In patients with post-traumatic stress disorder (PTSD), a decrease in the brain reward function was reported in behavioral- and in neuroimaging studies. While pathophysiological mechanisms underlying this response are unclear, there are several lines of evidence suggesting over-recruitment of the brain reward regions by aversive stimuli rendering them unavailable to respond to reward-related content. The purpose of this study was to juxtapose brain responses to functional neuroimaging probes that reliably produce rewarding and aversive experiences in PTSD subjects and in healthy controls. The stimuli used were pleasant, aversive and neutral images selected from the International Affective Picture System (IAPS) along with pain-inducing heat applied to the dorsum of the left hand; all were administered during 3 T functional magnetic resonance imaging. Analyses of IAPS responses for the pleasant images revealed significantly decreased subjective ratings and brain activations in PTSD subjects that included striatum and medial prefrontal-, parietal- and temporal cortices. For the aversive images, decreased activations were observed in the amygdala and in the thalamus. PTSD and healthy subjects provided similar subjective ratings of thermal sensory thresholds and each of the temperatures. When 46 °C (hot) and 42 °C (neutral) temperatures were contrasted, voxelwise between-group comparison revealed greater activations in the striatum, amygdala, hippocampus and medial prefrontal cortex in the PTSD subjects. These latter findings were for the most part mirrored by the 44 vs. 42 °C contrast. Our data suggest different brain alterations patterns in PTSD, namely relatively diminished corticolimbic response to pleasant and aversive psychosocial stimuli in the face of exaggerated response to heat-related pain. The present findings support the hypothesis that brain sensitization to pain in PTSD may interfere with the processing of psychosocial stimuli whether they are of rewarding or aversive valence.

Although the pathophysiology of auditory verbal hallucinations remains uncertain, the inner speech model remains a prominent theory. A systematic review and meta-analyses of both functional and structural neuroimaging studies were performed to investigate the inner speech model. Of the 417 papers retrieved, 26 met the inclusion criteria. Meta-analyses found the left insula to be significantly active during auditory verbal hallucinations and to have a significantly reduced grey matter volume in hallucinators. Dysfunction of the left insula may contribute to the misattribution of inner speech due to its suggested roles in both inner speech production and the salience network. No significant activity was found at Broca's area or Heschl's gyrus during auditory verbal hallucinations. Furthermore, no structural abnormalities were found at these sites or in the arcuate fasciculi. Overall, evidence was found to both support and oppose the inner speech model. Further research should particularly include a systematic review of task-based trait studies with a focus on inner speech production and self-referential processing, and analyses of additional language-related white matter tracts.

Mastocytosis is a rare disease in which chronic symptoms are related to mast cell accumulation and activation. Patients can display depression-anxiety-like symptoms and cognitive impairment. The pathophysiology of these symptoms may be associated with tissular mast cell infiltration, mast cell mediator release or both. The objective of this study is to perform morphological or functional brain analyses in mastocytosis to identify brain changes associated with this mast cell disorder. We performed a prospective and monocentric comparative study to evaluate the link between subjective psycho-cognitive complaints, psychiatric evaluation and objective medical data using magnetic resonance imaging with morphological and perfusion sequences (arterial spin-labeled perfusion) in 39 patients with mastocytosis compared with 33 healthy controls. In the test cohort of 39 mastocytosis patients with psycho-cognitive complaints, we found that 49% of them had morphological brain abnormalities, mainly abnormal punctuated white matter abnormalities (WMA). WMA were equally frequent in cutaneous mastocytosis patients and indolent forms of systemic mastocytosis patients (42% and 41% of patients with WMA, respectively). Patients with WMA showed increased perfusion in the putamen compared with patients without WMA and with healthy controls. Putamen perfusion was also negatively correlated with depression subscores. This study demonstrates, for we believe the first time, a high prevalence of morphological and functional abnormalities in the brains of mastocytosis patients with neuropsychiatric complaints. Further studies are required to determine the mechanism underpinning this association and to ascertain its specificity.

