Adverse childhood experiences differently affect Theory of Mind brain networks in schizophrenia and healthy controls

Patients with schizophrenia (SZ) show impairments in both affective and cognitive dimensions of theory of mind (ToM). SZ are also particularly vulnerable to detrimental effect of adverse childhood experiences (ACE), influencing the overall course of the disorder and fostering poor social functioning. ACE associate with long-lasting detrimental effects on brain structure, function, and connectivity in regions involved in ToM. Here, we investigated whether ToM networks are differentially affected by ACEs in healthy controls (HC) and SZ, and if these effects can predict the disorder clinical outcome. 26 HC and 33 SZ performed a ToM task during an fMRI session. Whole-brain functional response and connectivity (FC) were extracted, investigating the interaction between ACEs and diagnosis. FC values significantly affected by ACEs were entered in a cross-validated LASSO regression predicting Positive and Negative Syndrome Scale (PANSS), Interpersonal Reactivity Index (IRI), and task performance. ACEs and diagnosis showed a widespread interaction at both affective and cognitive tasks, including connectivity between vmPFC, ACC, precentral and postcentral gyri, insula, PCC, precuneus, parahippocampal gyrus, temporal pole, thalamus, and cerebellum, and functional response in the ACC, thalamus, parahippocampal gyrus and putamen. FC predicted the PANSS score, the fantasy dimension of IRI, and the AToM response latency. Our results highlight the crucial role of early stress in differentially shaping ToM related brain networks in HC and SZ. These effects can also partially explain the clinical and behavioral outcomes of the disorder, extending our knowledge of the effects of ACEs.

Choroid plexus volume is increased in mood disorders and associates with circulating inflammatory cytokines

Depressed patients exhibit altered levels of immune-inflammatory markers both in the peripheral blood and in the cerebrospinal fluid (CSF) and inflammatory processes have been widely implicated in the pathophysiology of mood disorders. The Choroid Plexus (ChP), located at the base of each of the four brain ventricles, regulates the exchange of substances between the blood and CSF and several evidence supported a key role for ChP as a neuro-immunological interface between the brain and circulating immune cells. Given the role of ChP as a regulatory gate between periphery, CSF spaces and the brain, we compared ChP volumes in patients with bipolar disorder (BP) or major depressive disorder (MDD) and healthy controls, exploring their association with history of illness and levels of circulating cytokines. Plasma levels of inflammatory markers and MRI scans were acquired for 73 MDD, 79 BD and 72 age- and sex-matched healthy controls (HC). Patients with either BD or MDD had higher ChP volumes than HC. With increasing age, the bilateral ChP volume was larger in patients, an effect driven by the duration of illness; while only minor effects were observed in HC. Right ChP volumes were proportional to higher levels of circulating cytokines in the clinical groups, including IFN-γ, IL-13 and IL-17. Specific effects in the two diagnostic groups were observed when considering the left ChP, with positive association with IL-1ra, IL-13, IL-17, and CCL3 in BD, and negative associations with IL-2, IL-4, IL-1ra, and IFN-γ in MDD. These results suggest that ChP could represent a reliable and easy-to-assess biomarker to evaluate the brain effects of inflammatory status in mood disorders, contributing to personalized diagnosis and tailored treatment strategies.

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Reduced corticolimbic habituation to negative stimuli characterizes bipolar depressed suicide attempters

