Identifying individuals with antisocial personality disorder using resting-state FMRI

The neurobiology of antisocial personality disorder

Despite increasing recognition that there is a neurobiological basis of antisocial behavior in addition to its psychosocial foundation, much less is known about the specificity of the neurobiological findings to the psychiatric condition of antisocial personality disorder (APD). This article provides a review of research on genetic, brain imaging, neurocognitive, and psychophysiological factors in relation to assessments of APD. Findings show that there are significant genetic effects on APD, particularly related to the serotonergic system, as well as abnormalities in brain regions such as the frontal lobe. Associations between psychophysiological measures of autonomic nervous system functioning and APD are more mixed. Results indicating that APD has a significant genetic basis and is characterized by abnormalities in brain structure/function and neurocognitive impairments provide additional evidence that supports the conceptualization of APD as a neurodevelopmental disorder. Findings may also help inform treatment approaches that target neurobiological risks for APD symptoms. This article is part of the Special Issue on "Personality Disorders".

Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality

INTRODUCTION

Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear.

METHODS

We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset.

RESULTS

SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control.

CONCLUSION

Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

BACKGROUND

The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.

METHODS

Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses.

RESULTS

A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β =  - 2.75, p < .001, R²_adj = 0.40; r =  - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF₁₀ > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance.

CONCLUSIONS

Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Antisocial behavior is associated with reduced frontoparietal activity to loss in a population-based sample of adolescents

BACKGROUND

Adolescent antisocial behavior (AB) is a public health concern due to the high financial and social costs of AB on victims and perpetrators. Neural systems involved in reward and loss processing are thought to contribute to AB. However, investigations into these processes are limited: few have considered anticipatory and consummatory components of reward, response to loss, nor whether associations with AB may vary by level of callous-unemotional (CU) traits.

METHODS

A population-based community sample of 128 predominantly low-income youth (mean age = 15.9 years; 42% male) completed a monetary incentive delay task during fMRI. A multi-informant, multi-method latent variable approach was used to test associations between AB and neural response to reward and loss anticipation and outcome and whether CU traits moderated these associations.

RESULTS

AB was not associated with neural response to reward but was associated with reduced frontoparietal activity during loss outcomes. This association was moderated by CU traits such that individuals with higher levels of AB and CU traits had the largest reductions in frontoparietal activity. Co-occurring AB and CU traits were also associated with increased precuneus response during loss anticipation.

CONCLUSIONS

Findings indicate that AB is associated with reduced activity in brain regions involved in cognitive control, attention, and behavior modification during negative outcomes. Moreover, these reductions are most pronounced in youth with co-occurring CU traits. These findings have implications for understanding why adolescents involved in AB continue these behaviors despite severe negative consequences (e.g. incarceration).

Dimensional personality impairment is associated with disruptions in intrinsic intralimbic functional connectivity

BACKGROUND

Recently proposed alternative dimensional models of personality disorder (PD) place the severity of impairments in self and interpersonal functioning at the core of personality pathology. However, associations of these impairments with disturbances in social, cognitive, and affective brain networks remain uninvestigated.

METHODS

The present study examined patterns of resting-state functional connectivity (rsFC) in a sample of 74 age- and sex-matched participants (45 inpatients with PD and 29 healthy controls). At a minimum, PD patients carried a diagnosis of borderline PD, although the majority of the sample had one or more additional PDs. rsFC patterns in the following networks were compared between groups and in association with dimensional personality impairments: default mode network (DMN)/core mentalization, frontolimbic, salience, and central executive. Further, the extent to which variation in rsFC was explained by levels of personality impairment as compared to typology-specific borderline PD symptom severity was explored.

RESULTS

Relative to controls, the PD group showed disruptions in rsFC within the DMN/core mentalization and frontolimbic networks. Among PD patients, greater severity of dimensional self-interpersonal impairment was associated with stronger intralimbic rsFC. In contrast, severity of borderline PD-specific typology was not associated with any rsFC patterns.

CONCLUSIONS

Disruptions in core mentalization and affective networks are present in PD. Higher intralimbic functional connectivity may underlie self-interpersonal personality impairment in PD regardless of diagnostic typology-specific PD symptoms, providing initial neurobiological evidence supporting alternative dimensional conceptualizations of personality pathology.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

OBJECTIVE

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

EVIDENCE REVIEW

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

FINDINGS

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

CONCLUSIONS AND RELEVANCE

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Associations between disrupted functional brain network topology and cognitive impairment in patients with rectal cancer during chemotherapy

Introduction

Cognitive impairment has been identified in patients with non-central nervous system cancer received chemotherapy. Chemotherapy-induced changes in the brain are considered as the possible causes of the cognitive deficits of patients. This study aimed to explore chemotherapy-related functional brain changes and cognitive impairment in rectal cancer (RC) patients who had just finished chemotherapy treatment.

Methods

In this study, RC patients after chemotherapy (on the day patients received the last dose of chemotherapy) (n=30) and matched healthy controls (HCs) (n=30) underwent cognitive assessments, structural magnetic resonance imaging (MRI) and resting-state functional MRI. The functional brain networks were constructed by thresholding the partial correlation matrices of 90 brain regions in the Anatomical Automatic Labeling template and the topologic properties were evaluated by graph theory analysis. Moreover, correlations between altered topological measures and scores of cognitive scales were explored in the patient group.

Results

Compared with HCs, RC patients had lower scores of cognitive scales. The functional brain network had preserved small-world topological features but with a tendency towards higher path length in the whole network. In addition, patients had decreased nodal global efficiency (E_glo(i)) in the left superior frontal gyrus (dorsolateral), superior frontal gyrus (orbital part), inferior frontal gyrus (opercular part), inferior frontal gyrus (triangular part) and right inferior frontal gyrus (triangular part). Moreover, values of E_glo(i) in the superior and inferior frontal gyrus were positively associated with cognitive function in the patient group.