In neuroimaging research, seasonal effects are often neglected or controlled as confounding factors. However, seasonal fluctuations in mood and behavior have been observed in both psychiatric disorders and healthy participants. There are vast opportunities for neuroimaging studies to understand seasonal variations in brain function. In this study, we used two longitudinal single-subject datasets with weekly measures over more than a year to investigate seasonal effects on intrinsic brain networks. We found that the sensorimotor network displayed a strong seasonal pattern. The sensorimotor network is not only relevant for integrating sensory inputs and coordinating movement, but it also affects emotion regulation and executive function. Therefore, the observed seasonality effects in the sensorimotor network could contribute to seasonal variations in mood and behavior. Genetic analyses revealed seasonal modulation of biological processes and pathways relevant to immune function, RNA metabolism, centrosome separation, and mitochondrial translation that have a significant impact on human physiology and pathology. In addition, we revealed critical factors such as head motion, caffeine use, and scan time that could interfere with seasonal effects and need to be considered in future studies.

Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30-50% of patients do not benefit sufficiently. We investigated whether structural and resting-state functional magnetic resonance imaging (MRI/rs-fMRI) data could distinguish between treatment responders and non-responders on the group and individual level. Forty-four male veterans with PTSD underwent baseline scanning followed by trauma-focused psychotherapy. Voxel-wise gray matter volumes were extracted from the structural MRI data and resting-state networks (RSNs) were calculated from rs-fMRI data using independent component analysis. Data were used to detect differences between responders and non-responders on the group level using permutation testing, and the single-subject level using Gaussian process classification with cross-validation. A RSN centered on the bilateral superior frontal gyrus differed between responders and non-responder groups (PFWE < 0.05) while a RSN centered on the pre-supplementary motor area distinguished between responders and non-responders on an individual-level with 81.4% accuracy (P < 0.001, 84.8% sensitivity, 78% specificity and AUC of 0.93). No significant single-subject classification or group differences were observed for gray matter volume. This proof-of-concept study demonstrates the feasibility of using rs-fMRI to develop neuroimaging biomarkers for treatment response, which could enable personalized treatment of patients with PTSD.

Though a plethora of functional magnetic resonance imaging (fMRI) studies explored the neurobiological underpinnings of borderline personality disorder (BPD), findings across different tasks were divergent. We conducted a systematic review and activation likelihood estimation (ALE) meta-analysis on the fMRI studies conducted in BPD patients compared to healthy controls (HC). We systematically searched PubMed and PsychINFO from inception until July 9th 2020 using combinations of database-specific terms like 'fMRI', 'Neuroimaging', 'borderline'. Eligible studies employed task-based fMRI of the brain in participants of any age diagnosed with BPD compared to HC, during any behavioral task and providing a direct contrast between the groups. From 762 entries, we inspected 92 reports full-texts and included 52 studies (describing 54 experiments). Across all experiments, the HC > BPD and BPD > HC meta-analyses did not yield any cluster of significant convergence of differences. Analyses restricted to studies of emotion processing revealed two significant clusters of activation in the bilateral hippocampal/amygdala complex and anterior cingulate for the BPD > HC meta-analysis. Fail-safe N and single study sensitivity analysis suggested significant findings were not robust. For the subgroup of emotional processing experiments, on a restricted number of experiments providing results for each group separately, another meta-analysis method (difference of convergence) showed a significant cluster in the insula/inferior frontal gyrus for the HC > BPD contrast. No consistent pattern of alteration in brain activity for BPD was evidenced suggesting substantial heterogeneity of processes and populations studied. A pattern of amygdala dysfunction emerged across emotion processing tasks, indicating a potential pathophysiological mechanism that could be transdiagnostic.

Physical symptoms, also known as somatic symptoms, are those for which medical examinations do not reveal a sufficient underlying root cause (e.g., pain and fatigue). The extant literature of the neurobiological underpinnings of physical symptoms is largely inconsistent and primarily comprises of (clinical) case-control studies with small sample sizes. In this cross-sectional study, we studied the association between dimensionally measured physical symptoms and brain morphology in pre-adolescents from two population-based cohorts; the Generation R Study (n = 2649, 10.1 ± 0.6 years old) and ABCD Study (n = 9637, 9.9 ± 0.6 years old). Physical symptoms were evaluated using continuous scores from the somatic complaints syndrome scale from the parent-reported Child Behavior Checklist (CBCL). High-resolution structural magnetic resonance imaging (MRI) was collected using 3-Tesla MRI systems. Linear regression models were fitted for global brain metrics (cortical and subcortical grey matter and total white matter volume) and surface-based vertex-wise measures (surface area and cortical thickness). Results were meta-analysed. Symptoms of anxiety/depression were studied as a contrasting comorbidity. In the meta-analyses across cohorts, we found negative associations between physical symptoms and surface area in the (i) left hemisphere; in the lateral orbitofrontal cortex and pars triangularis and (ii) right hemisphere; in the pars triangularis, the pars orbitalis, insula, middle temporal gyrus and caudal anterior cingulate cortex. However, only a subset of regions (left lateral orbitofrontal cortex and right pars triangularis) were specifically associated with physical symptoms, while others were also related to symptoms of anxiety/depression. No significant associations were observed for cortical thickness. This study in preadolescents, the most representative and well-powered to date, showed that more physical symptoms are modestly related to less surface area of the prefrontal cortex mostly. While these effects are subtle, future prospective research is warranted to understand the longitudinal relationship of physical symptoms and brain changes over time. Particularly, to elucidate whether physical symptoms are a potential cause or consequence of distinct neurodevelopmental trajectories.

Depression is assessed in various ways in research, with large population studies often relying on minimal phenotyping. Genetic results suggest clinical diagnoses and self-report measures of depression show some core similarities, but also important differences. It is not yet clear how neuroimaging associations depend on levels of phenotyping. We studied 39,300 UK Biobank imaging participants (20,701 female; aged 44.6 to 82.3 years, M = 64.1, SD = 7.5) with structural neuroimaging and lifetime depression data. Past depression phenotypes included a single-item self-report measure, an intermediate measure of 'probable' lifetime depression, derived from multiple questionnaire items relevant to a history of depression, and a retrospective clinical diagnosis according to DSM-IV criteria. We tested (i) associations between brain structural measures and each depression phenotype, and (ii) effects of phenotype on these associations. Depression-brain structure associations were small (β < 0.1) for all phenotypes, but still significant after FDR correction for many regional metrics. Lifetime depression was consistently associated with reduced white matter integrity across phenotypes. Cortical thickness showed negative associations with Self-reported Depression in particular. Phenotype effects were small across most metrics, but significant for cortical thickness in most regions. We report consistent effects of lifetime depression in brain structural measures, including reduced integrity of thalamic radiations and association fibres. We also observed significant differences in associations with cortical thickness across depression phenotypes. Although these results did not relate to level of phenotyping as expected, effects of phenotype definition are still an important consideration for future depression research.

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.

Developmental dyslexia (DD) is a complex neurodevelopmental deficit characterized by impaired reading acquisition, in spite of adequate neurological and sensorial conditions, educational opportunities and normal intelligence. Despite the successful characterization of DD-susceptibility genes, we are far from understanding the molecular etiological pathways underlying the development of reading (dis)ability. By focusing mainly on clinical phenotypes, the molecular genetics approach has yielded mixed results. More optimally reduced measures of functioning, that is, intermediate phenotypes (IPs), represent a target for researching disease-associated genetic variants and for elucidating the underlying mechanisms. Imaging data provide a viable IP for complex neurobehavioral disorders and have been extensively used to investigate both morphological, structural and functional brain abnormalities in DD. Performing joint genetic and neuroimaging studies in humans is an emerging strategy to link DD-candidate genes to the brain structure and function. A limited number of studies has already pursued the imaging-genetics integration in DD. However, the results are still not sufficient to unravel the complexity of the reading circuit due to heterogeneous study design and data processing. Here, we propose an interdisciplinary, multilevel, imaging-genetic approach to disentangle the pathways from genes to behavior. As the presence of putative functional genetic variants has been provided and as genetic associations with specific cognitive/sensorial mechanisms have been reported, new hypothesis-driven imaging-genetic studies must gain momentum. This approach would lead to the optimization of diagnostic criteria and to the early identification of 'biologically at-risk' children, supporting the definition of adequate and well-timed prevention strategies and the implementation of novel, specific remediation approach.

There is convincing evidence from rodent studies suggesting that prenatal infections affect the offspring's brain, but evidence in humans is limited. Here, we assessed the occurrence of common infections during each trimester of pregnancy and examined associations with brain outcomes in adolescent offspring. Our study was embedded in the Generation R Study, a large-scale sociodemographically diverse prospective birth cohort. We included 1094 mother-child dyads and investigated brain morphology (structural MRI), white matter microstructure (DTI), and functional connectivity (functional MRI), as outcomes at the age of 14. We focused on both global and focal regions. To define prenatal infections, we composed a score based on the number and type of infections during each trimester of pregnancy. Models were adjusted for several confounders. We found that prenatal infection was negatively associated with cerebral white matter volume (B = -0.069, 95% CI -0.123 to -0.015, p = 0.011), and we found an association between higher prenatal infection scores and smaller volumes of several frontotemporal regions of the brain. After multiple testing correction, we only observed an association between prenatal infections and the caudal anterior cingulate volume (B = -0.104, 95% CI -0.164 to -0.045, p < 0.001). We did not observe effects of prenatal infection on other measures of adolescent brain morphology, white matter microstructure, or functional connectivity, which is reassuring. Our results show potential regions of interest in the brain for future studies; data on the effect of severe prenatal infections on the offspring's brain in humans are needed.

Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when coupled with machine learning, can assist the diagnostic decision and increase its reliability. Sixty-four schizophrenia, 36 autism spectrum disorder (ASD), and 106 typically developing individuals were analyzed. FreeSurfer was used to obtain the data from the participant's brain scans. Six classifiers were utilized to classify the subjects. Subsequently, 26 ultra-high risk for psychosis (UHR) and 17 first-episode psychosis (FEP) subjects were run through the trained classifiers. Lastly, the classifiers' output of the patient groups was correlated with their clinical severity. All six classifiers performed relatively well to distinguish the subject groups, especially support vector machine (SVM) and Logistic regression (LR). Cortical thickness and subcortical volume feature groups were most useful for the classification. LR and SVM were highly consistent with clinical indices of ASD. When UHR and FEP groups were run with the trained classifiers, majority of the cases were classified as schizophrenia, none as ASD. Overall, SVM and LR were the best performing classifiers. Cortical thickness and subcortical volume were most useful for the classification, compared to surface area. LR, SVM, and DT's output were clinically informative. The trained classifiers were able to help predict the diagnostic category of both UHR and FEP Individuals.

In major depressive disorder (MDD) patients, nonsuicidal self-injury (NSSI) is a common comorbidity, and it is important to clarify the underlying neurobiology. Here, we investigated the association of NSSI with brain function and structure in MDD patients. A total of 260 MDD patients and 132 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging and three-dimensional T1-weighted structural scans. NSSI behaviour was assessed through interviews. Voxel-based morphometry analysis (VBM), regional homogeneity analysis (ReHo), functional connectome topology properties and network-based statistics were used to detect the differences in neuroimaging characteristics. Finally, the random forest method was used to evaluate whether these factors could predict NSSI in MDD. Compared with HCs, MDD patients with a history of NSSI showed significant right putamen grey matter volume (GMV), right superior orbital frontal cortex ReHo, left pallidum degree centrality, and putamen-centre function network differences. Compared to MDD subjects without NSSI, those with past NSSI showed significant right superior temporal gyrus (STG) GMV, right lingual gyrus ReHo, sigma and global efficiency, and cerebellum-centre function network differences. The right STG GMV and cerebellum-centre function network were more important than other factors in predicting NSSI behaviour in MDD. MDD patients with a history of NSSI have dysregulated spontaneous brain activity and structure in regions related to emotions, pain regulation, and the somatosensory system. Importantly, right STG GMV and cerebellar loops may play important roles in NSSI in MDD patients.

