Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis

Scientific psychiatry within technical reach

This is an invited commentary on the paper by Zou et al, accepted for publication in World Journal of Psychiatry. It reflects the findings of the authors in the broader context of the search for scientifically sound and evidence based nomothetic system for diagnosis and treatment in psychiatry, with a special focus on the application of translational neuroimaging in that effort.

Diagnostic value of structural, functional and effective connectivity in bipolar disorder

INTRODUCTION

The aim of this systematic review is to assess the functional magnetic resonance imaging (fMRI) studies of bipolar disorder (BD) patients that characterize differences in terms of structural, functional, and effective connectivity between the patients with BD, patients with other psychiatric disorders and healthy controls as possible biomarkers for diagnosing the disorder using neuroimaging.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), guidelines a systematic search for recent (since 2015) original studies on connectivity in bipolar disorder was conducted in PUBMED and SCOPUS.

RESULTS

A total of 60 studies were included in this systematic review: 20 of the structural connectivity, 33 of the functional connectivity, and only 7 of the studies focused on effective connectivity complied with the inclusion and exclusion criteria.

DISCUSSION

Despite the great heterogeneity in the findings, there are several trends that emerge. In structural connectivity studies, the main abnormalities in bipolar disorder patients were in the frontal gyrus, anterior, as well as posterior cingulate cortex and differences in emotion and reward-related networks. Cerebellum (vermis) to cerebrum functional connectivity was found to be the most common finding in BD. Moreover, prefrontal cortex and amygdala connectivity as part of the rich-club hubs were often reported to be disrupted. The most common findings based on effective connectivity were alterations in salience network, default mode network and executive control network. Although more studies with larger sample sizes are needed to ascertain altered brain connectivity as diagnostic biomarker, there is a perspective that the method could be used as a single marker of diagnosis in the future, and the process of adoption could be accelerated by using approaches such as semiunsupervised machine learning.

Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

BACKGROUND

Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive, affective and behavioral tasks, adapted for the functional magnetic resonance imaging (MRI) (fMRI) experimental environment. There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.

AIM

To investigate whether there exist specific neural circuits which underpin differential item responses to depressive, paranoid and neutral items (DN) in patients respectively with schizophrenia (SCZ) and major depressive disorder (MDD).

METHODS

60 patients were recruited with SCZ and MDD. All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm, comprised of block design, including blocks with items from diagnostic paranoid (DP), depression specific (DS) and DN from general interest scale. We performed a two-sample t-test between the two groups-SCZ patients and depressive patients. Our purpose was to observe different brain networks which were activated during a specific condition of the task, respectively DS, DP, DN.

RESULTS

Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task. We identified one component that is task-related and independent of condition (shared between all three conditions), composed by regions within the temporal (right superior and middle temporal gyri), frontal (left middle and inferior frontal gyri) and limbic/salience system (right anterior insula). Another component is related to both diagnostic specific conditions (DS and DP) e.g. It is shared between DEP and SCZ, and includes frontal motor/language and parietal areas. One specific component is modulated preferentially by to the DP condition, and is related mainly to prefrontal regions, whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus, several occipital areas, including lingual and fusiform gyrus, as well as parahippocampal gyrus. Finally, component 12 appeared to be unique for the neutral condition. In addition, there have been determined circuits across components, which are either common, or distinct in the preferential processing of the sub-scales of the task.

CONCLUSION

This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

Effective Connectivity Between the Orbitofrontal Cortex and the Precuneus Differentiates Major Psychiatric Disorders: Results from a Transdiagnostic Spectral DCM Study

Translational psychiatry has been a hot topic in neurosciences research. The authors present a commentary on the relevant findings from a transdiagnostic study applicable to clinic practice. Additional discussion on conceptual and clinical insight into this current broad line of research is explored in the integration of multi-level paradigm in Psychiatry research.

Perspectives before incremental trans-disciplinary cross-validation of clinical self-evaluation tools and functional MRI in psychiatry: 10 years later

Translational validity (or trans-disciplinary validity) is defined as one possible approach to achieving incremental validity by combining simultaneous clinical state-dependent measures and functional MRI data acquisition. It is designed under the assumption that the simultaneous administration of the two methods may produce a dataset with enhanced synchronization and concordance. Translational validation aims at "bridging" the explanatory gap by implementing validated psychometric tools clinically in the experimental settings of fMRI and then translating them back to clinical utility. Our studies may have identified common diagnostic task-specific denominators in terms of activations and network modulation. However, those common denominators need further investigation to determine whether they signify disease or syndrome-specific features (signatures), which, at the end of the day, raises one more question about the poverty of current conventional psychiatric classification criteria. We propose herewith a novel algorithm for translational validation based on our explorative findings. The algorithm itself includes pre-selection of a test based on its psychometric characteristics, adaptation to the functional MRI paradigm, exploration of the underpinning whole brain neural correlates in healthy controls as compared to a patient population with certain diagnoses, and finally, investigation of the differences between two or more diagnostic classes.

The tryptophan catabolite or kynurenine pathway in schizophrenia: meta-analysis reveals dissociations between central, serum, and plasma compartments

The tryptophan catabolite (TRYCAT) pathway is implicated in the pathophysiology of schizophrenia (SCZ) since the rate-limiting enzyme indoleamine-dioxygenase (IDO) may be induced by inflammatory and oxidative stress mediators. This systematic review searched PubMed, Web of Science, and Google Scholar for papers published from inception until August 2021 and meta-analyzed the association between SCZ and TRYCATs in the central nervous system (CNS) and peripheral blood. We included 61 studies comprising 2813 patients and 2948 healthy controls. In the CNS we found a significant (p < 0.001) increase in the kynurenine/tryptophan (KYN/TRP) (standardized mean difference, SMD = 0.769, 95% confidence interval, CI: 0.456; 1.082) and kynurenic acid (KA)/KYN + TRP (SMD = 0.697, CI: 0.478-0.917) ratios, KA (SMD = 0.646, CI: 0.422; 0.909) and KYN (SMD = 1.238; CI: 0.590; 1.886), while the 3OH-kynurenine (3HK) + KYN-3-monooxygenase (KMO)/KYN ratio was significantly reduced (SMD = -1.089, CI: -1.682; -0.496). There were significant differences between KYN/TRP, (KYN + KA)/TRP, (3HK + KMO)/KYN, KA, and KYN levels among the CNS and peripheral blood, and among serum and plasma KYN. The only useful peripheral marker of CNS TRYCATs findings was the increased KYN/TRP ratio in serum (SMD = 0.211, CI: 0.056; 0.366, p = 0.007), but not in plasma. There was no significant increase in a neurotoxic composite score based on KYN, 3HK, and picolinic, xanthurenic, and quinolinic acid. SCZ is accompanied by increased IDO activity in the CNS and serum, and reduced KMO activity and a shift towards KA production in the CNS. This CNS TRYCATs profile indicates neuroprotective, negative immunoregulatory and anti-inflammatory effects. Peripheral blood levels of TRYCATs are dissociated from CNS findings except for a modest increase in serum IDO activity.

Depressive symptoms due to stroke are strongly predicted by the volume and location of the cerebral infarction, white matter hyperintensities, hypertension, and age: A precision nomothetic psychiatry analysis

OBJECTIVES

To delineate the effects of white matter hyperintensities (WMHs) as measured by Fluid-attenuated inversion recovery (FLAIR) and infarction volume as measured by Diffusion-weighted imaging (DWI) on post-stroke depression symptoms.

METHODS

Baseline National Institutes of Health Stroke Score (NIHSS) and Modified Rankin Scale (mRS) scores, and FLAIR and DWI MRIs to assess WMHs and acute infarct volumes, respectively, were assessed in 47 patients (≥55 years) with acute ischemic stroke and 17 normal controls. The Montgomery-Åsberg Depression Rating Scale (MDRS) was assessed three months after the stroke.

RESULTS

The MADRS score was significantly increased in stroke patients as compared with normal controls. The MADRS scale is not unidimensional and cannot be used as an accurate indicator of depression severity in stroke patients. Three months after stroke, key depressive (sadness and inability to feel) and concentration-tension symptoms, and lassitude are significantly predicted by the infarct volume. Right side infarction strongly predicts key depressive symptoms and left side infarction strongly predicts concentration-tension and lassitude scores. Total WMHs significantly predict key depressive and concentration-tension symptoms, and lassitude, with these effects being mediated by right and left DWI stroke volumes and associated disabilities.

CONCLUSIONS

Interactions between age, hypertension, a chronic atherosclerotic process, and acute stroke account for the onset of key depressive symptoms three months after the acute infarct. Chronic and acute neuro-immune and neuro-oxidative stress pathways associated with the formation of WMHs and acute stroke may explain the incidence of post-stroke key depressive and concentration-tension symptoms, and lassitude.

Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression

We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry.

Identification of violent patients with schizophrenia using a hybrid machine learning approach at the individual level

Despite numerous risk factors associated with violence in patients with schizophrenia, predicting and preventing violent behavior is still a challenge. At present, machine learning (ML) has become a promising strategy for guiding individualized assessment. To build an effective model to predict the risk of violence in patients with schizophrenia, we proposed a hybrid ML method to improve the prediction capability in 42 violent offenders with schizophrenia and 33 non-violent patients with schizophrenia. The results revealed that the final model, which combined multimodal data, achieved the highest prediction performance with an accuracy of 90.67%. Specifically, the model, which fused three modalities of neuroimaging data, achieved a better accuracy than other fused models. In addition, the msot discriminative neuroimaging features involved in the prefrontal-temporal cognitive circuit and striatum reward system, indicating that dysfunction in cortical-subcortical circuits might be associated with high risk of violence in patients with schizophrenia. This study provides the first evidence supporting that the combination of specific multimodal neuroimaging and clinical data in ML analysis can effectively identify violent patients with schizophrenia. Furthermore, this work is crucial for the development of neuro-prediction models that could facilitate individualized treatment and interventions for violent behaviors in patients with schizophrenia.

