Clinical Strategies and Technical Challenges in Psychoradiology

Brain structural connectomic topology predicts medication response in youth with bipolar disorder: A randomized clinical trial

BACKGROUND

Response to pharmacotherapy varies considerably among youths with bipolar disorder (BD) and is poorly predicted by clinical or demographic features. It can take several weeks to determine whether medication for BD is clinically effective. Although neuroimaging biomarkers are promising predictors, few studies examined the predictive value of the brain connectomic topology.

METHODS

BD-I youth (N = 121) with no prior psychopharmacotherapy were randomized to 6-weeks of double-blind quetiapine or lithium. Structural magnetic resonance imaging (MRI) was performed before medication and at one week after medication initiation. Brain structural connectome was established from the MRI scans, and topological metrics were calculated for each patient. Deep learning-based prediction model was built using baseline and one-week connectome topology to predict medication response at week 6.

RESULTS

Both baseline topological metrics and one-week topological changes could predict treatment response with significant accuracy (73.8 % - 86.8 %). A longitudinally joint model combining baseline and one-week topology provided the highest accuracy (91.3 %). The transferability between models for quetiapine and lithium was relatively poor. In addition, predictions for the two drugs were driven by similar baseline but distinct one-week salient topological patterns.

LIMITATIONS

Independent replication is needed to validate our preliminary findings.

CONCLUSION

Brain structural connectomic topology at baseline and its acute changes within the first week enable accurate BD medication response prediction. The most contributive brain regions differed between prediction models for quetiapine and lithium after one week. These findings provide preliminary evidence for the development of neuroimaging-based biomarkers for guiding therapeutic interventions in youth with BD.

Altered local spontaneous activity and functional connectivity density in major depressive disorder patients with anhedonia

OBJECTIVE

The purpose of this study was to investigate the alterations of intrinsic brain function in major depressive disorder (MDD) patients with and without anhedonia based on whole-brain level by using two novel measures, four-dimensional spatial-temporal consistency of local neural activity (FOCA) and local functional connectivity density (lFCD).

METHODS

A total of 26 MDD patients with anhedonia (MDD-WA), 29 MDD patients without anhedonia (MDD-WoA), and 30 healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning and intrinsic brain function was explored by FOCA and lFCD. A two-sample t-test was conducted to explore FOCA and lFCD differences between MDD patients and HCs, then analysis of covariance (ANCOVA) and post hoc tests were performed to obtain brain regions with significant differences among three groups. Finally, the diagnostic performance of FOCA and lFCD values with significant inter-group difference was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

Compared to HCs, MDD patients showed decreased FOCA in the right cuneus (CUN) and left postcentral gyrus (PoCG), as well as diminished lFCD in the right CUN and left calcarine fissure and surrounding cortex (CAL). Interestingly, the MDD-WA group was more likely to exhibit decreased FOCA in the left PoCG and reduced lFCD in the left CAL after consideration for the effect of anhedonia in MDD patients. The MDD-WA group further showed increased FOCA in the bilateral caudate (CAU) and right ventral anterior nucleus (VA) when comparing to HCs. Additionally, as compared with MDD-WoA group, the MDD-WA group presented decreased FOCA in the right middle occipital gyrus (MOG), which was also negatively associated with the severity of anhedonia in MDD patients. Finally, FOCA values of the right MOG exhibited excellent discriminant validity in differentiating MDD-WA from MDD-WoA, and the other individual or combined indices of FOCA or lFCD values in the aforementioned distinct brain regions presented significant utility in distinguishing between MDD or MDD-WA and HCs.

CONCLUSIONS

The present findings suggest that aberrant intrinsic brain function in the left CAL, left PoCG, bilateral CAU, right VA, and, especially the right MOG may be associated with anhedonia in patients with MDD. Altered FOCA in the right MOG may have the potential to be a diagnostic neuroimaging biomarker for MDD patients with anhedonia.

