Resting-state functional connectivity in women with Major Depressive Disorder

Altered large-scale internetwork functional connectivity in patients with vestibular migraine and migraine without aura

OBJECTIVE

To investigate large-scale internetwork functional connectivity in patients with vestibular migraine (VM) and migraine without aura (MwoA).

METHODS

Resting-state functional magnetic resonance imaging data from 34 VM patients, 34 MwoA patients, and 33 healthy controls (HCs) were collected and the results were analyzed using independent component analysis (ICA). We also analyzed the correlations between clinical data and internetwork functional connectivity.

RESULTS

In contrast to HCs, MwoA patients showed decreased functional connectivity between the left frontoparietal network (lFPN) and right frontoparietal network (rFPN), with increased functional connectivity between the sensorimotor network (SMN) and lateral visual network (lVN). When compared to MwoA patients, VM patients demonstrated decreased functional network connectivity between the dorsal attention network (DAN) and posterior medial visual network (pmVN), between the SMN and pmVN, and between the SMN and lVN. Meanwhile, increased functional network connectivity was found between the lFPN and rFPN; however, there was no significant difference in functional network connectivity between VM patients and HCs. In addition, associations were found between clinical data and internetwork functional connectivity.

CONCLUSION

Functional connectivity between the lFPN and rFPN was reduced in patients with MwoA compared with HCs, which may indicate functional impairment in cognitive control, attention, somatosensory perception, and emotion regulation in patients with MwoA. VM patients showed decreased functional connectivity between the DAN, SMN, pmVN and lVN compared to patients with MwoA, which could account for the multisensory integration abnormalities and be the cause of vestibular symptoms in VM patients. These findings offer fresh perspectives on the pathophysiology of VM and MwoA.

Severity of alcohol use disorder influences sex differences in sleep, mood and brain functional connectivity impairments

Growing evidence suggests greater vulnerability of women than men to the adverse effects of alcohol on mood and sleep. However, the underlying neurobiological mechanisms are still poorly understood.

Here we examined sex difference in resting state functional connectivity in alcohol use disorder using a whole-brain data driven approach and tested for relationships with mood and self-reported sleep. To examine whether sex effects vary by severity of alcohol use disorder, we studied two cohorts: non-treatment seeking n = 141 participants with alcohol use disorder (low severity; 58 females) from the Human Connectome project, and recently detoxified n = 102 treatment seeking participants with alcohol use disorder (high severity; 34 females) at the National Institute on Alcohol Abuse and Alcoholism.

For both cohorts, participants with alcohol use disorder had greater sleep and mood problems than HC, whereas sex by alcohol use effect varied by severity. Non-treatment seeking females with alcohol use disorder showed significant greater impairments in sleep but not mood compared to non-treatment seeking males with alcohol use disorder, whereas treatment-seeking females with alcohol use disorder reported greater negative mood but not sleep than treatment-seeking males with alcohol use disorder. Greater sleep problems in non-treatment seeking females with alcohol use disorder were associated with lower cerebello-parahippocampal functional connectivity, while greater mood problems in treatment-seeking females with alcohol use disorder were associated with lower fronto-occipital functional connectivity during rest.

The current study suggests that changes in resting state functional connectivity may account for sleep and mood impairments in females with alcohol use disorder. The effect of severity on sex differences might reflect neuroadaptive processes with progression of alcohol use disorder and needs to be tested with longitudinal data in the future.

No associations in preregistered study of youth depression and functional connectivity of fronto-parietal and default mode networks

Adolescence is characterized by vulnerability to the onset of major depressive disorder (MDD). The goal of this preregistered study was to assess neural correlates of depression symptoms in young adolescents, both cross-sectionally and longitudinally. The default mode network (DMN) is believed to support internal attention towards self-referential thoughts, while the fronto-parietal network (FPN) is theorized to support cognitive control and regulation of attention. As MDD diagnosis has been associated with heightened connectivity within DMN regions and diminished connectivity within FPN regions relative to healthy controls, our study builds upon group-difference analyses by using dimensional measures of depression severity. Our preregistered hypotheses were that within-DMN functional connectivity would be positively associated with concurrent depression severity, while within-FPN functional connectivity would be negatively associated with concurrent depression severity. Preregistered analyses also examined between DMN-FPN connectivity as an alternative predictor variable, and assessed the longitudinal associations between all three functional connectivity measures and change in depression severity over three subsequent waves. Multiple regression models tested cross-sectional analyses and hierarchical linear models tested longitudinal analyses. One hundred and twenty-four youth completed a resting state functional MRI. Their depression severity was assessed at the time of the scan and at three follow-up sessions. None of the predictor variables were associated with concurrent depression severity, nor with the slope of depression symptom trajectories in longitudinal analyses. These negative results add to extant cross-sectional studies, and may inform future investigations of brain correlates of depression psychopathology in youth.

Novel method for identification of individualized resonant frequencies for treatment of Major Depressive Disorder (MDD) using repetitive Transcranial Magnetic Stimulation (rTMS): A proof-of-concept study

BACKGROUND

Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment for Major Depressive Disorder (MDD), but therapeutic benefit is highly variable. Clinical improvement is related to changes in brain circuits, which have preferred resonant frequencies (RFs) and vary across individuals.

OBJECTIVE

We developed a novel rTMS-electroencephalography (rTMS-EEG) interrogation paradigm to identify RFs using the association of power/connectivity measures with symptom severity and treatment outcome.

METHODS

35 subjects underwent rTMS interrogation at 71 frequencies ranging from 3 to 17 Hz administered to left dorsolateral prefrontal cortex (DLPFC). rTMS-EEG was used to assess resonance in oscillatory power/connectivity changes (phase coherence [PC], envelope correlation [EC], and spectral correlation coefficient [SCC]) after each frequency. Multiple regression was used to detect relationships between 10 Hz resonance and baseline symptoms as well as clinical improvement after 10 sessions of 10 Hz rTMS treatment.

RESULTS

Baseline symptom severity was significantly associated with SCC resonance in left sensorimotor (SM; p < 0.0004), PC resonance in fronto-parietal (p = 0.001), and EC resonance in centro-posterior channels (p = 0.002). Subjects significantly improved with 10 sessions of rTMS treatment. Only decreased SCC SM resonance was significantly associated with clinical improvement (r = 0.35, p = 0.04). Subjects for whom 10 Hz SM SCC was highly ranked as an RF among all stimulation frequencies had better outcomes from 10 Hz treatment.

CONCLUSIONS

Resonance of 10 Hz stimulation measured using SCC correlated with both symptom severity and improvement with 10 Hz rTMS treatment. Research should determine whether this interrogation paradigm can identify individualized rTMS treatment frequencies.

Brain Networks Connectivity in Mild to Moderate Depression: Resting State fMRI Study with Implications to Nonpharmacological Treatment

Network mechanisms of depression development and especially of improvement from nonpharmacological treatment remain understudied. The current study is aimed at examining brain networks functional connectivity in depressed patients and its dynamics in nonpharmacological treatment. Resting state fMRI data of 21 healthy adults and 51 patients with mild or moderate depression were analyzed with spatial independent component analysis; then, correlations between time series of the components were calculated and compared between-group (study 1). Baseline and repeated-measure data of 14 treated (psychotherapy or fMRI neurofeedback) and 15 untreated depressed participants were similarly analyzed and correlated with changes in depression scores (study 2). Aside from diverse findings, studies 1 and 2 both revealed changes in within-default mode network (DMN) and DMN to executive control network (ECN) connections. Connectivity in one pair, initially lower in depression, decreased in no treatment group and was inversely correlated with Montgomery-Asberg depression score change in treatment group. Weak baseline connectivity in this pair also predicted improvement on Montgomery-Asberg scale in both treatment and no treatment groups. Coupling of another pair, initially stronger in depression, increased in therapy though was unrelated to improvement. The results demonstrate possible role of within-DMN and DMN-ECN functional connectivity in depression treatment and suggest that neural mechanisms of nonpharmacological treatment action may be unrelated to normalization of initially disrupted connectivity.