Suicide attempts in Bipolar Disorder are characterized by high levels of lethality and impulsivity. Reduced rates of amygdala and cortico-limbic habituation can identify a fMRI phenotype of suicidality in the disorder related to internal over-arousing states. Hence, we investigated if reduced amygdala and whole-brain habituation may differentiate bipolar suicide attempters (SA, n = 17) from non-suicide attempters (nSA, n = 57), and healthy controls (HC, n = 32). Habituation was assessed during a fMRI task including facial expressions of anger and fear and a control condition. Associations with suicidality and current depressive symptomatology were assessed, including machine learning procedure to estimate the potentiality of habituation as biomarker for suicidality. SA showed lower habituation compared to HC and nSA in several cortico-limbic areas, including amygdalae, cingulate and parietal cortex, insula, hippocampus, para-hippocampus, cerebellar vermis, thalamus, and striatum, while nSA displayed intermediate rates between SA and HC. Lower habituation rates in the amygdalae were also associated with higher depressive and suicidal current symptomatology. Machine learning on whole-brain and amygdala habituation differentiated SA vs. nSA with 94% and 69% of accuracy, respectively. Reduced habituation in cortico-limbic system can identify a candidate biomarker for attempting suicide, helping in detecting at-risk bipolar patients, and in developing new therapeutic interventions.

Insulin resistance disrupts white matter microstructure and amplitude of functional spontaneous activity in bipolar disorder

BACKGROUND

Bipolar disorder (BD) is linked to several structural and functional brain alterations. In addition, BD patients have a three-fold increased risk of developing insulin resistance, which is associated with neural changes and poorer BD outcomes. Therefore, we investigated the effects of insulin and two derived measures (insulin resistance and sensitivity) on white matter (WM) microstructure, resting-state (rs) functional connectivity (FC), and fractional amplitude of low-frequency fluctuation (fALFF).

METHODS

BD patients (n = 92) underwent DTI acquisition, and a subsample (n = 22) underwent rs-fMRI. Blood samples were collected to determine insulin and glucose levels. The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI) were computed. DTI data were analyzed via tract-based spatial statistics and threshold-free cluster enhancement. From rs-fMRI data, both ROI-to-ROI FC matrices and fALFF maps were extracted.

RESULTS

Insulin showed a widespread negative association with fractional anisotropy (FA) and a positive effect on radial diffusivity (RD) and mean diffusivity (MD). HOMA-IR exerted a significant effect on RD in the right superior longitudinal fasciculus, whereas QUICKI was positively associated with FA and negatively with RD and MD in the left superior longitudinal fasciculus, left anterior corona radiata, and forceps minor. fALFF was negatively modulated by insulin and HOMA-IR and positively associated with QUICKI in the precuneus. No significant results were found in the ROI-to-ROI analysis.

CONCLUSION

Our findings suggest that WM microstructure and functional alterations might underlie the effect of IR on BD pathophysiology, even if the causal mechanisms need to be further investigated.

Long-term effect of childhood trauma: Role of inflammation and white matter in mood disorders

Bipolar disorder (BD) and major depressive disorder (MDD) are severe psychiatric illnesses that share among their environmental risk factors the exposure to adverse childhood experiences (ACE). Exposure to ACE has been associated with long-term changes in brain structure and the immune response. In the lasts decades, brain abnormalities including alterations of white matter (WM) microstructure and higher levels of peripheral immune/inflammatory markers have been reported in BD and MDD and an association between inflammation and WM microstructure has been shown. However, differences in these measures have been reported by comparing the two diagnostic groups. The aim of the present study was to investigate the interplay between ACE, inflammation, and WM in BD and MDD. We hypothesize that inflammation will mediate the association between ACE and WM and that this will be different in the two groups. A sample of 200 patients (100 BD, 100 MDD) underwent 3T MRI scan and ACE assessment through Childhood Trauma Questionnaire. A subgroup of 130 patients (75 MDD and 55 BD) underwent blood sampling for the assessment of immune/inflammatory markers. We observed that ACE associated with higher peripheral levels of IL-2, IL-17, bFGF, IFN-γ, TNF-α, CCL3, CCL4, CCL5, and PDGF-BB only in the BD group. Further, higher levels of CCL3 and IL-2 associated with lower FA in BD. ACE were found to differently affect WM microstructure in the two diagnostic groups and to be negatively associated with FA and AD in BD patients. Mediation analyses showed a significant indirect effect of ACE on WM microstructure mediated by IL-2. Our findings suggest that inflammation may mediate the detrimental effect of early experiences on brain structure and different mechanisms underlying brain alterations in BD and MDD.