From periphery immunity to central domain through clinical interview as a new insight on schizophrenia

Identifying disease predictors through advanced statistical models enables the discovery of treatment targets for schizophrenia. In this study, a multifaceted clinical and laboratory analysis was conducted, incorporating magnetic resonance spectroscopy with immunology markers, psychiatric scores, and biochemical data, on a cohort of 45 patients diagnosed with schizophrenia and 51 healthy controls. The aim was to delineate predictive markers for diagnosing schizophrenia. A logistic regression model was used, as utilized to analyze the impact of multivariate variables on the prevalence of schizophrenia. Utilization of a stepwise algorithm yielded a final model, optimized using Akaike's information criterion and a logit link function, which incorporated eight predictors (White Blood Cells, Reactive Lymphocytes, Red Blood Cells, Glucose, Insulin, Beck Depression score, Brain Taurine, Creatine and Phosphocreatine concentration). No single factor can reliably differentiate between healthy patients and those with schizophrenia. Therefore, it is valuable to simultaneously consider the values of multiple factors and classify patients using a multivariate model.

18F-FDG-PET Radiomics Based on White Matter Predicts The Progression of Mild Cognitive Impairment to Alzheimer Disease: A Machine Learning Study

RATIONALE AND OBJECTIVES

To build a model using white-matter radiomics features on positron-emission tomography (PET) and machine learning methods to predict progression from mild cognitive impairment (MCI) to Alzheimer disease (AD).

MATERIALS AND METHODS

We analyzed the data of 341 MCI patients from the Alzheimer's Disease Neuroimaging Initiative, of whom 102 progressed to AD during an 8-year follow-up. The patients were divided into the training (238 patients) and test groups (103 patients). PET-based radiomics features were extracted from the white matter in the training group, and dimensionally reduced to construct a psychoradiomics signature (PS), which was combined with multimodal data using machine learning methods to construct an integrated model. Model performance was evaluated using receiver operating characteristic curves in the test group.

RESULTS

Clinical Dementia Rating (CDR) scores, Alzheimer's Disease Assessment Scale (ADAS) scores, and PS independently predicted MCI progression to AD on multivariate logistic regression. The areas under the curve (AUCs) of the CDR, ADAS and PS in the training and test groups were 0.683, 0.755, 0.747 and 0.737, 0.743, 0.719 respectively, and were combined using a support vector machine to construct an integrated model. The AUC of the integrated model in the training and test groups was 0.868 and 0.865, respectively (sensitivity, 0.873 and 0.839, respectively; specificity, 0.784 and 0.806, respectively). The AUCs of the integrated model significantly differed from those of other predictors in both groups (p < 0.05, Delong test).

CONCLUSION

Our psych radiomics signature based on white-matter PET data predicted MCI progression to AD. The integrated model built using multimodal data and machine learning identified MCI patients at a high risk of progression to AD.

Recent advances in psychoradiology

Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brainin vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.

Robustness of radiomics to variations in segmentation methods in multimodal brain MRI

Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77–0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3–0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods.

BIOMARKERS AND NEUROBEHAVIORAL DIAGNOSIS

Our current diagnostic methods for treatment planning in Psychiatry and Neurodevelopmental Disabilities leave room for improvement, and null results in clinical trials in these fields may be a result of insufficient tools for patient stratification. Great hope has been placed in novel technologies to improve clinical and trial outcomes, but we have yet to see a substantial change in clinical practice. As we examine attempts at biomarker validation within these fields, we find that it may be the diagnoses themselves that fall short. We now need to improve neuropsychiatric nosologies with a focus on validity based not solely on behavioral features, but on a synthesis that includes genetic and biological data as well. The eventual goal is diagnostic biomarkers and diagnoses themselves based on distinct mechanisms, but such an understanding of the causal relationship across levels of analysis is likely to be elusive for some time. Rather, we propose an approach in the near-term that deconstructs diagnosis into a series of independent, empiric and clinically relevant associations among a single, defined patient group, a single biomarker, a single intervention and a single clinical outcome. Incremental study across patient groups, interventions, outcomes and modalities will lead to a more interdigitated network of knowledge, and correlations in metrics across levels of analysis will eventually give way to the causal understanding that will allow for mechanistically based diagnoses.