Conclusion

These results suggested that cognitive impairment was associated with disruptions of the topological organization in functional brain networks of RC patients who had just finished chemotherapy, which provided new insights into the pathophysiology underlying acute effects of chemotherapy on cognitive function.

Aberrant resting-state functional connectivity in incarcerated women with elevated psychopathic traits

Previous work in incarcerated men suggests that individuals scoring high on psychopathy exhibit aberrant resting-state paralimbic functional network connectivity (FNC). However, it is unclear whether similar results extend to women scoring high on psychopathy. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist - Revised (PCL-R)] were associated with aberrant inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of fluctuations across limbic and surrounding paralimbic regions among incarcerated women (n = 297). Resting-state networks were identified by applying group Independent Component Analysis to resting-state fMRI scans. We tested the association of psychopathic traits (PCL-R Factor 1 measuring interpersonal/affective psychopathic traits and PCL-R Factor 2 assessing lifestyle/antisocial psychopathic traits) to the three FNC measures. PCL-R Factor 1 scores were associated with increased low-frequency fluctuations in executive control and attentional networks, decreased high-frequency fluctuations in executive control and visual networks, and decreased intra-network FNC in default mode network. PCL-R Factor 2 scores were associated with decreased high-frequency fluctuations and default mode networks, and both increased and decreased intra-network functional connectivity in visual networks. Similar to previous analyses in incarcerated men, our results suggest that psychopathic traits among incarcerated women are associated with aberrant intra-network amplitude fluctuations and connectivity across multiple networks including limbic and surrounding paralimbic regions.

Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data

Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features.

Neural alterations in opioid-exposed infants revealed by edge-centric brain functional networks

Prenatal opioid exposure has been linked to adverse effects spanning multiple neurodevelopmental domains, including cognition, motor development, attention, and vision. However, the neural basis of these abnormalities is largely unknown. A total of 49 infants, including 21 opioid-exposed and 28 controls, were enrolled and underwent MRI (43 ± 6 days old) after birth, including resting state functional MRI. Edge-centric functional networks based on dynamic functional connections were constructed, and machine-learning methods were employed to identify neural features distinguishing opioid-exposed infants from unexposed controls. An accuracy of 73.6% (sensitivity 76.25% and specificity 69.33%) was achieved using 10 times 10-fold cross-validation, which substantially outperformed those obtained using conventional static functional connections (accuracy 56.9%). More importantly, we identified that prenatal opioid exposure preferentially affects inter- rather than intra-network dynamic functional connections, particularly with the visual, subcortical, and default mode networks. Consistent results at the brain regional and connection levels were also observed, where the brain regions and connections associated with visual and higher order cognitive functions played pivotal roles in distinguishing opioid-exposed infants from controls. Our findings support the clinical phenotype of infants exposed to opioids in utero and may potentially explain the higher rates of visual and emotional problems observed in this population. Finally, our findings suggested that edge-centric networks could better capture the neural differences between opioid-exposed infants and controls by abstracting the intrinsic co-fluctuation along edges, which may provide a promising tool for future studies focusing on investigating the effects of prenatal opioid exposure on neurodevelopment.

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

Functional MRI findings in personality disorders: A review

Personality disorders (PDs) have a prevalence of approximately 10% in the United States, translating to over 30 million people affected in just one country. The true prevalence of these disorders may be even higher, as the paucity of objective diagnostic criteria could be leading to underdiagnosis. Because little is known about the underlying neuropathologies of these disorders, patients are diagnosed using subjective criteria and treated nonspecifically. To better understand the neural aberrancies responsible for these patients' symptoms, a review of functional MRI literature was performed. The findings reveal that each PD is characterized by a unique set of activation changes corresponding to individual structures or specific neural networks. While unique patterns of neural activity are distinguishable within each PD, aberrations of the limbic/paralimbic structures and default mode network are noted across several of them. In addition to identifying valuable activation patterns, this review reveals a void in research pertaining to paranoid, schizoid, histrionic, narcissistic, and dependent PDs. By delineating patterns in PD neuropathology, we can more effectively direct future research efforts toward enhancing objective diagnostic techniques and developing targeted treatment modalities. Furthermore, understanding why patients are manifesting certain symptoms can advance clinical awareness and improve patient outcomes.

Risk and resilience for alcohol use disorder revealed in brain functional connectivity

A family history of alcoholism (FH) increases risk for alcohol use disorder (AUD), yet many at-risk individuals never develop alcohol use problems. FH is associated with intermediate levels of risk phenotypes, whereas distinct, compensatory brain changes likely promote resilience. Although several cognitive, behavioral, and personality factors have been associated with AUD, the relative contributions of these processes and their neural underpinnings to risk or resilience processes remains less clear. We examined whole-brain resting-state functional connectivity (FC) and behavioral metrics from 841 young adults from the Human Connectome Project, including healthy controls, individuals with AUD, and their unaffected siblings. First, we identified functional connections in which unaffected siblings were intermediate between controls and AUD, indicating AUD risk, and those in which siblings diverged, indicating resilience. Canonical correlations relating brain risk and resilience FC to behavioral patterns revealed AUD risk and resilience phenotypes. Risk phenotypes primarily implicated frontal-parietal networks corresponding with executive function, impulsivity, externalizing behaviors, and social-emotional intelligence. Conversely, resilience-related phenotypes were underpinned by networks of medial prefrontal, striatal, temporal, brainstem and cerebellar connectivity, which associated with high trait attention and low antisocial behavior. Additionally, we calculated "polyphenotypic" risk and resilience scores, to investigate how the relative load of risk and resilience phenotypes influenced the probability of an AUD diagnosis. Polyphenotypic scores predicted AUD in a dose-dependent manner. Moreover, resilience phenotypes interacted with risk phenotypes, reducing their effects. The hypothesis-generating results revealed interpretable AUD-related phenotypes and offer brain-informed targets for developing more effective interventions.