Connectivity patterns of cognitive control network in first episode medication-naive depression and remitted depression

BACKGROUND

Cognitive dysfunctions, such as impaired cognitive control, are frequently observed in patients with major depressive disorder (MDD). Although the cognitive control network (CCN) is widely considered a core feature of major depressive disorder (MDD), the relationship between cognitive dysfunction and symptom dimensions remains unclear. This study investigated differences in resting-state functional connectivity of the cognitive control network (CCN) between first-episode medication-naive MDD patients and remitted MDD.

METHODS

We collected resting-state functional MRI (rs-fMRI) data from 22 first-episode medication-naive major depressive disorder (fMDD) patients, 20 patients previously diagnosed with MDD in the remitted phase of depression (rMDD), and 20 healthy controls (HC). The CCN was derived from fMRI images using independent component analysis (ICA), a data-driven image analysis method.

RESULTS

Changes in functional connectivity (FC) within the CCN was mainly attenuated in the right dorsolateral prefrontal cortex and the left inferior parietal lobule, while strengthened in the right dorsal anterior cingulate cortex and the right insula in both fMDD and rMDD groups. Compared with the fMDD group, the rMDD group had decreased FC in the bilateral insula and the right dorsolateral prefrontal cortex. Further analysis explored that the FC in the bilateral insula, the right dorsal anterior cingulate cortex and the right inferior parietal lobule were correlated positively cognitive disturbance factor scores in both patients groups.

CONCLUSIONS

These findings are in agreement with the previous findings that the cognitive control network are impaired in MDD. Furthermore, our results suggest that the alteration of CCN might underpin the cognitive disturbance and the distinct patterns of the CCN between fMDD and rMDD patients may be an important target for effective cognitive remediation in MDD.

Increased Functional Connectivity Between Medulla and Inferior Parietal Cortex in Medication-Free Major Depressive Disorder

Emerging evidence has documented the abnormalities of primary brain functions in major depressive disorder (MDD). The brainstem has shown to play an important role in regulating basic functions of the human brain, but little is known about its role in MDD, especially the roles of its subregions. To uncover this, the present study adopted resting-state functional magnetic resonance imaging with fine-grained brainstem atlas in 23 medication-free MDD patients and 34 matched healthy controls (HC). The analysis revealed significantly increased functional connectivity of the medulla, one of the brainstem subregions, with the inferior parietal cortex (IPC) in MDD patients. A positive correlation was further identified between the increased medulla-IPC functional connectivity and Hamilton anxiety scores. Functional characterization of the medulla and IPC using a meta-analysis revealed that both regions primarily participated in action execution and inhibition. Our findings suggest that increased medulla-IPC functional connectivity may be related to over-activity or abnormal control of negative emotions in MDD, which provides a new insight for the neurobiology of MDD.

Abnormal resting state activity of left middle occipital gyrus and its functional connectivity in female patients with major depressive disorder

BACKGROUND

Women are more susceptible to major depressive disorder (MDD). A possible explanation is that women have a trait tendency to engage in a ruminative response style. Depending on cognitive model of depression, attention bias, memory bias and self-referential bias were closely related among depressed patients. Previous studies have explored the neural mechanism of the cognitive biases by using amplitude of low frequency fluctuations (ALFF) or functional connectivity (FC), and few combined these two metrics, especially focusing on female patients.

METHODS

We assessed 25 female patients diagnosed with MDD and 13 well matched healthy controls (HCs) using Rs-fMRI. Two metrics ALFF and FC based on abnormal ALFF were explored and made comparisons.

RESULTS

Compared with HCs, female patients with MDD showed that one cluster with significantly decreased ALFF in the left middle occipital gyrus(L-MOG). Furtherly we founded depressed female subjects showed significantly lower FC between the L-MOG seed and left orbitofrontal cortex, and significantly higher FC between the L-MOG seed and left medial prefrontal gyrus and left hippocampus.