The vulnerability to suicidal behavior has been modeled in deficits in both valuation and cognitive control processes, mediated by ventral and dorsal prefrontal cortices. To uncover potential markers of suicidality based on this model, we measured several brain morphometric parameters using 1.5T magnetic resonance imaging in a large sample and in a specifically designed study. We then tested their classificatory properties. Three groups were compared: euthymic suicide attempters with a past history of mood disorders and suicidal behavior (N=67); patient controls with a past history of mood disorders but not suicidal behavior (N=82); healthy controls without any history of mental disorder (N=82). A hypothesis-driven region-of-interest approach was applied targeting the orbitofrontal cortex (OFC), ventrolateral (VLPFC), dorsal (DPFC) and medial (including anterior cingulate cortex; MPFC) prefrontal cortices. Both voxel-based (SPM8) and surface-based morphometry (Freesurfer) analyses were used to comprehensively evaluate cortical gray matter measure, volume, surface area and thickness. Reduced left VLPFC volume in attempters vs both patient groups was found (P=0.001, surviving multiple comparison correction, Cohen's d=0.65 95% (0.33-0.99) between attempters and healthy controls). In addition, reduced measures in OFC and DPFC, but not MPFC, were found with moderate effect sizes in suicide attempters vs healthy controls (Cohen's d between 0.34 and 0.52). Several of these measures were correlated with suicidal variables. When added to mood disorder history, left VLPFC volume increased within-sample specificity in identifying attempters in a significant but limited way. Our study, therefore, confirms structural prefrontal alterations in individuals with histories of suicide attempts. A future clinical application of these markers will, however, necessitate further research.

Visual drug cues are powerful triggers of craving in drug abusers contributing to enduring addiction. According to previous qualitative reviews, the response of the orbitofrontal cortex to such cues is sensitive to whether subjects are seeking treatment. Here we re-evaluate this proposal and assessed whether the nature of the drug matters. To this end, we performed a quantitative meta-analysis of 64 neuroimaging studies on drug-cue reactivity across legal (nicotine, alcohol) or illegal substances (cocaine, heroin). We used the ALE algorithm and a hierarchical clustering analysis followed by a cluster composition statistical analysis to assess the association of brain clusters with the nature of the substance, treatment status, and their interaction. Visual drug cues activate the mesocorticolimbic system and more so in abusers of illegal substances, suggesting that the illegal substances considered induce a deeper sensitization of the reward circuitry. Treatment status had a different modulatory role for legal and illegal substance abusers in anterior cingulate and orbitofrontal areas involved in inter-temporal decision making. The class of the substance and the treatment status are crucial and interacting factors that modulate the neural reactivity to drug cues. The orbitofrontal cortex is not sensitive to the treatment status per se, rather to the interaction of these factors. We discuss that these varying effects might be mediated by internal predispositions such as the intention to quit from drugs and external contingencies such as the daily life environmental availability of the drugs, the ease of getting them and the time frame of potential reward through drug consumption.

Brain function relies on efficient communications between distinct brain systems. The pathology of major depressive disorder (MDD) damages functional brain networks, resulting in cognitive impairment. Here, we reviewed the associations between brain functional connectome changes and MDD pathogenesis. We also highlighted the utility of brain functional connectome for differentiating MDD from other similar psychiatric disorders, predicting recurrence and suicide attempts in MDD, and evaluating treatment responses. Converging evidence has now linked aberrant brain functional network organization in MDD to the dysregulation of neurotransmitter signaling and neuroplasticity, providing insights into the neurobiological mechanisms of the disease and antidepressant efficacy. Widespread connectome dysfunctions in MDD patients include multiple, large-scale brain networks as well as local disturbances in brain circuits associated with negative and positive valence systems and cognitive functions. Although the clinical utility of the brain functional connectome remains to be realized, recent findings provide further promise that research in this area may lead to improved diagnosis, treatments, and clinical outcomes of MDD.