Aggression subtypes relate to distinct resting state functional connectivity in children and adolescents with disruptive behavior

There is increasing evidence for altered brain resting state functional connectivity in adolescents with disruptive behavior. While a considerable body of behavioral research points to differences between reactive and proactive aggression, it remains unknown whether these two subtypes have dissociable effects on connectivity. Additionally, callous-unemotional traits are important specifiers in subtyping aggressive behavior along the affective dimension. Accordingly, we examined associations between two aggression subtypes along with callous-unemotional traits using a seed-to-voxel approach. Six functionally relevant seeds were selected to probe the salience and the default mode network, based on their presumed role in aggression. The resting state sequence was acquired from 207 children and adolescents of both sexes [mean age (standard deviation) = 13.30 (2.60); range = 8.02-18.35] as part of a Europe-based multi-center study. One hundred eighteen individuals exhibiting disruptive behavior (conduct disorder/oppositional defiant disorder) with varying comorbid attention-deficit/hyperactivity disorder (ADHD) symptoms were studied, together with 89 healthy controls. Proactive aggression was associated with increased left amygdala-precuneus coupling, while reactive aggression related to hyper-connectivities of the posterior cingulate cortex (PCC) to the parahippocampus, the left amygdala to the precuneus and to hypo-connectivity between the right anterior insula and the nucleus caudate. Callous-unemotional traits were linked to distinct hyper-connectivities to frontal, parietal, and cingulate areas. Additionally, compared to controls, cases demonstrated reduced connectivity of the PCC and left anterior insula to left frontal areas, the latter only when controlling for ADHD scores. Taken together, this study revealed aggression-subtype-specific patterns involving areas associated with emotion, empathy, morality, and cognitive control.

Präzisionspsychiatrie und der Beitrag von Brain Imaging und anderen Biomarkern

'Precision Psychiatry' as the psychiatric variant of 'Precision Medicine' aims to provide high-level diagnosis and treatment based on robust biomarkers and tailored to the individual clinical, neurobiological, and genetic constitution of the patient. The specific peculiarity of psychiatry, in which disease entities are normatively defined based on clinical experience and are also significantly influenced by contemporary history, society and philosophy, has so far made the search for valid and reliable psychobiological connections difficult. Nevertheless, considerable progress has now been made in all areas of psychiatric research, made possible above all by the critical review and renewal of previous concepts of disease and psychopathology, the increased orientation towards neurobiology and genetics, and in particular the use of machine learning methods. Notably, modern machine learning methods make it possible to integrate high-dimensional and multimodal data sets and generate models which provide new psychobiological insights and offer the possibility of individualized, biomarker-driven single-subject prediction of diagnosis, therapy response and prognosis. The aim of the present review is therefore to introduce the concept of 'Precision Psychiatry' to the interested reader, to concisely present modern, machine learning methods required for this, and to clearly present the current state and future of biomarker-based 'precision psychiatry'.

Aberrant default mode network and auditory network underlying the sympathetic skin response of the penis (PSSR) of patients with premature ejaculation: A resting-state fMRI study

INTRODUCTION

Hyperactivity of the sympathetic nervous system is considered as an important component involved in the pathological mechanisms of premature ejaculation (PE). However, the neural mechanisms of PE with high sympathetic activity are still not well understood.

METHODS

The activity of the sympathetic innervations in the penis was evaluated by the sympathetic skin response of the penis (PSSR) with an electromyograph and evoked potential equipment. Resting-state functional magnetic resonance imaging (fMRI) data were acquired from 18 PE patients with high sympathetic activity (sPE), 17 PE patients with normal sympathetic activity (nsPE), and 24 healthy controls (HC). We investigated the neural basis of sPE based on the measure of regional homogeneity (ReHo). Moreover, the correlations between brain regions with altered ReHo and PEDT scores and PSSR latencies in the patient group were explored.

RESULTS

Altered ReHo values among three groups were found in the temporal, cingulated, and parietal cortex in the default mode network (DMN), as well as the temporal cortex in the auditory network (AUD). Compared with HC, Patients with sPE had increased ReHo values of brain regions in DMN, AUD, and decreased ReHo values of brain regions in DMN. In addition, increased ReHo values were found in DMN of patients with nsPE, while decreased ReHo values were found in DMN and the attention network (AN). Moreover, sPE patients had increased ReHo values in AUD and decreased ReHo values in DMN when compared with nsPE patients. Finally, altered ReHo values of brain regions in DMN and AUD were associated with PEDT scores and PSSR latencies in the patient group.

CONCLUSION

Our results suggested that PE patients had abnormal ReHo values in DMN, AUD, and AN. Patients with sPE were characterized by increased neuronal activity in AUD and decreased activity in DMN. This highlighted the significances of DMN, AUD, and AN in the pathophysiology of PE and also provided potential neuroimaging biomarkers for distinguishing sPE from nsPE and HC.

Gradually evaluating of suicidal risk in depression by semi-supervised cluster analysis on resting-state fMRI

A timely and effective evaluation of the suicidal ideation bears practical meaning, particularly for the depressive who tend to disguise the real suicide intent and without obvious symptoms. Measuring individual ideation of the depression with uncertain or transient suicide crisis is the purpose. Resting-state fMRI data were collected from 78 depressed patients with variable clinical suicidal crisis. Thirty subjects were well labeled as extremely serious individuals with suicide attempters or as without suicidal ideation. A feature mask was constructed via the two sample t-test on their regional conncectivities. Then, a semi-supervised machine learning frame using the feature mask was designed to assist in clarifying gradation of suicidal susceptibility for the residual forty-eight vaguely defined subjects, by a way of Iterative Self-Organizing Data analysis techniques (ISODATA). Such semi-supervised model was designed purposely to block out the effect of disease itself on the suicide intendancy evaluation. The vague-labeled patients were divided into another two different stages relating to their suicidal susceptibility. The distance ratio of each subject to the two well-defined extreme groups in the feature space can be utilized as the suicide risk index. The re-evaluation of the Nurses' Global Assessment of Suicide Risk (NGASR) via experts blind to original HAM-D rates was significantly correlated with the model estimation. The constructed model suggested its potential to examine the risk of suicidal in an objective way. The functional connectivity, locating mostly within the frontal-temporal circuit and involving the default mode network (DMN), were well integrated to discriminative the gradual susceptibility of suicidal.