CONCLUSIONS

Our results showed L-MOG may act as a connection, which involved in the processing of cognitive biases of MDD by connected with limbic-cortical regions in resting state. These findings may enhance the understanding of the neurobiological mechanism in female patients with MDD.

Global connectivity of the fronto-parietal cognitive control network is related to depression symptoms in the general population

We all vary in our mental health, even among people not meeting diagnostic criteria for mental illness. Understanding this individual variability may reveal factors driving the risk for mental illness, as well as factors driving subclinical problems that still adversely affect quality of life. To better understand the large-scale brain network mechanisms underlying this variability, we examined the relationship between mental health symptoms and resting-state functional connectivity patterns in cognitive control systems. One such system is the fronto-parietal cognitive control network (FPN). Changes in FPN connectivity may impact mental health by disrupting the ability to regulate symptoms in a goal-directed manner. Here we test the hypothesis that FPN dysconnectivity relates to mental health symptoms even among individuals who do not meet formal diagnostic criteria but may exhibit meaningful symptom variation. We found that depression symptoms severity negatively correlated with between-network global connectivity (BGC) of the FPN. This suggests that decreased connectivity between the FPN and the rest of the brain is related to increased depression symptoms in the general population. These findings complement previous clinical studies to support the hypothesis that global FPN connectivity contributes to the regulation of mental health symptoms across both health and disease.

Elevated hippocampal choline level is associated with altered functional connectivity in females with major depressive disorder: A pilot study

Metabolic and functional alterations in hippocampus have been associated with the pathophysiology of major depressive disorder (MDD). However, how the hippocampal biochemical disruptions lead to dysfunction of limbic-cortical circuit remains unclear. The present pilot study combined magnetic resonance spectroscopy (MRS) and resting-state functional magnetic resonance imaging (rs-fMRI) to investigate the hippocampal metabolic alteration and its relationship with the intrinsic functional connectivity (FC) changes in MDD. Both MRS and fMRI data were obtained from twelve women with MDD and twelve age-matched, healthy women. Bilateral hippocampi were chosen as regions of interest, in which metabolite concentrations of total choline (tCho), N-acetylaspartate and creatine were quantified. Bilateral hippocampal FC to the whole brain and its correlations with hippocampal metabolite concentrations were conducted. Females with MDD showed significantly elevated left hippocampal tCho level, and decreased anti-correlations between the left hippocampus and bilateral superior frontal gyrus (SFG), left inferior frontal gyrus, and right superior temporal gyrus. More importantly, the left hippocampal tCho level was associated with FC to the right SFG and right fusiform gyrus in healthy women, whereas it was significantly associated with FC to the right lingual gyrus in women with MDD. Our findings suggested that regional metabolic alterations in the left hippocampus might be related to the network-level dysfunction.

Spontaneous Changes in Functional Connectivity of Independent Components of fMRI Signal in Healthy Volunteers at Rest and in Subjects with Mild Depression

Depression is associated with changes in the pattern of interaction of cerebral networks, which can reflect both existing symptoms and compensatory processes. The study is based on analysis of resting state fMRI data from 15 patients with mild depression and 19 conventionally healthy individuals. From fMRI signal recorded at rest for 4 min, the independent components were reconstructed. The intergroup differences and dynamics of functional connectivity from the first to the second recording were analyzed. Initially, depressive patients demonstrated weaker connectivity between cerebellar declive network (CN) and left central executive network (CEN) and also sensorimotor network (SMN); left CEN and primary visual network (PVN). During the second recording, the patients demonstrated more intensive reciprocal connection of the dorsal domain of default mode network (DMN) and auditory network (AN). In healthy subjects, positive correlations of the dorsal DMN and left CEN, right CEN and CN, and negative correlation of dorsal DMN and visuospatial network weakened from the first to second record. In the depression group, the interaction of AN with PVN, the right CEN with the anterior salience network and with ventral DMN weakened. At the same time, the connectivity between SMN and CN were strengthened. The results can be interpreted as spontaneous normalization of brain activity, but no direct evidence for their relation to the improvement of depression symptoms was found.

