Seeing the bigger picture: multimodal neuroimaging to investigate neuropsychiatric illnesses

Anhedonia Across and Beyond the Schizophrenia Spectrum

Anhedonia refers to the diminished ability to experience pleasure, and is a core feature of schizophrenia (SCZ). The neurocognitive and neural correlates of anhedonia remain elusive. Based on several influential theoretical models for negative symptoms, this selective review proposed four important neurocognitive domains, which may unveil the neurobiological mechanisms of anhedonia. The authors critically reviewed the current evidence regarding value representation of reward, prospection, emotion-behavior decoupling, and belief updating in the Chinese setting, covering both behavioral and neuroimaging research. We observed a limited application of the transdiagnostic approach in previous studies on the four domains, and the lack of adequate measures to tap into the expressivity deficit in SCZ. Despite many behavioral paradigms for these four domains utilized both social and non-social stimuli, previous studies seldom focused on the social-versus-non-social differentiation. We further advocated several important directions for future research.

Neuropsychology and Neurobiology of Negative Schizotypy: A Selective Review

Schizotypy refers to a latent personality organization that reflects liability to schizophrenia. Because schizotypy is a multidimensional construct, people with schizotypy vary in behavioral and neurobiological features. In this article, we selectively review the neuropsychological and neurobiological profiles of people with schizotypy, with a focus on negative schizotypy. Empirical evidence is presented for alterations of neuropsychological performance in negative schizotypy. We also cover the Research Domain Criteria domains of positive valence, social process, and sensorimotor systems. Moreover, we systematically summarize the neurobiological correlates of negative schizotypy at the structural, resting-state, and task-based neural levels, as well as the neurochemical level. The convergence and inconsistency of the evidence are critically reviewed. Regarding theoretical and clinical implications, we argue that negative schizotypy represents a useful organizational framework for studying neuropsychology and neurobiology across different psychiatric disorders.

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Brain networks in eating disorders: a systematic review of graph theory studies

PURPOSE

Recent evidence from neuroimaging research has shown that eating disorders (EDs) are characterized by alterations in interconnected neural systems, whose characteristics can be usefully described by connectomics tools. The present paper aimed to review the neuroimaging literature in EDs employing connectomic tools, and, specifically, graph theory analysis.

METHODS

A systematic review of the literature was conducted to identify studies employing graph theory analysis on patients with eating disorders published before the 22nd of June 2020.

RESULTS

Twelve studies were included in the systematic review. Ten of them address anorexia nervosa (AN) (AN = 199; acute AN = 85, weight recovered AN with acute diagnosis = 24; fully recovered AN = 90). The remaining two articles address patients with bulimia nervosa (BN) (BN = 48). Global and regional unbalance in segregation and integration properties were described in both disorders.

DISCUSSION

The literature concerning the use of connectomics tools in EDs evidenced the presence of alterations in the topological characteristics of brain networks at a global and at a regional level. Changes in local characteristics involve areas that have been demonstrated to be crucial in the neurobiology and pathophysiology of EDs. Regional imbalances in network properties seem to reflect on global patterns.

LEVEL OF EVIDENCE

Level I, systematic review.

A multimodal IVA fusion approach to identify linked neuroimaging markers

In this study, we introduce a method to perform independent vector analysis (IVA) fusion to estimate linked independent sources and apply to a large multimodal dataset of over 3000 subjects in the UK Biobank study, including structural (gray matter), diffusion (fractional anisotropy), and functional (amplitude of low frequency fluctuations) magnetic resonance imaging data from each subject. The approach reveals a number of linked sources showing significant and meaningful covariation with subject phenotypes. One such mode shows significant linear association with age across all three modalities. Robust age-associated reductions in gray matter density were observed in thalamus, caudate, and insular regions, as well as visual and cingulate regions, with covarying reductions of fractional anisotropy in the periventricular region, in addition to reductions in amplitude of low frequency fluctuations in visual and parietal regions. Another mode identified multimodal patterns that differentiated subjects in their time-to-recall during a prospective memory test. In sum, the proposed IVA-based approach provides a flexible, interpretable, and powerful approach for revealing links between multimodal neuroimaging data.