Connections that characterize callousness: Affective features of psychopathy are associated with personalized patterns of resting-state network connectivity

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There was significant heterogeneity in participants’ neural networks.

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Psychopathy associated with default mode-central executive network connectivity.

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Associations were specific to affective psychopathic traits.

Background

Psychopathic traits are hypothesized to be associated with dysfunction across three resting-state networks: the default mode (DMN), salience (SN), and central executive (CEN). Past work has not considered heterogeneity in the neural networks of individuals who display psychopathic traits, which is likely critical in understanding the etiology of psychopathy and could underlie different symptom presentations. Thus, this study maps person-specific resting state networks and links connectivity patterns to features of psychopathy.

Methods

We examined resting-state functional connectivity among eight regions of interest in the DMN, SN, and CEN using a person-specific, sparse network mapping approach (Group Iterative Multiple Model Estimation) in a community sample of 22-year-old men from low-income, urban families (N = 123). Associations were examined between a dimensional measure of psychopathic traits and network density (i.e., number of connections within and between networks).

Results

There was significant heterogeneity in neural networks of participants, which were characterized by person-specific connections and no common connections across the sample. Psychopathic traits, particularly affective traits, were associated with connection density between the DMN and CEN, such that greater density was associated with elevated psychopathic traits.

Discussion

Findings emphasize that neural networks underlying psychopathy are highly individualized. However, individuals with high levels of psychopathic traits had increased density in connections between the DMN and CEN, networks that have been linked with self-referential thinking and executive functioning. Taken together, the results highlight the utility of person-specific approaches in modeling neural networks underlying psychopathic traits, which could ultimately inform personalized prevention and intervention strategies.

Antisocial behavior with callous-unemotional traits is associated with widespread disruptions to white matter structural connectivity among low-income, urban males

Antisocial behavior (AB), including violence, criminality, and substance abuse, is often linked to deficits in emotion processing, reward-related learning, and inhibitory control, as well as their associated neural networks. To better understand these deficits, the structural connections between brain regions implicated in AB can be examined using diffusion tensor imaging (DTI), which assesses white matter microstructure. Prior studies have identified differences in white matter microstructure of the uncinate fasciculus (UF), primarily within offender samples. However, few studies have looked beyond the UF or determined whether these relationships are present dimensionally across the range of AB and callous-unemotional (CU) traits. In the current study, we examined associations between AB and white matter microstructure from major fiber tracts, including the UF. Further, we explored whether these associations were specific to individuals high on CU traits. Within a relatively large community sample of young adult men from low-income, urban families (N = 178), we found no direct relations between dimensional, self-report measures of either AB or CU traits and white matter microstructure. However, we found significant associations between AB and white matter microstructure of several tracts only for those with high co-occurring levels of CU traits. In general, these associations did not differ according to race, socioeconomic status, or comorbid psychiatric symptoms. The current results suggest a unique neural profile of severe AB in combination with CU traits, characterized by widespread differences in white matter microstructure, which differs from either AB or CU traits in isolation and is not specific to hypothesized tracts (i.e., the UF).

Is there an "antisocial" cerebellum? Evidence from disorders other than autism characterized by abnormal social behaviours

The cerebellum is a hindbrain structure which involvement in functions not related to motor control and planning is being increasingly recognized in the last decades. Studies on Autism Spectrum Disorders (ASD) have reported cerebellar involvement on these conditions characterized by social deficits and repetitive motor behavior patterns. Although such an involvement hints at a possible cerebellar participation in the social domain, the fact that ASD patients present both social and motor deficits impedes drawing any firm conclusion regarding cerebellar involvement in pathological social behaviours, probably influenced by the classical view of the cerebellum as a purely "motor" brain structure. Here, we suggest the cerebellum can be a key node for the production and control of normal and particularly aberrant social behaviours, as indicated by its involvement in other neuropsychiatric disorders which main symptom is deregulated social behaviour. Therefore, in this work, we briefly review cerebellar involvement in social behavior in rodent models, followed by discussing the findings linking the cerebellum to those other psychiatric conditions characterized by defective social behaviours. Finally, possible commonalities between the studies and putative underlying impaired functions will be discussed and experimental approaches both in patients and experimental animals will also be proposed, aimed at stimulating research on the role of the cerebellum in social behaviours and disorders characterized by social impairments, which, if successful, will definitely help reinforcing the proposed cerebellar involvement in the social domain.

A Systematic Literature Review of Neuroimaging of Psychopathic Traits

INTRODUCTION

Core psychopathy is characterized by grandiosity, callousness, manipulativeness, and lack of remorse, empathy, and guilt. It is often comorbid with conduct disorder and antisocial personality disorder (ASPD). Psychopathy is present in forensic as well as prison and general populations. In recent years, an increasing amount of neuroimaging studies has been conducted in order to elucidate the obscure neurobiological etiology of psychopathy. The studies have yielded heterogenous results, and no consensus has been reached.

AIMS

This study systematically reviewed and qualitatively summarized functional and structural neuroimaging studies conducted on individuals with psychopathic traits. Furthermore, this study aimed to evaluate whether the findings from different MRI modalities could be reconciled from a neuroanatomical perspective.

MATERIALS AND METHODS

After the search and auditing processes, 118 neuroimaging studies were included in this systematic literature review. The studies consisted of structural, functional, and diffusion tensor MRI studies.

