Lisanne M. Jenkins

Increased Coupling of Intrinsic Networks in Remitted Depressed Youth Predicts Rumination and Cognitive Control

Objective Functional connectivity MRI (fcMRI) studies of individuals currently diagnosed with major depressive disorder (MDD) document hyperconnectivities within the default mode network (DMN) and between the DMN and salience networks (SN) with regions of the cognitive control network (CCN). Studies of individuals in the remitted state are needed to address whether effects derive from trait, and not state or chronic burden features of MDD. Method fcMRI data from two 3.0 Tesla GE scanners were collected from 30 unmedicated (47% medication naïve) youth (aged 18–23, modal depressive episodes = 1, mean age of onset = 16.2, SD = 2.6) with remitted MDD (rMDD; modal years well = 4) and compared with data from 23 healthy controls (HCs) using four bilateral seeds in the DMN and SN (posterior cingulate cortex (PCC), subgenual anterior cingulate (sgACC), and amygdala), followed by voxel-based comparisons of the whole brain. Results Compared to HCs, rMDD youth exhibited hyperconnectivities from both PCC and sgACC seeds with lateral, parietal, and frontal regions of the CCN, extending to the dorsal medial wall. A factor analysis reduced extracted data and a PCC factor was inversely correlated with rumination among rMDD youth. Two factors from the sgACC hyperconnectivity clusters were related to performance in cognitive control on a Go/NoGo task, one positively and one inversely. Conclusions Findings document hyperconnectivities of the DMN and SN with the CCN (BA 8/10), which were related to rumination and sustained attention. Given these cognitive markers are known predictors of response and relapse, hyperconnectivities may increase relapse risk or represent compensatory mechanisms.

Shared white matter alterations across emotional disorders: A voxel-based meta-analysis of fractional anisotropy

Background White matter (WM) integrity may represent a shared biomarker for emotional disorders (ED). Aims: To identify transdiagnostic biomarkers of reduced WM by meta-analysis of findings across multiple EDs. Method Web of Science was searched systematically for studies of whole brain analysis of fractional anisotropy (FA) in adults with major depressive disorder, bipolar disorder, social anxiety disorder, obsessive-compulsive disorder or posttraumatic stress disorder compared with a healthy control (HC) group. Peak MNI coordinates were extracted from 37 studies of voxel-based analysis (892 HC and 962 with ED) and meta-analyzed using seed-based d Mapping (SDM) Version 4.31. Separate meta-analyses were also conducted for each disorder. Results In the transdiagnostic meta-analysis, reduced FA was identified in ED studies compared to HCs in the left inferior fronto-occipital fasciculus, forceps minor, uncinate fasciculus, anterior thalamic radiation, superior corona radiata, bilateral superior longitudinal fasciculi, and cerebellum. Disorder-specific meta-analyses revealed the OCD group had the most similarities in reduced FA to other EDs, with every cluster of reduced FA overlapping with at least one other diagnosis. The PTSD group was the most distinct, with no clusters of reduced FA overlapping with any other diagnosis. The BD group were the only disorder to show increased FA in any region, and showed a more bilateral pattern of WM changes, compared to the other groups which tended to demonstrate a left lateralized pattern of FA reductions. Conclusions Distinct diagnostic categories of ED show commonalities in WM tracts with reduced FA when compared to HC, which links brain networks involved in cognitive and affective processing. This meta-analysis facilitates an increased understanding of the biological markers that are shared by these ED.

Decoupling of the amygdala to other salience network regions in adolescent-onset recurrent major depressive disorder

BACKGROUND Recent meta-analyses of resting-state networks in major depressive disorder (MDD) implicate network disruptions underlying cognitive and affective features of illness. Heterogeneity of findings to date may stem from the relative lack of data parsing clinical features of MDD such as phase of illness and the burden of multiple episodes. METHOD Resting-state functional magnetic resonance imaging data were collected from 17 active MDD and 34 remitted MDD patients, and 26 healthy controls (HCs) across two sites. Participants were medication-free and further subdivided into those with single v. multiple episodes to examine disease burden. Seed-based connectivity using the posterior cingulate cortex (PCC) seed to probe the default mode network as well as the amygdala and subgenual anterior cingulate cortex (sgACC) seeds to probe the salience network (SN) were conducted. RESULTS Young adults with remitted MDD demonstrated hyperconnectivity of the left PCC to the left inferior frontal gyrus and of the left sgACC to the right ventromedial prefrontal cortex (PFC) and left hippocampus compared with HCs. Episode-independent effects were observed between the left PCC and the right dorsolateral PFC, as well as between the left amygdala and right insula and caudate, whereas the burden of multiple episodes was associated with hypoconnectivity of the left PCC to multiple cognitive control regions as well as hypoconnectivity of the amygdala to large portions of the SN. CONCLUSIONS This is the first study of a homogeneous sample of unmedicated young adults with a history of adolescent-onset MDD illustrating brain-based episodic features of illness.

