Autobiographical memory and default mode network function in schizophrenia: an fMRI study

Disturbance at the self-other boundary in schizophrenia: Linking phenomenology to clinical neuroscience

In this selective review, we describe the current neuroscientific literature on disturbances of the self-other boundary in schizophrenia as they relate to structural and experiential aspects of the self. Within these two broad categories, the structural self includes body ownership and agency, and the experiential self includes self-reflection, source monitoring, and self-referential and autobiographical memory. Further, we consider how disturbances in these domains link to the phenomenology of schizophrenia. We identify faulty internal predictive coding as a potential mechanism of disturbance in body ownership and agency, which results in susceptibility to bias (over- or under-attributing outcomes to one's own actions or intentions). This is reflected in reduced activity in the temporoparietal junction (TPJ), a heteromodal association area implicated in several aspects of self-other processing, as well as reduced fronto-parietal functional connectivity. Deficits of the experiential self in schizophrenia may stem from a lack of salience of self-related information, whereby the mental representation of self is not as rich as in healthy controls and therefore does not result in the same level of privileged processing. As a result, memory for self-referential material and autobiographical memory processes is impaired, which hinders creation of a cohesive life narrative. Impairments of the experiential self implicate abnormal activation patterns along the cortical midline, including medial prefrontal cortex and posterior cingulate/precuneus, as well as TPJ. In fact, TPJ appears to be involved in all the reviewed aspects of the self-other disturbance. We conclude with suggestions for future work, including implications for interventions with critical timing considerations.

Abnormal functional connectivity strength in age-related macular degeneration patients: a fMRI study

OBJECTIVE

Age-related macular degeneration (AMD) is a serious blinding eye disease. Previous neuroimaging studies reported that AMD were accompanied by abnormalities of the brain. However, whether AMD patients were associated with functional connectivity strength (FCS) or not remains unknown. In our study, the purpose of the study was to assess FCS changes in AMD patients.

METHODS

In our study, 20 AMD patients and 20 healthy controls (HCs), matched closely by sex, age, and educational level were underwent MRI scanning. FCS method and seed-based functional connectivity (FC) method were applied to investigate the functional network changes between two groups. Moreover, support vector machine (SVM) method was applied to assess the FCS maps as a feature to classification of AMD diseases.

RESULTS

Our study reported that AMD patients showed decreased FCS values in the bilateral calcarine, left supplementary motor area, left superior parietal lobule and left paracentral lobule (ParaL) relative to the HC group. Meanwhile, our study found that the AMD patients showed abnormal FC within visual network, sensorimotor network and default mode network. Moreover, the SVM method showed that FCS maps as machine learning features shows good classification efficiency (area under curve = 0.82) in the study.

CONCLUSION

Our study demonstrated that AMD patients showed abnormal FCS with the visual network, sensorimotor network and default mode network, which might reflect the impaired vision, cognition and motor function in AMD patients. In addition, FCS indicator can be used as an effective biological marker to assist the clinical diagnosis of AMD.

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review

In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural-functional coupling of glioma. Additionally, the brain-computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.

Abnormal Default Mode Network Homogeneity in Major Depressive Disorder With Gastrointestinal Symptoms at Rest

Background

Gastrointestinal (GI) symptoms are prominent in many patients with major depressive disorder (MDD). However, it remains unclear whether MDD patients with GI symptoms have brain imaging alterations in the default mode network (DMN) regions.

Methods

A total of 35 MDD patients with GI symptoms, 17 MDD patients without GI symptoms, and 28 healthy controls (HCs) were recruited. All participants underwent resting-state functional magnetic resonance imaging scans. Network homogeneity (NH) and support vector machine (SVM) methods were used to analyze the imaging data.

Results

Gastrointestinal group showed higher 17-item Hamilton Rating Scale for Depression total scores and factor scores than the non-GI group. Compared with the non-GI group and HCs, the GI group showed decreased NH in the right middle temporal gyrus (MTG) and increased NH in the right precuneus (PCu). The SVM results showed that a combination of NH values of the right PCu and the right MTG exhibited the highest accuracy of 88.46% (46/52) to discriminate MDD patients with GI symptoms from those without GI symptoms.

Conclusion

Major depressive disorder patients with GI symptoms have more severe depressive symptoms than those without GI symptoms. Distinctive NH patterns in the DMN exist in MDD patients with GI symptoms, which can be applied as a potential brain imaging marker to discriminate MDD patients with GI symptoms from those without GI symptoms.