RESULTS

Psychopathy was associated with numerous neuroanatomical abnormalities. Structurally, gray matter anomalies were seen in frontotemporal, cerebellar, limbic, and paralimbic regions. Associated gray matter volume (GMV) reductions were most pronounced particularly in most of the prefrontal cortex, and temporal gyri including the fusiform gyrus. Also decreased GMV of the amygdalae and hippocampi as well the cingulate and insular cortices were associated with psychopathy, as well as abnormal morphology of the hippocampi, amygdala, and nucleus accumbens. Functionally, psychopathy was associated with dysfunction of the default mode network, which was also linked to poor moral judgment as well as deficient metacognitive and introspective abilities. Second, reduced white matter integrity in the uncinate fasciculus and dorsal cingulum were associated with core psychopathy. Third, emotional detachment was associated with dysfunction of the posterior cerebellum, the human mirror neuron system and the Theory of Mind denoting lack of empathy and persistent failure in integrating affective information into cognition.

CONCLUSIONS

Structural and functional aberrancies involving the limbic and paralimbic systems including reduced integrity of the uncinate fasciculus appear to be associated with core psychopathic features. Furthermore, this review points towards the idea that ASPD and psychopathy might stem from divergent biological processes.

Identifying mild-moderate Parkinson's disease using whole-brain functional connectivity

OBJECTIVE

Our study aims to extract significant disorder-associated patterns from whole brain functional connectivity to distinguish mild-moderate Parkinson's disease (PD) patients from controls.

METHODS

Resting-state fMRI data were measured from thirty-six PD individuals and thirty-five healthy controls. Multivariate pattern analysis was applied to investigate whole-brain functional connectivity patterns in individuals with 'mild-moderate' PD. Additionally, the relationship between the asymmetry of functional connectivity and the side of the initial symptoms was also analyzed.

RESULTS

In a leave-one-out cross-validation, we got the generalization rate of 80.28% for distinguishing PD patients from controls. The most discriminative functional connectivity was found in cortical networks that included the default mode, sensorimotor and attention networks. Compared to patients with the left side initially affected, an increased abnormal functional connectivity was found in patients in whom the right side was initially affected.

CONCLUSIONS

Our results indicated that discriminative functional connectivity is likely associated with disturbances of cortical networks involved in sensorimotor control and attention. The spatiotemporal patterns of motor asymmetry may be related to the lateralized dysfunction on the early stages of PD.

SIGNIFICANCE

This study identifies discriminative functional connectivity that is associated with disturbances of cortical networks. Our results demonstrated new evidence regarding the functional brain changes related to the unilateral motor symptoms of early PD.

Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning

Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.

Disrupted functional connectome in antisocial personality disorder

Studies on antisocial personality disorder (ASPD) subjects focus on brain functional alterations in relation to antisocial behaviors. Neuroimaging research has identified a number of focal brain regions with abnormal structures or functions in ASPD. However, little is known about the connections among brain regions in terms of inter-regional whole-brain networks in ASPD patients, as well as possible alterations of brain functional topological organization. In this study, we employ resting-state functional magnetic resonance imaging (R-fMRI) to examine functional connectome of 32 ASPD patients and 35 normal controls by using a variety of network properties, including small-worldness, modularity, and connectivity. The small-world analysis reveals that ASPD patients have increased path length and decreased network efficiency, which implies a reduced ability of global integration of whole-brain functions. Modularity analysis suggests ASPD patients have decreased overall modularity, merged network modules, and reduced intra- and inter-module connectivities related to frontal regions. Also, network-based statistics show that an internal sub-network, composed of 16 nodes and 16 edges, is significantly affected in ASPD patients, where brain regions are mostly located in the fronto-parietal control network. These results suggest that ASPD is associated with both reduced brain integration and segregation in topological organization of functional brain networks, particularly in the fronto-parietal control network. These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD.

Associating resting-state connectivity with trait impulsivity

Psychometric research has identified stable traits that predict inter-individual differences in appetitive motivation and approach behavior. Behavioral Inhibition System/Behavioral Activation System (BIS/BAS) scales have been developed to quantitatively assess these traits. However, neural mechanisms corresponding to the proposed constructs reflected in BIS/BAS are still poorly defined. The ventral striatum (VS) and orbitofrontal cortex (OFC) are implicated in subserving reward-related functions that are also associated with the BAS. In this study, we examined whether functional connectivity between these regions predicts components of these scales. We employed resting-state functional connectivity and BIS/BAS scores assessed by a personality questionnaire. Participants completed a resting state run and the Behavioral Inhibition and Activation Systems (BIS/BAS) Questionnaire. Using resting-state BOLD, we assessed correlations between two basal ganglia ROIs (caudate and putamen) and bilateral OFC ROIs, establishing single subject connectivity summary scores. Summary scores were correlated with components of BIS/BAS scores. Results demonstrate a novel correlation between BAS-fun seeking and resting-state connectivity between middle OFC and putamen, implying that spontaneous synchrony between reward-processing regions may play a role in defining personality characteristics related to impulsivity.

Alterations of Brain Functional Architecture Associated with Psychopathic Traits in Male Adolescents with Conduct Disorder

Psychopathic traits of conduct disorder (CD) have a core callous-unemotional (CU) component and an impulsive-antisocial component. Previous task-driven fMRI studies have suggested that psychopathic traits are associated with dysfunction of several brain areas involved in different cognitive functions (e.g., empathy, reward, and response inhibition etc.), but the relationship between psychopathic traits and intrinsic brain functional architecture has not yet been explored in CD. Using a holistic brain-wide functional connectivity analysis, this study delineated the alterations in brain functional networks in patients with conduct disorder. Compared with matched healthy controls, we found decreased anti-synchronization between the fronto-parietal network (FPN) and default mode network (DMN), and increased intra-network synchronization within the frontothalamic-basal ganglia, right frontoparietal, and temporal/limbic/visual networks in CD patients. Correlation analysis showed that the weakened FPN-DMN interaction was associated with CU traits, while the heightened intra-network functional connectivity was related to impulsivity traits in CD patients. Our findings suggest that decoupling of cognitive control (FPN) with social understanding of others (DMN) is associated with the CU traits, and hyper-functions of the reward and motor inhibition systems elevate impulsiveness in CD.