With an increasing prevalence of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in response to an aging population, it is critical to identify and understand neuroprotective mechanisms against cognitive decline. One potential mechanism is redundancy: the existence of duplicate elements within a system that provide alternative functionality in case of failure. As the hippocampus is one of the earliest sites affected by AD pathology, we hypothesized that functional hippocampal redundancy is protective against cognitive decline. We compared hippocampal functional redundancy derived from resting-state functional MRI networks in cognitively normal older adults, with individuals with early and late MCI, as well as the relationship between redundancy and cognition. Posterior hippocampal redundancy was reduced between cognitively normal and MCI groups, plateauing across early and late MCI. Higher hippocampal redundancy was related to better memory performance only for cognitively normal individuals. Critically, functional hippocampal redundancy did not come at the expense of network efficiency. Our results provide support that hippocampal redundancy protects against cognitive decline in aging.

Mindfulness-based interventions are showing increasing promise as a treatment for psychological disorders, with improvements in cognition and emotion regulation after intervention. Understanding the changes in functional brain activity and neural plasticity that underlie these benefits from mindfulness interventions is thus of interest in current neuroimaging research. Previous studies have found functional brain changes during resting and task states to be associated with mindfulness both cross-sectionally and longitudinally, particularly in the executive control, default mode and salience networks. However, limited research has combined information from rest and task to study mindfulness-related functional changes in the brain, particularly in the context of intervention studies with active controls. Recent work has found that the reconfiguration efficiency of brain activity patterns between rest and task states is behaviorally relevant in healthy young adults. Thus, we applied this measure to investigate how mindfulness intervention changed functional reconfiguration between rest and a breath-counting task in elderly participants with self-reported sleep difficulties. Improving on previous longitudinal designs, we compared the intervention effects of a mindfulness-based therapy to an active control (sleep hygiene) intervention. We found that mindfulness intervention improved self-reported mindfulness measures and brain functional reconfiguration efficiency in the executive control, default mode and salience networks, though the brain and behavioral changes were not associated with each other. Our findings suggest that neuroplasticity may be induced through regular mindfulness practice, thus bringing the intrinsic functional configuration in participants' brains closer to a state required for mindful awareness.

Neuroimaging studies over the last two decades have begun to specify the neurobiological correlates of psychopathy, a personality disorder that is strongly related to criminal offending and recidivism. Despite the accumulation of neuroimaging studies of psychopathy, a clear and comprehensive picture of the disorder's neural correlates has yet to emerge. The current study is a meta-analysis of functional MRI studies of psychopathy. Multilevel kernel density analysis was used to identify consistent findings across 25 studies (460 foci) of task-related brain activity. Psychopathy was associated with increased task-related activity predominantly in midline cortical regions overlapping with the default mode network (dorsomedial prefrontal cortex, posterior cingulate, and precuneus) as well as medial temporal lobe (including amygdala). Psychopathy was related to decreased task-related activity in a region of the dorsal anterior cingulate cortex overlapping with the salience network. These findings challenge predominant theories of amygdala hypoactivity and highlight the potential role of hyperactivity in medial default mode network regions and hypoactivity in a key node of the salience network during task performance in psychopathy.

Previous animal models and structural imaging investigations have linked hippocampal neuroplasticity to electroconvulsive therapy (ECT) response, but the relationship between changes in hippocampal volume and temporal coherence in the context of ECT response is unknown. We hypothesized that ECT response would increase both hippocampal resting-state functional magnetic resonance imaging connectivity and hippocampal volumes. Patients with major depressive disorder (n=19) were scanned before and after the ECT series. Healthy, demographically matched comparisons (n=20) were scanned at one-time interval. Longitudinal changes in functional connectivity of hippocampal regions and volumes of hippocampal subfields were compared with reductions in ratings of depressive symptoms. Right hippocampal connectivity increased (normalized) after the ECT series and correlated with depressive symptom reduction. Similarly, the volumes of the right hippocampal cornu ammonis (CA2/3), dentate gyrus and subiculum regions increased, but the hippocampal subfields were unchanged relative to the comparison group. Connectivity changes were not evident in the left hippocampus, and volume changes were limited to the left CA2/3 subfields. The laterality of the right hippocampal functional connectivity and volume increases may be related to stimulus delivery method, which was predominately right unilateral in this investigation. The findings suggested that increased hippocampal functional connectivity and volumes may be biomarkers for ECT response.