A supplementary functional connectivity microstate attached to the default mode network in depression revealed by resting-state magnetoencephalography

Default mode network (DMN) has discernable involvement in the representation of negative, self-referential information in depression. Both increased and decreased resting-state functional connectivity between the anterior and posterior DMN have been observed in depression. These conflicting connectivity differences necessitated further exploration of the resting-state DMN dysfunction in depression. Hence, we investigated the time-varying dynamic interactions within the DMN via functional connectivity microstates in a sub-second level. 25 patients with depression and 25 matched healthy controls were enrolled in the MEG analysis. Spherical K-means algorithms embedded within an iterative optimization frame were applied to sliding windowed correlation matrices, resulting in sub-second alternations of two functional connectivity microstates for groups and highlighting the presence of functional variability. In the power dominant state, depressed patients showed a transient decreased pattern that reflected inter/intra-subnetwork deregulation. A supplementary negatively correlated state simultaneously presented with increased connectivity between the ventromedial prefrontal cortex (vmPFC) and the posterior cingulate cortex (PCC), two core nodes for the anterior and posterior DMN respectively. Additionally, depressed patients stayed longer in the supplementary microstate compared to healthy controls. During the time spent in the supplementary microstate, an attempt to compensate for the aberrant effect of vmPFC on PCC across DMN subnetworks was possibly made to balance the self-related processes disturbed by the dominant pattern. The functional compensation mechanism of the supplementary microstate attached to the dominant disrupted one provided a possible explanation to the existing inconsistent findings between the anterior and posterior DMN in depression.

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity

Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.

Lower cognitive control network connectivity in stroke participants with depressive features

Around one-third of people develop depression following ischaemic stroke, yet the underlying mechanisms are poorly understood. Post-stroke depression has been linked to frontal infarcts, mainly lesions in the left dorsolateral prefrontal cortex (DLPFC). But depression is a network disorder that cannot be fully characterised through lesion-symptom mapping. Researchers of depression in non-stroke populations have successfully tapped into the cognitive control network (CCN) using the bilateral DLPFC as a seed, and found that CCN resting-state connectivity is reduced in even mildly depressed subjects, compared to healthy controls. Hence, we aimed to investigate the association between post-stroke depressive features and the CCN resting-state connectivity in a stroke population. We analysed DLPFC resting-state connectivity in 64 stroke participants, 20 of whom showed depressive features assessed with the Patient Health Questionnaire (PHQ-9) at 3 months after stroke. We directly compared groups showing symptoms of depression with those who did not, and performed a regression with PHQ-9 scores in all participants, controlling for age, gender, lesion volume and stroke severity. Post-stroke depression was associated with lower connectivity between the left DLPFC and the right supramarginal gyrus (SMG) in both group and regression analyses. Neither the seed nor the results overlapped with stroke lesions. These findings confirm an important role of the left DLPFC in post-stroke depression, but now show that large-scale network disruptions following stroke associated with depressive features occur without lesions in the DLPFC.

Identify changes of brain regional homogeneity in early and later adult onset patients with first-episode depression using resting-state fMRI

Objective

Previous work exhibited different brain grey matter volume (GMV) changes between patients with early adult onset depression (EOD, age 18–29) and later adult onset depression (LOD, age 30–44) by using 30-year-old as the cut-off age. To identify whether regional homogeneity (ReHo) changes are also different between EOD and LOD by using same cut-off age, we used resting-state functional magnetic resonance imaging (fMRI) to detect the abnormal ReHo between patients with EOD and LOD in the present study.

Methods

Resting-state fMRI scans of 58 patients with EOD, 62 patients with LOD, 60 young healthy controls (HC), and 52 old HC were obtained. The ReHo approach was used to analyze the images.