Development of self-inflicted injury: Comorbidities and continuities with borderline and antisocial personality traits

Self-inflicted injury (SII) is a continuum of intentionally self-destructive behaviors, including nonsuicidal self-injuries, suicide attempts, and death by suicide. These behaviors are among the most pressing yet perplexing clinical problems, affecting males and females of every race, ethnicity, culture, socioeconomic status, and nearly every age. The complexity of these behaviors has spurred an immense literature documenting risk and vulnerability factors ranging from individual to societal levels of analysis. However, there have been relatively few attempts to articulate a life span developmental model that integrates ontogenenic processes across these diverse systems. The objective of this review is to outline such a model with a focus on how observed patterns of comorbidity and continuity can inform developmental theories, early prevention efforts, and intervention across traditional diagnostic boundaries. Specifically, when SII is viewed through the developmental psychopathology lens, it becomes apparent that early temperamental risk factors are associated with risk for SII and a range of highly comorbid conditions, such as borderline and antisocial personality disorders. Prevention efforts focused on early-emerging biological and temperamental contributors to psychopathology have great potential to reduce risk for many presumably distinct clinical problems. Such work requires identification of early biological vulnerabilities, behaviorally conditioned social mechanisms, as well as societal inequities that contribute to self-injury and underlie intergenerational transmission of risk.

A CCA and ICA-Based Mixture Model for Identifying Major Depression Disorder

The fMRI signals are usually filtered before processing and analyzing. This process can result in the loss of information carried by the higher frequency in the low frequency fluctuation. ICA and CCA are two classical methods in fMRI. ICA finds the statistically independent components of the observed data, however these components are usually physiologically uninterpretable without auxiliary procedures. CCA decomposes two sets of data into component pairs in some order, however these components may be mixtures of real signals and noise. In order to obtain statistically independent components and avoid the loss of information in the process of filtering, we propose a mixed model based on ICA and CCA, which does not need to filter the data. It is shown by the experiments that the new model has some advantages compared with the classical ICA and CCA. The components obtained by the new model is statistically independent. The useful information included in the low frequency fluctuation can be preserved. Experiments on synthetic data show satisfying results. As an application, this new model is used to design an algorithm to discriminate the major depressions from normal controls, with encouraging experimental results.

Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images

The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a "proof of concept" about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis.

Identifying the presence of Parkinson's disease using low-frequency fluctuations in BOLD signals

Parkinson's disease (PD) is a chronic, progressive, and degenerative neurological disorder that is characterized by the degeneration of dopamine neurons in the substantia nigra and the formation of intracellular Lewy inclusion bodies. Resting-state functional magnetic resonance imaging (RS-fMRI) has demonstrated evidence of changes in metabolic patterns in individuals with PD. The purpose of this study was to determine whether the presence of PD could be "predicted" based on resting fluctuations in the blood oxygenation level dependent signal. We utilized RS-fMRI to measure the amplitude of low-frequency fluctuation (ALFF) and the fractional ALFF (fALFF) in 51 patients with PD and 50 age- and sex-matched healthy controls. Compared with the healthy controls, the individuals with PD exhibited altered ALFFs in the bilateral lingual gyrus and left putamen and an altered fALFF in the right cerebellum posterior lobe. Support vector machines (SVMs), which comprise a supervised pattern recognition method that enables predictions at the individual level, were trained to separate individuals with PD from healthy controls based on the ALFF and fALFF. Using the leave-one-out cross-validation method to analyze our sample, we reliably distinguished the participants with PD from the controls with 92% sensitivity and 87% specificity. Overall, these findings suggest that the SVM-neuroimaging approach may be of particular clinical value because it enables the accurate identification of PD at the individual level. RS-fMRI should be considered for development as a biomarker and an analytical tool for the evaluation of PD.

Reduced cortical thickness and increased surface area in antisocial personality disorder

Antisocial personality disorder (ASPD), one of whose characteristics is high impulsivity, is of great interest in the field of brain structure and function. However, little is known about possible impairments in the cortical anatomy in ASPD, in terms of cortical thickness (CTh) and surface area (SA), as well as their possible relationship with impulsivity. In this neuroimaging study, we first investigated the changes of CTh and SA in ASPD patients, in comparison to those of healthy controls, and then performed correlation analyses between these measures and the ability of impulse control. We found that ASPD patients showed thinner cortex while larger SA in several specific brain regions, i.e., bilateral superior frontal gyrus (SFG), orbitofrontal and triangularis, insula cortex, precuneus, middle frontal gyrus (MFG), middle temporal gyrus (MTG), and left bank of superior temporal sulcus (STS). In addition, we also found that the ability of impulse control was positively correlated with CTh in the SFG, MFG, orbitofrontal cortex (OFC), pars triangularis, superior temporal gyrus (STG), and insula cortex. To our knowledge, this study is the first to reveal simultaneous changes in CTh and SA in ASPD, as well as their relationship with impulsivity. These cortical structural changes may introduce uncontrolled and callous behavioral characteristic in ASPD patients, and these potential biomarkers may be very helpful in understanding the pathomechanism of ASPD.

Classification and treatment of antisocial individuals: From behavior to biocognition

Antisocial behavior is a heterogeneous construct that can be divided into subtypes, such as antisocial personality and psychopathy. The adverse consequences of antisocial behavior produce great burden for the perpetrators, victims, family members, and for society at-large. The pervasiveness of antisocial behavior highlights the importance of precisely characterizing subtypes of antisocial individuals and identifying specific factors that are etiologically related to such behaviors to inform the development of targeted treatments. The goals of the current review are (1) to briefly summarize research on the operationalization and assessment of antisocial personality and psychopathy; (2) to provide an overview of several existing treatments with the potential to influence antisocial personality and psychopathy; and (3) to present an approach that integrates and uses biological and cognitive measures as starting points to more precisely characterize and treat these individuals. A focus on integrating factors at multiple levels of analysis can uncover person-specific characteristics and highlight potential targets for treatment to alleviate the burden caused by antisocial behavior.

Functional Neuroimaging in Psychopathy

BACKGROUND AND AIM

Psychopathy is associated with cognitive and affective deficits causing disruptive, harmful and selfish behaviour. These have considerable societal costs due to recurrent crime and property damage. A better understanding of the neurobiological bases of psychopathy could improve therapeutic interventions, reducing the related social costs. To analyse the major functional neural correlates of psychopathy, we reviewed functional neuroimaging studies conducted on persons with this condition.

METHODS

We searched the PubMed database for papers dealing with functional neuroimaging and psychopathy, with a specific focus on how neural functional changes may correlate with task performances and human behaviour.

RESULTS

Psychopathy-related behavioural disorders consistently correlated with dysfunctions in brain areas of the orbitofrontal-limbic (emotional processing and somatic reaction to emotions; behavioural planning and responsibility taking), anterior cingulate-orbitofrontal (correct assignment of emotional valence to social stimuli; violent/aggressive behaviour and challenging attitude) and prefrontal-temporal-limbic (emotional stimuli processing/response) networks. Dysfunctional areas more consistently included the inferior frontal, orbitofrontal, dorsolateral prefrontal, ventromedial prefrontal, temporal (mainly the superior temporal sulcus) and cingulated cortices, the insula, amygdala, ventral striatum and other basal ganglia.

CONCLUSIONS

Emotional processing and learning, and several social and affective decision-making functions are impaired in psychopathy, which correlates with specific changes in neural functions.

Aberrant functional brain connectome in people with antisocial personality disorder

Antisocial personality disorder (ASPD) is characterised by a disregard for social obligations and callous unconcern for the feelings of others. Studies have demonstrated that ASPD is associated with abnormalities in brain regions and aberrant functional connectivity. In this paper, topological organisation was examined in resting-state fMRI data obtained from 32 ASPD patients and 32 non-ASPD controls. The frequency-dependent functional networks were constructed using wavelet-based correlations over 90 brain regions. The topology of the functional networks of ASPD subjects was analysed via graph theoretical analysis. Furthermore, the abnormal functional connectivity was determined with a network-based statistic (NBS) approach. Our results revealed that, compared with the controls, the ASPD patients exhibited altered topological configuration of the functional connectome in the frequency interval of 0.016–0.031 Hz, as indicated by the increased clustering coefficient and decreased betweenness centrality in the medial superior frontal gyrus, precentral gyrus, Rolandic operculum, superior parietal gyrus, angular gyrus, and middle temporal pole. In addition, the ASPD patients showed increased functional connectivity mainly located in the default-mode network. The present study reveals an aberrant topological organisation of the functional brain network in individuals with ASPD. Our findings provide novel insight into the neuropathological mechanisms of ASPD.

Genetic and Neuroimaging Features of Personality Disorders: State of the Art

Personality disorders often act as a common denominator for many psychiatric problems, and studies on personality disorders contribute to the etiopathology, diagnosis, and treatment of many mental disorders. In recent years, increasing evidence from various studies has shown distinctive features of personality disorders, and that from genetic and neuroimaging studies has been especially valuable. Genetic studies primarily target the genes encoding neurotransmitters and enzymes in the serotoninergic and dopaminergic systems, and neuroimaging studies mainly focus on the frontal and temporal lobes as well as the limbic-paralimbic system in patients with personality disorders. Although some studies have suffered due to unclear diagnoses of personality disorders and some have included few patients for a given personality disorder, great opportunities remain for investigators to launch new ideas and technologies in the field.

Possible role of a dysregulation of the endogenous opioid system in antisocial personality disorder

Around half the inmates in prison institutions have antisocial personality disorder (ASPD). A recent theory has proposed that a dysfunction of the endogenous opioid system (EOS) underlies the neurobiology of borderline personality disorder (BPD). In the present theoretical paper, based on a comprehensive database and hand search of the relevant literature, this hypothesis is extended to ASPD, which may be the predominant expression of EOS dysfunction in men, while the same pathology underlies BPD in women. According to evidence from human and animal studies, the problematic behaviours of persons with antisocial, callous, or psychopathic traits may be seen as desperate, unconscious attempts to stimulate their deficient EOS, which plays a key role in brain reward circuits. If the needs of this system are not being met, the affected persons experience dysphoric mood, discomfort, or irritability, and strive to increase binding of endogenous opioids to receptors by using the rewarding effects of aggression by exertion of physical or manipulative power on others, by abusing alcohol or substances that have the reward system as target, by creating an "endorphin rush" by self-harm, by increasing the frequency of their sexual contacts, or by impulsive actions and sensation seeking. Symptoms associated with ASPD can be treated with opioid antagonists like naltrexone, naloxone, or nalmefene.

Structural and Functional Alterations in Right Dorsomedial Prefrontal and Left Insular Cortex Co-Localize in Adolescents with Aggressive Behaviour: An ALE Meta-Analysis

Recent neuroimaging work has suggested that aggressive behaviour (AB) is associated with structural and functional brain abnormalities in processes subserving emotion processing and regulation. However, most neuroimaging studies on AB to date only contain relatively small sample sizes. To objectively investigate the consistency of previous structural and functional research in adolescent AB, we performed a systematic literature review and two coordinate-based activation likelihood estimation meta-analyses on eight VBM and nine functional neuroimaging studies in a total of 783 participants (408 [224AB/184 controls] and 375 [215 AB/160 controls] for structural and functional analysis respectively). We found 19 structural and eight functional foci of significant alterations in adolescents with AB, mainly located within the emotion processing and regulation network (including orbitofrontal, dorsomedial prefrontal and limbic cortex). A subsequent conjunction analysis revealed that functional and structural alterations co-localize in right dorsomedial prefrontal cortex and left insula. Our results are in line with meta-analytic work as well as structural, functional and connectivity findings to date, all of which make a strong point for the involvement of a network of brain areas responsible for emotion processing and regulation, which is disrupted in AB. Increased knowledge about the behavioural and neuronal underpinnings of AB is crucial for the development of novel and implementation of existing treatment strategies. Longitudinal research studies will have to show whether the observed alterations are a result or primary cause of the phenotypic characteristics in AB.

