A review of impaired visual processing and the daily visual world in patients with schizophrenia

Feuerstein Instrumental Enrichment Program for People With Schizophrenia After the First Episode of Psychosis: Protocol for an Open-Label Intervention Study

BACKGROUND

Schizophrenia is a disorder associated with neurocognitive deficits that adversely affect daily functioning and impose an economic burden. Cognitive rehabilitation interventions, particularly during the early phases of illness, have been shown to improve cognition, functionality, and quality of life. The Feuerstein Instrumental Enrichment (FIE) program, based on the Mediated Learning Experience and the Structural Cognitive Modifiability theory, has been applied in various disorders, but its applicability in schizophrenia has not yet been clarified.

OBJECTIVE

This study aims to investigate the effects of the FIE program on the functionality of patients with first-episode schizophrenia.

METHODS

In total, 17 patients will be recruited for an open-label intervention consisting of twice-weekly sessions for 10 weeks. The primary outcome measure will be changes in the Goal Achievement Scale score. