Functional and structural networks of lateral and medial orbitofrontal cortex as potential neural pathways for depression in childhood

Neural mechanisms underlying cognitive impairment in depression and cognitive benefits of exercise intervention

Depression is associated with functional brain impairments, although comprehensive studies remain limited. This study reviews neural mechanisms underlying cognitive impairment in depression and identifies associated activation abnormalities in brain regions. The study also explores the underlying neural processes of cognitive benefits of exercise intervention for depression. Executive function impairments, including working memory, inhibitory control and cognitive flexibility are associated with frontal cortex and anterior cingulate areas, especially dorsolateral prefrontal cortex. Depression is associated with certain neural impairments of reward processing, especially orbitofrontal cortex, prefrontal cortex, nucleus accumbens and other striatal regions. Depressed patients exhibit decreased activity in the hippocampus during memory function. Physical exercise has been found to enhance memory function, executive function, and reward processing in depression patients by increasing functional brain regions and the brain-derived neurotrophic factor (BDNF) as a nutritional factor also plays a key role in exercise intervention. The study documents neurophysiological mechanisms behind exercise intervention's improved functions. In summary, the study provides insights into neural mechanisms underlying cognitive impairments in depression and the effectiveness of exercise as a treatment.

Dysfunction of neurovascular coupling in patients with cerebral small vessel disease: A combined resting-state fMRI and arterial spin labeling study

BACKGROUND

Cerebral small vessel disease (CSVD) closely correlates to cognitive impairment, but its pathophysiology and the neurovascular mechanisms of cognitive deficits were unclear. We aimed to explore the dysfunctional patterns of neurovascular coupling (NVC) in patients with CSVD and further investigate the neurovascular mechanisms of CSVD-related cognitive impairment.

METHODS

Forty-three patients with CSVD and twenty-four healthy controls were recruited. We adopted resting-state functional magnetic resonance imaging combined with arterial spin labeling to investigate the NVC dysfunctional patterns in patients with CSVD. The Human Brain Atlas with 246 brain regions was applied to extract the NVC coefficients for each brain region. Partial correlation analysis and mediation analysis were used to explore the relationship between CSVD pathological features, NVC dysfunctional patterns, and cognitive decline.

RESULTS

8 brain regions with NVC dysfunction were found in patients with CSVD (p < 0.025, Bonferroni correction). The NVC dysfunctional patterns in regions of the default mode network and subcortical nuclei were negatively associated with lacunes, white matter hyperintensities burden, and the severity of CSVD (FDR correction, q < 0.05). The NVC decoupling in regions located in the default mode network positively correlated with delayed recall deficits (FDR correction, q < 0.05). Mediation analysis suggested that the decreased NVC pattern of the left superior frontal gyrus partially mediated the impact of white matter hyperintensities on delayed recall (Mediation effect: -0.119; 95%CI: -11.604,-0.458; p < 0.05).

CONCLUSION

The findings of this study reveal the NVC dysfunctional pattern in patients with CSVD and illustrate the neurovascular mechanism of CSVD-related cognitive impairment. The NVC function in the left superior frontal gyrus may serve as a promising biomarker and therapeutic target for memory deficits in patients with CSVD.

Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population

Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.

The road not taken: Common and distinct neural correlates of regret and relief

Humans anticipate and evaluate both obtained and counterfactual outcomes - outcomes that could have been had an alternate decision been taken - and experience associated emotions of regret and relief. Although many functional magnetic resonance imaging (fMRI) studies have examined the neural correlates of these emotions, there is substantial heterogeneity in their results. We conducted coordinate-based ALE and network-based ANM meta-analysis of fMRI studies of experienced regret and relief to examine commonalities and differences in their neural correlates. Regionally, we observed that the experience of both regret and relief was associated with greater activation in the right ventral striatum (VS), which is implicated in tracking reward prediction error. At the network level, regret and relief shared the reward-sensitive mesocorticolimbic network with preferential activation of the medial orbitofrontal cortex (mOFC) for regret processing and medial cingulate cortex (MCC) for relief processing. Our research identified shared and separable brain systems subserving regret and relief experience, which may inform the treatment of regret-related mood disorders.

Brain-based Sex Differences in Depression: A Systematic Review of Neuroimaging Studies

Major depressive disorder (MDD) is a common psychiatric illness with a wide range of symptoms such as mood decline, loss of interest, and feelings of guilt and worthlessness. Women develop depression more often than men, and the diagnostic criteria for depression mainly rely on female patients' symptoms. By contrast, male depression usually manifests as anger attacks, aggression, substance use, and risk-taking behaviors. Various studies have focused on the neuroimaging findings in psychiatric disorders for a better understanding of their underlying mechanisms. With this review, we aimed to summarize the existing literature on the neuroimaging findings in depression, separated by male and female subjects. A search was conducted on PubMed and Scopus for magnetic resonance imaging (MRI), functional MRI (fMRI), and diffusion tensor imaging (DTI) studies of depression. After screening the search results, 15 MRI, 12 fMRI, and 4 DTI studies were included. Sex differences were mainly reflected in the following regions: 1) total brain, hippocampus, amygdala, habenula, anterior cingulate cortex, and corpus callosum volumes, 2) frontal and temporal gyri functions, along with functions of the caudate nucleus and prefrontal cortex, and 3) frontal fasciculi and frontal projections of corpus callosum microstructural alterations. Our review faces limitations such as small sample sizes and heterogeneity in populations and modalities. But in conclusion, it reflects the possible roles of sex-based hormonal and social factors in the depression pathophysiology.

Anatomical and Functional Connectivity of Critical Deep Brain Structures and Their Potential Clinical Application in Brain Stimulation

Subcortical structures, such as the hippocampus, amygdala, and nucleus accumbens (NAcc), play crucial roles in human cognitive, memory, and emotional processing, chronic pain pathophysiology, and are implicated in various psychiatric and neurological diseases. Interventions modulating the activities of these deep brain structures hold promise for improving clinical outcomes. Recently, non-invasive brain stimulation (NIBS) has been applied to modulate brain activity and has demonstrated its potential for treating psychiatric and neurological disorders. However, modulating the above deep brain structures using NIBS may be challenging due to the nature of these stimulations. This study attempts to identify brain surface regions as source targets for NIBS to reach these deep brain structures by integrating functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). We used resting-state functional connectivity (rsFC) and probabilistic tractography (PTG) analysis to identify brain surface stimulation targets that are functionally and structurally connected to the hippocampus, amygdala, and NAcc in 119 healthy participants. Our results showed that the medial prefrontal cortex (mPFC) is functionally and anatomically connected to all three subcortical regions, while the precuneus is connected to the hippocampus and amygdala. The mPFC and precuneus, two key hubs of the default mode network (DMN), as well as other cortical areas distributed at the prefrontal cortex and the parietal, temporal, and occipital lobes, were identified as potential locations for NIBS to modulate the function of these deep structures. The findings may provide new insights into the NIBS target selections for treating psychiatric and neurological disorders and chronic pain.

Functional connectivity analysis of childhood depressive symptoms

BACKGROUND

Childhood depression is a highly distinct and prevalent condition with an unknown neurobiological basis. We wish to explore the resting state fMRI data in children for potential associations between neural connectivity and childhood depressive symptoms.

METHODS

A longitudinal birth cohort study with neuroimaging data obtained at 4.5, 6.0 and 7.5 years of age and the Children Depression Inventory 2 (CDI) administered between 8.5 and 10.5 years was used. The CDI score was used as the dependent variable and tested for correlation, both simple Pearson and network based statistic, with the functional connectivity values obtained from the resting state fMRI. Cross-validated permutation testing with a general linear model was used to validate that the identified functional connections were indeed implicated in childhood depression.

RESULTS

Ten functional connections and four brain regions (Somatomotor Area B, Temporoparietal Junction, Orbitofrontal Cortex and Insula) were identified as significantly associated with childhood depressive symptoms for girls at 6.0 and 7.5 years. No significant functional connections were found in girls at 4.5 years or for boys at any timepoint. Network based statistic and permutation testing confirmed these findings.

CONCLUSIONS

This study revealed significant sex-dependent associations of neural connectivity and childhood depressive symptoms. The regions identified are implicated in speech/language, social cognition and information integration and suggest unique pathways to childhood depressive symptoms.

Greed personality trait links to negative psychopathology and underlying neural substrates

Greed personality trait (GPT), characterized by the desire to acquire more and the dissatisfaction of never having enough, has been hypothesized to link with negative emotion/affect characteristics and aggressive behaviors. To describe its emotion-related features, we utilized a series of scales to measure corresponding emotion/affect and aggression (n = 411) and collected their neuroimaging data (n = 330) to explore underlying morphological substrates. Correlational analyses revealed that greedy individuals show more negative symptoms (e.g. depression, loss of interest, negative affect), lower psychological well-being and more aggression. Mediation analyses further demonstrated that negative symptoms and psychological well-being mediated greedy individuals' aggression. Moreover, exploratory factor analysis extracted factor scores across three factors (negative psychopathology, happiness, and motivation) from the measures scales. Negative psychopathology and happiness remained robust mediators. Importantly, these findings were replicated in an independent sample (n = 68). Voxel-based morphometry analysis also revealed that gray matter volumes (GMVs) in the prefrontal-parietal-occipital system were associated with negative psychopathology and happiness, and GMVs in the frontal pole and middle frontal cortex mediated the relationships between GPT and aggressions. These findings provide novel insights into the negative characteristics of dispositional greed, and suggest their mediating roles on greedy individuals' aggression and underlying neuroanatomical substrates.

Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network

The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.

Effect of parental depressive symptoms on offspring's brain structure and function: A systematic review of neuroimaging studies

Perinatal Depression (PND) is a severe mental disorder that appears during pregnancy or in the post-partum. Although PND has been associated with behavioral problems in the offspring, its effects on brain development are unclear. With this review we aimed at summarizing the existing literature on the effects of perinatal depressive symptoms on children's brains. A search on PubMed and Embase of structural, functional Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI) studies exploring the effect of PND on offspring's brain was conducted. We selected twenty-six studies, ten structural MRI, five DTI, six fMRI and five with combined techniques. Overall, the studies showed: a) gray matter alterations in amygdala and fronto-temporal lobes; b) microstructural alterations in amygdala, frontal lobe, cingulum, longitudinal fasciculus and fornix; and c) functional alterations between limbic and mesocortical networks. The small sample size and the heterogeneity in populations and methodologies limit this review. In conclusion, PND seems to influence structure and function of offspring, that may contribute to the risk of behavioral disturbances later in life.

Divergent Projection Patterns Revealed by Reconstruction of Individual Neurons in Orbitofrontal Cortex

The orbitofrontal cortex (OFC) is involved in diverse brain functions via its extensive projections to multiple target regions. There is a growing understanding of the overall outputs of the OFC at the population level, but reports of the projection patterns of individual OFC neurons across different cortical layers remain rare. Here, by combining neuronal sparse and bright labeling with a whole-brain florescence imaging system (fMOST), we obtained an uninterrupted three-dimensional whole-brain dataset and achieved the full morphological reconstruction of 25 OFC pyramidal neurons. We compared the whole-brain projection targets of these individual OFC neurons in different cortical layers as well as in the same cortical layer. We found cortical layer-dependent projections characterized by divergent patterns for information delivery. Our study not only provides a structural basis for understanding the principles of laminar organizations in the OFC, but also provides clues for future functional and behavioral studies on OFC pyramidal neurons.

Neuroanatomical and functional substrates of the greed personality trait

Greedy individuals often exhibit more impulsive decision-making and short-sighted behaviors. It has been assumed that altered reward circuitry and prospection network is associated with greed personality trait (GPT). In this study, we first explored the morphological characteristics (i.e., gray matter volume; GMV) of GPT combined with univariate and multivariate pattern analysis (MVPA) approaches. Second, we adopted a revised version of inter-temporal choice task and independently manipulated the amount and delay time of future rewards. Using brain-imaging design, reward- and prospection-related brain activations were assessed and their associations with GPT were further examined. The MVPA results showed that GPT was associated with the GMVs in the right lateral frontal pole cortex, left ventromedial prefrontal cortex, right lateral occipital cortex, and right occipital pole. Additionally, we observed that the amount-relevant brain activations (responding to reward circuitry) in the lateral orbitofrontal cortex were negatively associated with individual's variability in GPT scores, whereas the delay time-relevant brain activations (responding to prospection network system) in the dorsolateral prefrontal cortex, dorsomedial prefrontal cortex, superior parietal lobule, and anterior cingulate cortex were positively associated with individual's variability in GPT scores. These findings not only provide novel insights into the neuroanatomical substrates underlying the human dispositional greed, but also suggest the critical roles of reward and prospection processing on the greed.

Abnormal Degree Centrality in White Matter Hyperintensities: A Resting-State Functional Magnetic Resonance Imaging Study

Background: White matter hyperintensities (WMHs) are a common occurrence with aging and are associated with cognitive impairment. However, the neurobiological mechanisms of WMHs remain poorly understood. Functional magnetic resonance imaging (fMRI) is a prominent tool that helps in non-invasive examinations and is increasingly used to diagnose neuropsychiatric diseases. Degree centrality (DC) is a common and reliable index in fMRI, which counts the number of direct connections for a given voxel in a network and reflects the functional connectivity within brain networks. We explored the underlying mechanism of cognitive impairment in WMHs from the perspective of DC. Methods: A total of 104 patients with WMHs and 37 matched healthy controls (HCs) were enrolled in the current study. All participants underwent individual and overall cognitive function tests and resting-state fMRI (rs-fMRI). WMHs were divided into three groups (39 mild WMHs, 37 moderate WMHs, and 28 severe WMHs) according to their Fazekas scores, and the abnormal DC values in the WMHs and HCs groups were analyzed. Results: There was a significant difference in the right inferior frontal orbital gyrus and left superior parietal gyrus between the WMHs and HCs groups. The functional connectivity between the right inferior frontal orbital gyrus and left inferior temporal gyrus, left superior parietal gyrus, and left parietal inferior gyrus was also different in the WMHs group. Conclusion: The change in DC value may be one of the underlying mechanisms of cognitive impairment in individuals with WMHs, which provides us with a new approach to delaying cognitive impairment in WMHs.

Inflammatory modulation of the associations between prenatal maternal depression and neonatal brain

Inflammatory signaling has a role in sensing intrauterine environment, which may be moderators in altering fetal brain development upon maternal environment. This study integrated cytokine transcriptome of post-mortem fetal brains, neonatal brain imaging and genetic variants (n = 161) to examine whether cytokines are candidates for modulating the relationship between prenatal maternal depression and fetal brain development. This study obtained the transcriptome data of 208 cytokine genes in 12 fetal brain regions from the BrainSpan database. We also included 161 mother-child dyads with prenatal maternal depressive symptoms assessed at 26 weeks of gestation, cytokine genotype data extracted from umbilical cord specimens, and neonatal brain images from a longitudinal prospective birth cohort. We revealed that 22 cytokine genes are expressed in specific brain regions in utero, whose variants have roles in modulating the effects of the prenatal environment on the accelerated fetal development of the hippocampus, auditory, parietal, orbitofrontal, and dorsal prefrontal cortex. Neonates high in the genetic expression score (GES) of TNFRSF19 and IL17RB showed a larger right hippocampal volume, high in the GES of BMPR1B showed the thicker thickness of the sensorimotor cortex, and high in the GES of IL1RAP and CXCR4 demonstrated the thicker thickness of the dorsal and orbital prefrontal cortex in relation with greater prenatal maternal depressive symptoms. Our findings suggest that in humans, the cytokine genes are expressed in a brain region-specific manner in utero and may have potential roles in modulating the fetal development of the corresponding brain regions in response to the maternal environment.

Abnormal Alterations of Regional Spontaneous Neuronal Activity in Inferior Frontal Orbital Gyrus and Corresponding Brain Circuit Alterations: A Resting-State fMRI Study in Somatic Depression

Background: Major depressive disorders often involve somatic symptoms and have been found to have fundamental differences from non-somatic depression (NSD). However, the neural basis of this type of somatic depression (SD) is unclear. The aim of this study is to use the amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) analyses to examine the abnormal, regional, spontaneous, neuronal activity and the corresponding brain circuits in SD patients. Methods: 35 SD patients, 25 NSD patients, and 27 matched healthy controls were selected to complete this study. The ALFF and seed-based FC analyses were employed, and the Pearson correlation was determined to observe possible clinical relevance. Results: Compared with NSD, the SD group showed a significant ALFF increase in the right inferior temporal gyrus; a significant ALFF decrease in left hippocampus, right inferior frontal orbital gyrus and left thalamus; and a significant decrease in the FC value between the right inferior frontal orbital gyrus and the left inferior parietal cortex (p < 0.05, corrected). Within the SD group, the mean ALFF value of the right inferior frontal orbital gyrus was associated with the anxiety factor scores (r = -0.431, p = 0.010, corrected). Conclusions: Our findings suggest that abnormal differences in the regional spontaneous neuronal activity of the right inferior frontal orbital gyrus were associated with dysfunction patterns of the corresponding brain circuits during rest in SD patients, including the limbic-cortical systems and the default mode network. This may be an important aspect of the underlying mechanisms for pathogenesis of SD at the neural level.