrus was negatively correlated with the positive and negative syndrome scores (PANSS). Age of onset was inversely correlated with tBEN in the right fusiform gyrus and left insula. Our findings demonstrate a detrimental tBEN reduction in schizophrenia that is related to clinical characteristics. The tBEN increase in a few regions might be a result of tBEN redistribution across the whole brain in schizophrenia.

In recent years, machine learning techniques have received increasing attention as a promising approach to differentiating patients from healthy subjects. Therefore, some resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used interregional functional connections as discriminative features. The aim of this study was to investigate ADHD-related spatially distributed discriminative features derived from whole-brain resting-state functional connectivity patterns using machine learning. We measured the interregional functional connections of the R-fMRI data from 40 ADHD patients and 28 matched typically developing controls. Machine learning was used to discriminate ADHD patients from controls. Classification performance was assessed by permutation tests. The results from the model with the highest classification accuracy showed that 85.3% of participants were correctly identified using leave-one-out cross-validation (LOOV) with support vector machine (SVM). The majority of the most discriminative functional connections were located within or between the cerebellum, default mode network (DMN) and frontoparietal regions. Approximately half of the most discriminative connections were associated with the cerebellum. The cerebellum, right superior orbitofrontal cortex, left olfactory cortex, left gyrus rectus, right superior temporal pole, right calcarine gyrus and bilateral inferior occipital cortex showed the highest discriminative power in classification. Regarding the brain-behaviour relationships, some functional connections between the cerebellum and DMN regions were significantly correlated with behavioural symptoms in ADHD ( < 0.05). This study indicated that whole-brain resting-state functional connections might provide potential neuroimaging-based information for clinically assisting the diagnosis of ADHD.

Concerns have arisen regarding the heterogeneity of patients with major depressive disorder (MDD), particularly when the varying disease progression trajectories among individuals are overlooked. Recognizing these distinct trajectories is crucial for personalized assessments and accurate disease progression predictions in MDD, posing a significant challenge in clinical practice. We utilized a data‐driven subtype and stage inference (SuStaIn) model to infer trajectories based on cross‐sectional amplitude of low‐frequency fluctuations (ALFF) derived from resting‐state functional magnetic resonance imaging data of 833 patients with MDD and 834 healthy controls. Based on distinct trajectories, two subtypes of MDD were identified: Subtype 1 showed declining ALFF from paracentral lobule (PCL) to thalamus to medial orbitofrontal cortex (OFCmed), with higher core depression scores and gray matter atrophy, whereas Subtype 2 had an opposing trajectory, with initial OFCmed ALFF decrease gradually extending to PCL. Our findings contribute to a better understanding of MDD heterogeneity and facilitate precise disease progression predictions. Major depressive disorder exhibits profound phenotypic and temporal heterogeneity. This study aims to identify distinct brain change trajectories in MDD using the subtype and stage inference model to deepen our understanding of heterogeneity, as well as to compare the cognitive function change patterns among subtypes with different trajectories.

•Based on GMV deviations, we identified two distinct subtypes of structural changes in MDD: one characterized by GMV reduction and the other by GMV increase.•Our study found that the GMV increase subtype does not exhibit signs of accelerated brain aging.•We discovered that different structural subtypes of MDD share common epicenters within the DMN, while also possessing their own specific epicenters.•We revealed that the heterogeneity of structural changes in MDD involves distinct microscopic molecular characteristics. Major depressive disorder (MDD), a prevalent mental health condition, manifests intricate alterations in brain structure that evolve gradually over time and across various brain regions. Despite significant research efforts, two fundamental questions remain unsettled: the precise brain origins of MDD and whether MDD contributes to accelerates brain aging. To this end, we conducted a comprehensive investigation leveraging data from 830 MDD patients and 853 matched healthy controls (HC). Normative models, established on HC gray matter volume (GMV) data, were utilized to quantify individual deviations in GMV among MDD patients. Employing k-means clustering to these deviation profiles, we successfully discerned two clinically distinct subtypes. Subtype 1 is characterized by GMV atrophy, coupled with indications of accelerated brain aging processes. In contrast, subtype 2 exhibits increased GMV without significant acceleration of aging phenomena. Intriguingly, both subtypes converge on the default mode network as a common disease epicenter, highlighting a shared neurophysiological underpinning. However, subtype-specific epicenters diverge, with subtype 1 featuring unique foci primarily in the hippocampus and amygdala, whereas subtype 2 distinguishes itself with epicenters primarily located in the accumbens. This nuanced examination of subtype-specific brain alterations, incorporating their intricate spatiotemporal dynamics, provides profound insights into the heterogeneity and complexity inherent in MDD. [Display omitted]