Results

The ANOVA analysis revealed that the ReHo values in the frontoparietal, occipital, and cerebellar regions were significantly different among the four groups. Relative to patients with LOD, patients with EOD displayed significantly increased ReHo in the left precuneus, and decreased ReHo in the right fusiform. The ReHo values in the left precuneus and the right fusiform had no significant correlation with the score of the depression rating scale or illness duration in both patient subgroups. Compared to young HC, patients with EOD showed significantly increased ReHo in the right frontoparietal regions and the right calcarine. Furthermore, the increased ReHo in the right frontoparietal regions, right insula and left hippocampus, and decreased ReHo in the left inferior occipital gyrus, right middle occipital gyrus, left calcarine, and left supplementary motor area were observed in patients with LOD when compared to old HC.

Conclusions

The ReHo of brain areas that were related to mood regulation was changed in the first-episode, drug-naive adult patients with MDD. Adult patients with EOD and LOD exhibited different ReHo abnormalities relative to each age-matched comparison group, suggesting that depressed adult patients with different age-onset might have different pathological mechanism.

Late-Life Depression: Modifications of Brain Resting State Activity

Late-life depression (LLD) is a common emotional and mental disability in the elderly population characterized by the presence of depressed mood, the loss of interest or pleasure in daily activities, and other depression symptoms. It has a serious effect on the quality of life of elderly individuals and increases their risk of developing physical and mental diseases. It is an important area of research, given the growing elderly population. Brain functional connectivity modifications represent one of the neurobiological biomarker for LLD even if to date remains poorly understood. In our study, we enrolled 10 elderly patients with depressive symptoms compared to 11 age-matched healthy controls. All participants were evaluated by means of neuropsychological tests and underwent the same functional magnetic resonance imaging (fMRI) protocol to evaluate modifications of brain resting state functional connectivity. Between-group differences were observed for the Geriatric Depression Scale and Hamilton Depression Rating Scale, with higher scores for patients with LLD. Voxel-wise, 1-way analysis of variance revealed between-group differences in left frontoparietal network (lFPN) and sensory motor network (SMN): Increased intrinsic connectivity in the LLD group was observed in the left dorsolateral prefrontal cortex and in the left superior parietal lobule of the lFPN and increased intrinsic connectivity in the LLD group was observed in the bilateral primary somatosensory cortex of the SMN. Our findings support the use of resting state fMRI as a potential biomarker for LLD; even if to confirm the relationship between brain changes and the pathophysiology of LLD, longitudinal neuroimaging studies are required.

Distinguishing medication-free subjects with unipolar disorder from subjects with bipolar disorder: state matters

OBJECTIVES

Recent studies have indicated that pattern recognition techniques of functional magnetic resonance imaging (fMRI) data for individual classification may be valuable for distinguishing between major depressive disorder (MDD) and bipolar disorder (BD). Importantly, medication may have affected previous classification results as subjects with MDD and BD use different classes of medication. Furthermore, almost all studies have investigated only depressed subjects. Therefore, we focused on medication-free subjects. We additionally investigated whether classification would be mood state independent by including depressed and remitted subjects alike.

METHODS

We applied Gaussian process classifiers to investigate the discriminatory power of structural MRI (gray matter volumes of emotion regulation areas) and resting-state fMRI (resting-state networks implicated in mood disorders: default mode network [DMN], salience network [SN], and lateralized frontoparietal networks [FPNs]) in depressed (n=42) and remitted (n=49) medication-free subjects with MDD and BD.

RESULTS

Depressed subjects with MDD and BD could be classified based on the gray matter volumes of emotion regulation areas as well as DMN functional connectivity with 69.1% prediction accuracy. Prediction accuracy using the FPNs and SN did not exceed chance level. It was not possible to discriminate between remitted subjects with MDD and BD.

CONCLUSIONS

For the first time, we showed that medication-free subjects with MDD and BD can be differentiated based on structural MRI as well as resting-state functional connectivity. Importantly, the results indicated that research concerning diagnostic neuroimaging tools distinguishing between MDD and BD should consider mood state as only depressed subjects with MDD and BD could be correctly classified. Future studies, in larger samples are needed to investigate whether the results can be generalized to medication-naïve or first-episode subjects.