Intelligence-related differences in the asymmetry of spontaneous cerebral activity

Recent evidence suggests the spontaneous BOLD signal synchronization of corresponding interhemispheric, homotopic regions as a stable trait of human brain physiology, with emerging differences in such organization being also related to some pathological conditions. To understand whether such brain functional symmetries play a role into higher-order cognitive functioning, here we correlated the functional homotopy profiles of 119 healthy subjects with their intelligence level. Counterintuitively, reduced homotopic connectivity in above average-IQ versus average-IQ subjects was observed, with significant reductions in visual and somatosensory cortices, supplementary motor area, rolandic operculum, and middle temporal gyrus, possibly suggesting that a downgrading of interhemispheric talk at rest could be associated with higher cognitive functioning. These regions also showed an increased spontaneous synchrony with medial structures located in ipsi- and contralateral hemispheres, with such pattern being mostly detectable for regions placed in the left hemisphere. The interactions with age and gender have been also tested, with different patterns for subjects above and below 25 years old and less homotopic connectivity in the prefrontal cortex and posterior midline regions in female participants with higher IQ scores. These findings support prior evidence suggesting a functional role for homotopic connectivity in human cognitive expression, promoting the reduction of synchrony between primary sensory regions as a predictor of higher intelligence levels.

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory

OBJECTIVE

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation.

METHOD

In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD.

RESULTS

Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%.

CONCLUSION

Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD.

SIGNIFICANCE

Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease.

Decoding the processing of lying using functional connectivity MRI

Background

Previous functional MRI (fMRI) studies have demonstrated group differences in brain activity between deceptive and honest responses. The functional connectivity network related to lie-telling remains largely uncharacterized.

Methods

In this study, we designed a lie-telling experiment that emphasized strategy devising. Thirty-two subjects underwent fMRI while responding to questions in a truthful, inverse, or deceitful manner. For each subject, whole-brain functional connectivity networks were constructed from correlations among brain regions for the lie-telling and truth-telling conditions. Then, a multivariate pattern analysis approach was used to distinguish lie-telling from truth-telling based on the functional connectivity networks.

Results

The classification results demonstrated that lie-telling could be differentiated from truth-telling with an accuracy of 82.81% (85.94% for lie-telling, 79.69% for truth-telling). The connectivities related to the fronto-parietal networks, cerebellum and cingulo-opercular networks are most discriminating, implying crucial roles for these three networks in the processing of deception.

Conclusions

The current study may shed new light on the neural pattern of deception from a functional integration viewpoint.

Electronic supplementary material

The online version of this article (doi:10.1186/s12993-014-0046-4) contains supplementary material, which is available to authorized users.

Mental health problems are associated with low-frequency fluctuations in reaction time in a large general population sample. The TRAILS study

BACKGROUND

Increased intra-subject reaction time variability (RT-ISV) as coarsely measured by the standard deviation (RT-SD) has been associated with many forms of psychopathology. Low-frequency RT fluctuations, which have been associated with intrinsic brain rhythms occurring approximately every 15-40s, have been shown to add unique information for ADHD. In this study, we investigated whether these fluctuations also relate to attentional problems in the general population, and contribute to the two major domains of psychopathology: externalizing and internalizing problems.

METHODS

RT was monitored throughout a self-paced sustained attention task (duration: 9.1 ± 1.2 min) in a Dutch population cohort of young adults (n=1455, mean age: 19.0 ± 0.6 years, 55.1% girls). To characterize temporal fluctuations in RT, we performed direct Fourier Transform on externally validated frequency bands based on frequency ranges of neuronal oscillations: Slow-5 (0.010-0.027 Hz), Slow-4 (0.027-0.073 Hz), and three additional higher frequency bands. Relative magnitude of Slow-4 fluctuations was the primary predictor in regression models for attentional, internalizing and externalizing problems (measured by the Adult Self-Report questionnaire). Additionally, stepwise regression models were created to investigate (a) whether Slow-4 significantly improved the prediction of problem behaviors beyond the RT-SD and (b) whether the other frequency bands provided important additional information.

RESULTS

The magnitude of Slow-4 fluctuations significantly predicted attentional and externalizing problems and even improved model fit after modeling RT-SD first (R(2) change=0.6%, P<.01). Subsequently, adding Slow-5 explained additional variance for externalizing problems (R(2) change=0.4%, P<.05). For internalizing problems, only RT-SD made a significant contribution to the regression model (R(2)=0.5%, P<.01), that is, none of the frequency bands provided additional information.

CONCLUSIONS

Low-frequency RT fluctuations have added predictive value for attentional and externalizing, but not internalizing problems beyond global differences in variability. This study extends previous findings in clinical samples of children with ADHD to adolescents from the general population and demonstrates that deconstructing RT-ISV into temporal components can provide more distinctive information for different domains of psychopathology.

Changes in low-frequency fluctuations in patients with antisocial personality disorder revealed by resting-state functional MRI

Antisocial Personality Disorder (APD) is a personality disorder that is most commonly associated with the legal and criminal justice systems. The study of the brain in APD has important implications in legal contexts and in helping ensure social stability. However, the neural contribution to the high prevalence of APD is still unclear. In this study, we used resting-state functional magnetic resonance imaging (fMRI) to investigate the underlying neural mechanisms of APD. Thirty-two healthy individuals and thirty-five patients with APD were recruited. The amplitude of low-frequency fluctuations (ALFF) was analyzed for the whole brain of all subjects. Our results showed that APD patients had a significant reduction in the ALFF in the right orbitofrontal cortex, the left temporal pole, the right inferior temporal gyrus, and the left cerebellum posterior lobe compared to normal controls. We observed that the right orbitofrontal cortex had a negative correlation between ALFF values and MMPI psychopathic deviate scores. Alterations in ALFF in these specific brain regions suggest that APD patients may be associated with abnormal activities in the fronto-temporal network. We propose that our results may contribute in a clinical and forensic context to a better understanding of APD.