Major depressive disorder (MDD) is a highly heterogeneous psychiatric disorder characterized with considerable individual variability in clinical manifestations which may correspond to brain alterations including the default mode network (DMN). This study analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 796 MDD patients and 823 healthy controls (HC) to investigate individual variability in functional connectivity (IVFC) between the DMN and 108 non-DMN regions. We aimed to identify MDD-related IVFC abnormalities and their clinical relevance, alongside exploring gene expression correlations. The results revealed similar spatial patterns of IVFC within the DMN in both groups, yet significantly increased IVFC values in MDD patients were observed in regions such as the ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, fusiform gyrus, and occipital cortex. Notably, the mean IVFC in the DMN and fusiform gyrus was positively correlated with Hamilton Rating Scale for Depression (HAMD) scores in MDD patients. Gene expression analyses explained 47.0% of the variance in MDD-related IVFC alterations, with the most associated genes enriched in processes including membrane potential regulation, head development, synaptic transmission, and dopaminergic synapse. These findings highlight the clinical importance of IVFC variability in the DMN and suggest its potential role as a biomarker in MDD.

Entropy analysis of resting-state functional magnetic resonance imaging (R-fMRI) has recently been adopted to characterize brain temporal dynamics in some neuropsychological or psychiatric diseases. Thalamus-related dysfunction might be a potential trait marker of major depressive disorder (MDD), but the abnormal changes in the thalamus based on R-fMRI are still unclear from the perspective of brain temporal dynamics. The aim of this study was to identify local entropy changes and subregional connectivity patterns of the thalamus in MDD patients. We measured the sample entropy of the R-fMRI data from 46 MDD patients and 32 matched healthy controls. We employed the Louvain method for the module detection algorithm to automatically identify a functional parcellation of the thalamus and then examined the whole-brain subregional connectivity patterns. The results indicated that the MDD patients had decreased entropy in the bilateral thalami compared with healthy controls. Increased functional connectivity between the thalamic subregions and the medial part of the superior frontal gyrus (mSFG) was found in MDD patients. This study showed new evidence about sample entropy changes in MDD patients. The functional connectivity alterations that were widely distributed across almost all the thalamic subregions with the mSFG in MDD suggest a general involvement independent of the location and function of the subregions.

Research into cognitive emotion regulation (ER) extends our understanding of human cognition, which is capable of processing objective information and is crucial in maintaining subjective/internal homeostasis. Among various ER strategies, the alleviation of negative emotion via reappraisal is of particular importance for adaptation and psychological well-being. Although still debated, previous neuroimaging studies tend to infer that the reappraisal ER is mediated by the capability of working memory (WM), which has not been examined empirically. This meta-analytical study of published neuroimaging literature used activation likelihood estimation (ALE) to compare the neural circuits that regulate negative emotion (reappraisal tasks; 46 studies/1254 subjects) and execute WM (2-back tasks; 50 studies/1312 subjects), with special emphasis on the prefrontal cortex (PFC). Taking the canonical WM network as a reference, ALE results revealed that the dorsal midline PFC was partly shared by both ER and WM, whereas ER-specific PFC structures were delineated in the inferior, middle, and superior frontal cortices, as well as in the posterior brain regions. The peak coordinates of ER in the middle frontal cortex were dorsal to those of WM by 15.1 mm (left) and 21.6 mm (right). The results support specialized emotion-related neural substrates in the PFC, negating the assumption that reappraisal ER and WM rely on the same neural resources. The holistic picture of \"emotional brain\" may need to incorporate the emotion-related PFC circuit, together with subcortical and limbic emotion centers.

An abnormality of structures and functions in the hippocampus may have a key role in the pathophysiology of major depressive disorder (MDD). However, it is unclear whether structure factors of the hippocampus effectively impact antidepressant responses by hippocampal functional activity in MDD patients. We collected longitudinal data from 36 MDD patients before and after a 3-month course of antidepressant pharmacotherapy. Additionally, we obtained baseline data from 43 healthy controls matched for sex and age. Using resting-state functional magnetic resonance imaging (rs-fMRI), we estimated the dynamic functional connectivity (dFC) of the hippocampal subregions using a sliding-window method. The gray matter volume was calculated using voxel-based morphometry (VBM). The results indicated that patients with MDD exhibited significantly lower dFC of the left rostral hippocampus (rHipp.L) with the right precentral gyrus, left superior temporal gyrus and left postcentral gyrus compared to healthy controls at baseline. In MDD patients, the dFC of the rHipp.L with right precentral gyrus at baseline was correlated with both the rHipp.L volume and HAMD remission rate, and also mediated the effects of the rHipp.L volume on antidepressant performance. Our findings suggested that the interaction between hippocampal structure and functional activity might affect antidepressant performance, which provided a novel insight into the hippocampus-related neurobiological mechanism of MDD.

An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the diagnostic classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of ASD patients were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (P < 0.05). This study highlights the important role of the DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.