Social cognition in patients following surgery to the prefrontal cortex

Impaired social cognition, including emotion recognition, may explain dysfunctional emotional and social behaviour in patients with lesions to the ventromedial prefrontal cortex (VMPFC). However, the VMPFC is a large, poorly defined area that can be sub-divided into orbital and medial sectors. We sought to investigate social cognition in patients with discrete, surgically circumscribed prefrontal lesions. Twenty-seven patients between 1 and 12 months post-neurosurgery were divided into groups based on Brodmann areas resected, determined by post-surgical magnetic resonance imaging. We hypothesised that patients with lesions to the VMPFC (n=5), anterior cingulate cortex (n=4), orbitofrontal cortex (n=7) and dorsolateral prefrontal cortex (DLPFC, n=11) would perform worse than a control group of 26 extra-cerebral neurosurgery patients on measures of dynamic facial emotion recognition, theory of mind (ToM) and empathy. Results indicated the VMPFC-lesioned group performed significantly worse than the control group on the facial emotion recognition task overall, and for fear specifically, and performed worse on the ToM measure. The DLPFC group also performed worse on the ToM and empathy measures, but DLPFC lesion location was not a predictor of performance in hierarchical multiple regressions that accounted for other variables, including the reduced estimated verbal IQ in this group. It was concluded that isolated orbital or medial prefrontal lesions are not sufficient to produce impairments in social cognition. This is the first study to demonstrate that it is the combination of lesions to both areas that affect social cognition, irrespective of lesion volume. While group sizes were similar to other comparable studies that included patients with discrete, surgically circumscribed lesions to the prefrontal cortex, future large, multi-site studies are needed to collect larger samples and confirm these results.

Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI.

Predicting treatment response for major depressive disorder can provide a tremendous benefit for our overstretched health care system by reducing number of treatments and time to remission, thereby decreasing morbidity. The present study used neural and performance predictors during a cognitive control task to predict treatment response (% change in Hamilton Depression Rating Scale pre- to post-treatment). Forty-nine individuals diagnosed with major depressive disorder were enrolled with intent to treat in the open-label study; 36 completed treatment, had useable data, and were included in most data analyses. Participants included in the data analysis sample received treatment with escitalopram (n = 22) or duloxetine (n = 14) for 10 weeks. Functional MRI and performance during a Parametric Go/No-go test were used to predict per cent reduction in Hamilton Depression Rating Scale scores after treatment. Haemodynamic response function-based contrasts and task-related independent components analysis (subset of sample: n = 29) were predictors. Independent components analysis component beta weights and haemodynamic response function modelling activation during Commission errors in the rostral and dorsal anterior cingulate, mid-cingulate, dorsomedial prefrontal cortex, and lateral orbital frontal cortex predicted treatment response. In addition, more commission errors on the task predicted better treatment response. Together in a regression model, independent component analysis, haemodynamic response function-modelled, and performance measures predicted treatment response with 90% accuracy (compared to 74% accuracy with clinical features alone), with 84% accuracy in 5-fold, leave-one-out cross-validation. Convergence between performance markers and functional magnetic resonance imaging, including novel independent component analysis techniques, achieved high accuracy in prediction of treatment response for major depressive disorder. The strong link to a task paradigm provided by use of independent component analysis is a potential breakthrough that can inform ways in which prediction models can be integrated for use in clinical and experimental medicine studies.

Attenuated intrinsic connectivity within cognitive control network among individuals with remitted depression: Temporal stability and association with negative cognitive styles.

Many individuals with major depressive disorder (MDD) experience cognitive dysfunction including impaired cognitive control and negative cognitive styles. Functional connectivity magnetic resonance imaging studies of individuals with current MDD have documented altered resting‐state connectivity within the default‐mode network and across networks. However, no studies to date have evaluated the extent to which impaired connectivity within the cognitive control network (CCN) may be present in remitted MDD (rMDD), nor have studies examined the temporal stability of such attenuation over time. This represents a major gap in understanding stable, trait‐like depression risk phenotypes. In this study, resting‐state functional connectivity data were collected from 52 unmedicated young adults with rMDD and 47 demographically matched healthy controls, using three bilateral seeds in the CCN (dorsolateral prefrontal cortex, inferior parietal lobule, and dorsal anterior cingulate cortex). Mean connectivity within the entire CCN was attenuated among individuals with rMDD, was stable and reliable over time, and was most pronounced with the right dorsolateral prefrontal cortex and right inferior parietal lobule, results that were corroborated by supplemental independent component analysis. Attenuated connectivity in rMDD appeared to be specific to the CCN as opposed to representing attenuated within‐network coherence in other networks (e.g., default‐mode, salience). In addition, attenuated connectivity within the CCN mediated relationships between rMDD status and cognitive risk factors for depression, including ruminative brooding, pessimistic attributional style, and negative automatic thoughts. Given that these cognitive markers are known predictors of relapse, these results suggest that attenuated connectivity within the CCN could represent a biomarker for trait phenotypes of depression risk. Hum Brain Mapp 38:2939–2954, 2017.

Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity

Understanding abnormal resting-state functional connectivity of distributed brain networks may aid in probing and targeting mechanisms involved in major depressive disorder (MDD). To date, few studies have used resting state functional magnetic resonance imaging (rs-fMRI) to attempt to discriminate individuals with MDD from individuals without MDD, and to our knowledge no investigations have examined a remitted (r) population. In this study, we examined the efficiency of support vector machine (SVM) classifier to successfully discriminate rMDD individuals from healthy controls (HCs) in a narrow early-adult age range. We empirically evaluated four feature selection methods including multivariate Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net feature selection algorithms. Our results showed that SVM classification with Elastic Net feature selection achieved the highest classification accuracy of 76.1% (sensitivity of 81.5% and specificity of 68.9%) by leave-one-out cross-validation across subjects from a dataset consisting of 38 rMDD individuals and 29 healthy controls. The highest discriminating functional connections were between the left amygdala, left posterior cingulate cortex, bilateral dorso-lateral prefrontal cortex, and right ventral striatum. These appear to be key nodes in the etiopathophysiology of MDD, within and between default mode, salience and cognitive control networks. This technique demonstrates early promise for using rs-fMRI connectivity as a putative neurobiological marker capable of distinguishing between individuals with and without rMDD. These methods may be extended to periods of risk prior to illness onset, thereby allowing for earlier diagnosis, prevention, and intervention.

The significance of negative correlations in brain connectivity

Understanding the modularity of functional magnetic resonance imaging (fMRI)–derived brain networks or “connectomes” can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization‐based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood–oxygen–level–dependent (BOLD) signal correlation between two nodes is negative. We validated this novel probability‐based modularity approach on two independent publicly‐available resting‐state connectome data sets (the Human Connectome Project [HCP] and the 1,000 functional connectomes) and demonstrated that negative correlations alone are sufficient in understanding resting‐state modularity. In fact, this approach (a) permits a dual formulation, leading to equivalent solutions regardless of whether one considers positive or negative edges; (b) is theoretically linked to the Ising model defined on the connectome, thus yielding modularity result that maximizes data likelihood. Additionally, we were able to detect novel and consistent sex differences in modularity in both data sets. As data sets like HCP become widely available for analysis by the neuroscience community at large, alternative and perhaps more advantageous computational tools to understand the neurobiological information of negative edges in fMRI connectomes are increasingly important.

Differential Resting State Connectivity Patterns and Impaired Semantically Cued List Learning Test Performance in Early Course Remitted Major Depressive Disorder.

OBJECTIVES There is a well-known association between memory impairment and major depressive disorder (MDD). Additionally, recent studies are also showing resting-state functional magnetic resonance imaging (rsMRI) abnormalities in active and remitted MDD. However, no studies to date have examined both rs connectivity and memory performance in early course remitted MDD, nor the relationship between connectivity and semantically cued episodic memory. METHODS The rsMRI data from two 3.0 Tesla GE scanners were collected from 34 unmedicated young adults with remitted MDD (rMDD) and 23 healthy controls (HCs) between 18 and 23 years of age using bilateral seeds in the hippocampus. Participants also completed a semantically cued list-learning test, and their performance was correlated with hippocampal seed-based rsMRI. Regression models were also used to predict connectivity patterns from memory performance. RESULTS After correcting for sex, rMDD subjects performed worse than HCs on the total number of words recalled and recognized. rMDD demonstrated significant in-network hypoactivation between the hippocampus and multiple fronto-temporal regions, and multiple extra-network hyperconnectivities between the hippocampus and fronto-parietal regions when compared to HCs. Memory performance negatively predicted connectivity in HCs and positively predicted connectivity in rMDD. Conclusions Even when individuals with a history of MDD are no longer displaying active depressive symptoms, they continue to demonstrate worse memory performance, disruptions in hippocampal connectivity, and a differential relationship between episodic memory and hippocampal connectivity.

Comorbid anxiety increases cognitive control activation in Major Depressive Disorder

Major Depressive Disorder (MDD) and anxiety disorders often co‐occur, with poorer treatment response and long‐term outcomes. However, little is known about the shared and distinct neural mechanisms of comorbid MDD and anxiety (MDD+Anx). This study examined how MDD and MDD+Anx differentially impact cognitive control.