Prenatal origins of neuropsychiatric diseases

AIM

The main objective is to review the available evidence in the literature for developmental origins of neuropsychiatric diseases and their underlying mechanisms. We also probe emerging cutting-edge prenatal MR imaging tools and their future role in advancing our understanding the prenatal footprints of neuropsychiatric disorders.

OBSERVATIONS

Both human and animal studies support early intrauterine origins of neuropsychiatric disease, particularly autism spectrum disorders (ASD), attention and hyperactivity disorders, schizophrenia, depression, anxiety and mood disorders. Specific mechanisms of intrauterine injury include infection, inflammation, hypoxia, hypoperfusion, ischaemia polysubstance use/abuse, maternal mental health and placental dysfunction.

CONCLUSIONS AND RELEVANCE

There is ample evidence to suggest developmental vulnerability of the foetal brain to intrauterine exposures that increases and individual's risk for neuropsychiatric disease, especially the risk of ASD, depression and anxiety. Elucidating the exact timing and mechanisms of injury can be difficult and require novel, non-invasive approaches to the study emerging structural and functional brain development of the foetus. Clinical care should both emphasise maternal health during pregnancy, as well as close, continued monitoring for at risk offspring throughout young adulthood for the early identification and treatment of neuropsychiatric diseases.

Artificial intelligence applications in psychoradiology

One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness, prediction of prognosis before treatment, and guidance for selection of effective treatments that target patient-relevant pathophysiological features. This is the primary aim of the field of Psychoradiology. Using databases collected from large samples at multiple centers, sophisticated artificial intelligence (AI) algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose, predict, and make treatment decisions. In this review, we selectively summarize psychoradiological research using magnetic resonance imaging of the brain to explore the neural mechanism of psychiatric disorders, and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders, as well as limitations in the application of AI that should be considered in future translational research.

Left superior temporal sulcus morphometry mediates the impact of anxiety and depressive symptoms on sleep quality in healthy adults

Anxiety and depressive symptoms may predispose individuals to sleep disturbance. Understanding how these emotional symptoms affect sleep quality, especially the underlying neural basis, could support the development of effective treatment. The aims of the present study were therefore to investigate potential changes in brain morphometry associated with poor sleep quality and whether this structure played a mediating role between the emotional symptoms and sleep quality. One hundred and forty-one healthy adults (69 women, mean age = 26.06 years, SD = 6.36 years) were recruited. A structural magnetic resonance imaging investigation was performed, and self-reported measures of anxiety, depressive symptoms and sleep quality were obtained for each participant. Whole-brain regression analysis revealed that worse sleep quality was associated with thinner cortex in left superior temporal sulcus (STS). Furthermore, the thickness of left STS mediated the association between the emotional symptoms and sleep quality. A subsequent commonality analysis showed that physiological component of the depressive symptoms had the greatest influence on sleep quality. In conclusion, thinner cortex in left STS may represent a neural substrate for the association between anxiety and depressive symptoms and poor sleep quality and may thus serve as a potential target for neuromodulatory treatment of sleep problems.

Brain gray matter structures associated with trait impulsivity: A systematic review and voxel-based meta-analysis

Trait impulsivity is a multifaceted personality characteristic that contributes to maladaptive life outcomes. Although a growing body of neuroimaging studies have investigated the structural correlates of trait impulsivity, the findings remain highly inconsistent and heterogeneous. Herein, we performed a systematic review to depict an integrated delineation of gray matter (GM) substrates of trait impulsivity and a meta-analysis to examine concurrence across previous whole-brain voxel-based morphometry studies. The systematic review summarized the diverse findings in GM morphometry in the past literature, and the quantitative meta-analysis revealed impulsivity-related volumetric GM alterations in prefrontal, temporal, and parietal cortices. In addition, we identified the modulatory effects of age and gender in impulsivity-GM volume associations. The present study advances understanding of brain GM morphometry features underlying trait impulsivity. The findings may have practical implications in the clinical diagnosis of and intervention for impulsivity-related disorders.

Effect of experimental orthodontic pain on gray and white matter functional connectivity

AIM

Over 90% of patients receiving orthodontic treatment experience clinically significant pain. However, little is known about the neural correlates of orthodontic pain and which has therefore been investigated in the present study of healthy subjects using an experimental paradigm.

METHODS

Resting-state functional magnetic resonance imaging (rsfMRI) was performed in 44 healthy subjects 24 hours after an elastic separator had been introduced between the first and the second molar on the right side of the lower jaw and in 49 age- and sex-matched healthy control (HC) subjects. A K-means clustering algorithm was used to identify functional gray matter (GM) and white matter (WM) resting-state networks, and differences in functional connectivity (FC) of GM and WM between the group of subjects with experimental orthodontic pain and HC were analyzed.

RESULTS

Twelve GM networks and 14 WM networks with high stability were identified. Compared with HC, subjects with orthodontic pain showed significantly increased FC between WM12, which includes posterior thalamic radiation and posterior cingulum bundle, and most GM networks. Besides, the WM12 network showed significant differences in FC with three GM-WM loops involving the default mode network, dorsal attention network, and salience network, respectively.

CONCLUSIONS

Orthodontic pain is shown to produce an alteration of FC in networks relevant to pain processing, which may be mediated by a WM network relevant to emotion perception and cognitive processing.

Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies

Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.

Neurostructural correlates of hope: dispositional hope mediates the impact of the SMA gray matter volume on subjective well-being in late adolescence

There has been increasing interest in identifying factors to predict subjective well-being in the emerging field of positive psychology over the past two decades. Dispositional hope, which reflects one's goal-directed tendencies, including both pathway thinking (planning to meet goals) and agency thinking (goal-directed determination), has emerged as a stable predictor for subjective well-being. However, the neurobiological substrates of dispositional hope and the brain-hope mechanism for predicting subjective well-being remain unclear. Here, we examined these issues in 231 high school graduates within the same grade by estimating cortical gray matter volume (GMV) utilizing a voxel-based morphometry method based on structural magnetic resonance imaging. Whole-brain regression analyses and prediction analyses showed that higher dispositional hope was stably associated with greater GMV in the left supplementary motor area (SMA). Furthermore, mediation analyses revealed that dispositional hope mediated the relation between left SMA volume and subjective well-being. Critically, our results were obtained after adjusting for age, sex, family socioeconomic status and total GMV. Altogether, our study presents novel evidence for the neuroanatomical basis of dispositional hope and suggests an underlying indirect effect of dispositional hope on the link between brain gray matter structure and subjective well-being.

Neurostructural correlates of optimism: Gray matter density in the putamen predicts dispositional optimism in late adolescence

Dispositional optimism reflects one's generalized positive expectancies for future outcomes and plays a crucial role in personal developmental outcomes and health (e.g., counteracting related mental disorders such as depression and anxiety). Increasing evidence has suggested that extraversion is an important personality factor contributing to dispositional optimism. However, less is known about the association between dispositional optimism and brain structure and the role of extraversion in this association. Here, we examined these issues in 231 healthy high school students aged 16 to 20 years (110 males, mean age = 18.48 years, SD = 0.54) by estimating regional gray matter density (rGMD) using a voxel-based morphometry method via structural magnetic resonance imaging. Whole-brain regression analyses revealed a significant positive correlation between dispositional optimism and the rGMD of the bilateral putamen after adjusting for age, sex, family socioeconomic status (SES), general intelligence, and total gray matter volume (TGMV). Moreover, prediction analyses using fourfold balanced cross-validation combined with linear regression confirmed a significant connection between dispositional optimism and putamen density after adjusting for age, sex, and family SES. More importantly, subsequent mediation analysis showed that extraversion may account for the association between putamen density and dispositional optimism after adjusting for age, sex, family SES, general intelligence, TGMV, and the other four Big Five personality traits. Taken together, the current study provides new evidence regarding the neurostructural basis underlying dispositional optimism in adolescents and underscores the importance of extraversion as an essential personality factor for dispositional optimism acquisition.