White Matter Microstructural Properties Associated with Impaired Attention in Chronic Schizophrenia: A Multi-Center Study

Decreased integration of default-mode network during a working memory task in schizophrenia with severe attention deficits

Background

Working memory (WM) and attention deficits are both important features of schizophrenia. WM is closely related to attention, for it acted as an important characteristic in activating and manipulating WM. However, the knowledge of neural mechanisms underlying the relationship between WM and attention deficits in schizophrenia is poorly investigated.

Methods

Graph theory was used to examine the network topology at the whole-brain and large-scale network levels among 125 schizophrenia patients with different severity of attention deficits (65 mild attention deficits; 46 moderate attention deficits; and 14 severe attention deficits) and 53 healthy controls (HCs) during an N-back WM task. These analyses were repeated in the same participants during the resting state.

Results

In the WM task, there were omnibus differences in small-worldness and normalized clustering coefficient at a whole-brain level and normalized characterized path length of the default-mode network (DMN) among all groups. Post hoc analysis further indicated that all patient groups showed increased small-worldness and normalized clustering coefficient of the whole brain compared with HCs, and schizophrenia with severe attention deficits showed increased normalized characterized path length of the DMN compared with schizophrenia with mild attention deficits and HCs. However, these observations were not persisted under the resting state. Further correlation analyses indicated that the increased normalized characterized path length of the DMN was correlated with more severe attentional deficits and poorer accuracy of the WM task.

Conclusion

Our research demonstrated that, compared with the schizophrenia patients with less attention deficits, disrupted integration of the DMN may more particularly underlie the WM deficits in schizophrenia patients with severe attention deficits.

Cognitive deficits, clinical variables, and white matter microstructure in schizophrenia: a multisite harmonization study

Cognitive deficits are among the best predictors of real-world functioning in schizophrenia. However, our understanding of how cognitive deficits relate to neuropathology and clinical presentation over the disease lifespan is limited. Here, we combine multi-site, harmonized cognitive, imaging, demographic, and clinical data from over 900 individuals to characterize a) cognitive deficits across the schizophrenia lifespan and b) the association between cognitive deficits, clinical presentation, and white matter (WM) microstructure. Multimodal harmonization was accomplished using T-scores for cognitive data, previously reported standardization methods for demographic and clinical data, and an established harmonization method for imaging data. We applied t-tests and correlation analysis to describe cognitive deficits in individuals with schizophrenia. We then calculated whole-brain WM fractional anisotropy (FA) and utilized regression-mediation analyses to model the association between diagnosis, FA, and cognitive deficits. We observed pronounced cognitive deficits in individuals with schizophrenia (p < 0.006), associated with more positive symptoms and medication dosage. Regression-mediation analyses showed that WM microstructure mediated the association between schizophrenia and language/processing speed/working memory/non-verbal memory. In addition, processing speed mediated the influence of diagnosis and WM microstructure on the other cognitive domains. Our study highlights the critical role of cognitive deficits in schizophrenia. We further show that WM is crucial when trying to understand the role of cognitive deficits, given that it explains the association between schizophrenia and cognitive deficits (directly and via processing speed).

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Sex-differential associations between cognitive impairments and white matter abnormalities in first episode and drug-naïve schizophrenia

AIM

Previous evidence has suggested that schizophrenia patients may display sex differences in cognitive impairments and cognitive impairments are related to disrupted white matter (WM) microstructure. The current research aims to address the intriguing possibility for the sex-specific association between cognitive deficits and WM abnormalities in first-episode and drug-naïve schizophrenia.

METHODS

Cognitive performance on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) was measured in 39 FEND patients (females:males = 23:16) and 30 healthy controls (females:males = 17:13), together with whole-brain WM fractional anisotropy (FA) values determined using voxel-based diffusion tensor imaging. Correlations between cognitive performance and FA values were assessed.

RESULTS

Patients performed significantly worse than healthy controls in the total score and most of the subscores of MCCB. Female patients displayed better cognitive performance than male patients on the Trail Making A Test, the Hopkins Verbal Learning Test and the Spatial Span Test in the Wechsler Memory Scale. More importantly, sex-differential association between cognitive performance and FA values was found in patients, but not in healthy controls. In particular, FA values in the cerebellum were negatively correlated with the continuous performance and digital sequence scores in male patients but positively correlated with the performance on the Spatial Span Test in the Wechsler Memory Scale in female patients.

CONCLUSIONS

These findings suggest sex-specific neurobiological substrates involved in cognitive deficits in early-onset schizophrenia and have important implications for differentially targeted interventions between males and females.

White matter microstructural associates of apathy-avolition in schizophrenia

Apathy is present at the onset in nearly half the patients with schizophrenia. Current therapies lack the efficiency to improve apathy in patients. The presence of apathy is also associated with poorer outcomes. Despite its clinical importance, the underlying mechanism of apathy in schizophrenia is unclear, but it seems frontostriatal connections play a role. In this study, we investigated whole-brain white matter microstructural properties associated with the severity of apathy-avolition in schizophrenia. We included 80 schizophrenia patients (60 Male, 20 Female) from the Mind Clinical Imaging Consortium database and associated Apathy-Avolition score of "Scale for Assessment of Negative Symptoms" with fiber integrity measures derived from diffusion-weighted imaging using Tract-Based Spatial Statistics (TBSS). We also did tractography on eight tracts, including bilateral superior longitudinal fasciculus, uncinate fasciculus, cingulum, genu and splenium of the corpus callosum. Age, gender, years of education, chlorpromazine equivalent cumulative dose, and acquisition site were inserted as covariates. We showed a widespread association between lower fiber integrity (by measures of increased mean diffusivity and decreased fractional anisotropy) and increased apathy-avolition in TBSS, which we also validated in tractography. Moreover, mean diffusivity, and not fractional anisotropy, was associated with apathy independent of disease severity. In conclusion, we propose diffuse white-matter pathology, within the corpus callosum, limbic system, and the frontostriatal circuit is involved in apathy-avolition in schizophrenia. Also, we suggest that diffuse neuroinflammatory processes may play a part in apathy-avolition, independent of disease severity.

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.

Cognitive deficits and white matter abnormalities in never-treated first-episode schizophrenia

Cognitive impairment is viewed as a core symptom of schizophrenia (SCZ), but its pathophysiological mechanism remains unclear. White matter (WM) disruption is considered to be a central abnormality that may contribute to cognitive impairment in SCZ patients. However, few studies have addressed the association between cognition and WM integrity in never-treated first-episode (NTFE) patients with SCZ. In this study, we used the MATRICS Consensus Cognitive Battery (MCCB) to evaluate cognitive function in NTFE patients (n = 39) and healthy controls (n = 30), and associated it with whole-brain fractional anisotropy (FA) values obtained via voxel-based diffusion tensor imaging. We found that FA was lower in five brain areas of SCZ patients, including the cingulate gyrus, internal capsule, corpus callosum, cerebellum, and brainstem. Compared with the healthy control group, the MCCB's total score and 8 out of 10 subscores were significantly lower in NTFE patients (all p < 0.001). Moreover, in patients but not healthy controls, the performance in the Trail Making Test was negatively correlated with the FA value in the left cingulate. Our findings provide evidence that WM disconnection is involved in some cognitive impairment in the early course of SCZ.