Subthreshold depression is associated with impaired resting-state functional connectivity of the cognitive control network

Subthreshold depression (StD) is a prevalent condition associated with social morbidity and increased service utilization, as well as a high risk of developing into a major depressive disorder (MDD). The lack of well-defined diagnostic criteria for StD has limited research on this disorder, with very few brain-imaging studies examining the neurobiology of StD. Yet, identifying the neural pathology of StD has the potential to elucidate risk factors and prognostic markers for major depression and is crucial for developing tailored treatments for patients at mild stages of depression. We investigated resting-state functional connectivity (rs-FC) of the cognitive control network (CCN), known to be dysregulated in MDD, using the bilateral dorsolateral prefrontal cortex (DLPFC) as a seed, focusing on two cohorts of StD subjects (young and middle aged) as well as matched controls. Irrespective of age, we found a significant rs-FC decrease in the CCN of the StD subjects, compared with matched controls, particularly between the DLPFC and the brain regions associated with the representation of self and other mental states (temporo-parietal junction (TPJ) and precuneus), as well as salience detection and orienting (insula). The functional connectivity between the DLPFC and the left TPJ was also associated with depressive symptom scores measured by the Center for Epidemiologic Studies Depression Scale. This finding may shed light on the neural pathology of StD, leading to better understanding of mild stages of depression, its diagnosis and the development of new treatments.

Cognition-related brain networks underpin the symptoms of unipolar depression: Evidence from a systematic review

This systematic review sources the latest neuroimaging evidence for the role of cognition-related brain networks in depression, and relates their abnormal functioning to symptoms of the disorder. Using theoretically informed and rigorous inclusion criteria, we integrate findings from 59 functional neuroimaging studies of adults with unipolar depression using a narrative approach. Results demonstrate that two distinct neurocognitive networks, the autobiographic memory network (AMN) and the cognitive control network (CCN), are central to the symptomatology of depression. Specifically, hyperactivity of the introspective AMN is linked to pathological brooding, self-blame, rumination. Anticorrelated under-engagement of the CCN is associated with indecisiveness, negative automatic thoughts, poor concentration, distorted cognitive processing. Downstream effects of this imbalance include reduced regulation of networks linked to the vegetative and affective symptoms of depression. The configurations of these networks can change between individuals and over time, plausibly accounting for both the variable presentation of depressive disorders and their fluctuating course. Framing depression as a disorder of neurocognitive networks directly links neurobiology to psychiatric practice, aiding researchers and clinicians alike.

Reduced striatal activation in females with major depression during the processing of affective stimuli

The extent to which affective reactivity and associated neural underpinnings are altered by depression remains equivocal. This study assessed striatal activation in fifty-one unmedicated female participants meeting DSM-IV criteria for Major Depressive Disorder (MDD) and 61 age-matched healthy females (HC) aged 17-63 years. Participants completed an affective reactivity functional magnetic resonance imaging task. Data were preprocessed using SPM8, and region-of-interest analyses were completed using MarsBaR to extract caudate, putamen, and nucleus accumbens (NAcc) activation. General linear repeated measure ANOVAs were used to assess group differences and correlational analyses were used to measure the association between activation, depression severity, and anhedonia. Main effects of hemisphere, valence, and group status were observed, with MDD participants demonstrating decreased striatal activation compared with HC. Across groups and valence types, the left hemisphere demonstrated greater activation than the right hemisphere in the putamen and nucleus accumbens, whereas the right hemisphere demonstrated greater activation than the left in the caudate. Additionally, unpleasant stimuli elicited greater activation than pleasant and neutral stimuli in the caudate and putamen, and unpleasant stimuli elicited greater activation than neutral stimuli in the NAcc. There were no significant associations between activation, depression severity, and anhedonia. Overall, depression was characterized by reduced affective reactivity in the striatum, regardless of stimuli valence, supporting the emotion context insensitivity model of depression.