Precision Psychiatry: Machine learning as a tool to find new pharmacological targets

There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs.

OBJECTIVE

The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets.

METHODS

Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review.

RESULTS

The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents.

CONCLUSION

Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

Pharmaco-Magnetic Resonance as a Tool for Monitoring the Medication-Related Effects in the Brain May Provide Potential Biomarkers for Psychotic Disorders

The neurodegenerative and neurodevelopmental hypotheses represent the basic etiological framework for the origin of schizophrenia. Additionally, the dopamine hypothesis, adopted more than two decades ago, has repeatedly asserted the position of dopamine as a pathobiochemical substrate through the action of psychostimulants and neuroleptics on the mesolimbic and mesocortical systems, giving insight into the origin of positive and negative schizophrenic symptoms. Meanwhile, cognitive impairments in schizophrenia remain incompletely understood but are thought to be present during all stages of the disease, as well as in the prodromal, interictal and residual phases. On the other hand, observations on the effects of NMDA antagonists, such as ketamine and phencyclidine, reveal that hypoglutamatergic neurotransmission causes not only positive and negative but also cognitive schizophrenic symptoms. This review aims to summarize the different hypotheses about the origin of psychoses and to identify the optimal neuroimaging method that can serve to unite them in an integral etiological framework. We systematically searched Google scholar (with no concern to the date published) to identify studies investigating the etiology of schizophrenia, with a focus on impaired central neurotransmission. The complex interaction between the dopamine and glutamate neurotransmitter systems provides the long-needed etiological concept, which combines the neurodegenerative hypothesis with the hypothesis of impaired neurodevelopment in schizophrenia. Pharmaco-magnetic resonance imaging is a neuroimaging method that can provide a translation of scientific knowledge about the neural networks and the disruptions in and between different brain regions, into clinically applicable and effective therapeutic results in the management of severe psychotic disorders.

Examining early structural and functional brain alterations in postpartum depression through multimodal neuroimaging

Postpartum depression (PPD) affects approximately 1 in 10 women after childbirth. A thorough understanding of a preexisting vulnerability to PPD will likely aid the early detection and treatment of PPD. Using a within-sample association, the study examined whether the brain's structural and functional alterations predict the onset of depression. 157 euthymic postpartum women were subjected to a multimodal MRI scan within the first 6 days of childbirth and were followed up for 12 weeks. Based on a clinical interview 12 weeks postpartum, participants were classified as mentally healthy or having either PPD or adjustment disorder (AD). Voxel-based morphometry and resting-state functional connectivity comparisons were performed between the three groups. 13.4% of women in our study developed PPD (n = 21) and 12.1% (n = 19) adjustment disorder (AD). The risk factors for PPD were a psychiatric history and the experience and severity of baby blues and the history of premenstrual syndrome. Despite the different risk profiles, no differences between the PPD, AD and control group were apparent based on structural and functional neuroimaging data immediately after childbirth. At 12 weeks postpartum, a significant association was observed between Integrated Local Correlation (LCor) and the Edinburgh Postnatal Depression Score (EPDS). Our findings do not support the notion that the brain's structural and resting-state functional alterations, if present, can be used as an early biomarker of PPD or AD. However, effects may become apparent if continuous measures of symptom severity are chosen.

Implications from translational cross-validation of clinical assessment tools for diagnosis and treatment in psychiatry

Traditional therapeutic methods in psychiatry, such as psychopharmacology and psychotherapy help many people suffering from mental disorders, but in the long-term prove to be effective in a relatively small proportion of those affected. Therapeutically, resistant forms of mental disorders such as schizophrenia, major depressive disorder, and bipolar disorder lead to persistent distress and dysfunction in personal, social, and professional aspects. In an effort to address these problems, the translational approach in neuroscience has initiated the inclusion of novel or modified unconventional diagnostic and therapeutic techniques with promising results. For instance, neuroimaging data sets from multiple modalities provide insight into the nature of pathophysiological mechanisms such as disruptions of connectivity, integration, and segregation of neural networks, focusing on the treatment of mental disorders through instrumental biomedical methods such as electro-convulsive therapy (ECT), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS) and deep brain stimulation (DBS). These methodologies have yielded promising results that have yet to be understood and improved to enhance the prognosis of the severe and persistent psychotic and affective disorders. The current review is focused on the translational approach in the management of schizophrenia and mood disorders, as well as the adaptation of new transdisciplinary diagnostic tools such as neuroimaging with concurrently administered psychopathological questionnaires and integration of the results into the therapeutic framework using various advanced instrumental biomedical tools such as ECT, TMS, tDCS and DBS.