Circulating inflammatory markers impact cognitive functions in bipolar depression

BACKGROUND

Cognitive impairment is a core feature of bipolar disorder, with a prevalence of about 64.4% during episodes and 57.1% in euthymia. Recent evidences suggest that cognitive deficits in BD may follow immune dysfunction and elevated levels of inflammatory cytokines have been reported during periods of depression, mania and euthymia, suggesting the presence of a chronic, low-grade inflammatory state. The aim of the study is to investigate if immune/inflammatory markers and especially chemokines associate to cognitive performances.

METHODS

Seventy-six consecutively admitted inpatients with a depressive episode in course of bipolar disorder performed a neuropsychological evaluation with the Brief Assessment of Cognition in Schizophrenia and plasma blood levels of cytokines, chemokines and growth factors were analyzed with Luminex technology.

RESULTS

Higher levels of IL-1β, IL-6, CCL2, CCL4, CCL5, CXCL10, and bFGF are associated with the likelihood of having a poor cognitive performance.

LIMITATIONS

Limitation include the lack of a group of healthy controls and the lack of information regarding previous psychopharmacological treatments, alcohol and tobacco use.

CONCLUSIONS

Our results confirm the importance of chemokines in bipolar disorder and suggest that inflammatory markers suggestive of a low-grade inflammatory state could contribute to the neurocognitive deficits observed in depressed patients.

Investigating predictive factors of dialectical behavior therapy skills training efficacy for alcohol and concurrent substance use disorders: A machine learning study

BACKGROUND

Dialectical Behavior Therapy Skills Training (DBT-ST) as stand-alone treatment has demonstrated promising outcomes for the treatment of alcohol use disorder (AUD) and concurrent substance use disorders (SUDs). However, no studies have so far empirically investigated factors that might predict efficacy of this therapeutic model.

METHODS

275 treatment-seeking individuals with AUD and other SUDs were consecutively admitted to a 3-month DBT-ST program (in- + outpatient; outpatient settings). The machine learning routine applied (i.e. penalized regression combined with a nested cross-validation procedure) was conducted in order to estimate predictive values of a wide panel of clinical variables in a single statistical framework on drop-out and substance-use behaviors, dealing with related multicollinearity, and eliminating redundant variables.

RESULTS

The cross-validated elastic net model significantly predicted the drop-out. The bootstrap analysis revealed that subjects who showed substance-use behaviors during the intervention and who were treated with the mixed setting (i.e., in- and outpatient) program, together with higher ASI alcohol scores were associated with an higher probability of drop-out. On the contrary, older subjects, higher levels of education, together with higher scores of DERS awareness subscale were negatively associated to drop-out. Similarly, lifetime co-diagnoses of anxiety, bipolar, and gambling disorders, together with bulimia nervosa negatively predicted the drop-out. The machine learning model did not identify predictive variables of substance-use behaviors during the treatment.

CONCLUSIONS

The DBT-ST program could be considered a valid therapeutic approach especially when AUD and other SUDs co-occur with other psychiatric conditions and, it is carried out as a full outpatient intervention.

A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

Multivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four well-known dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated deficits based on cross-validated regularized regression. In particular, we investigated the prediction ability over different neuropsychological scores referring to language, verbal memory, and spatial memory domains. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were the two best methods at extracting representative features, followed by Dictionary Learning (DL) and Non-Negative Matrix Factorization (NNMF). Consistent with these results, features extracted by PCA and ICA were found to be the best predictors of the neuropsychological scores across all the considered cognitive domains. For each feature extraction method, we also examined the impact of the regularization method, model complexity (in terms of number of features that entered in the model) and quality of the maps that display predictive edges in the resting state networks. We conclude that PCA-based models, especially when combined with L1 (LASSO) regularization, provide optimal balance between prediction accuracy, model complexity, and interpretability.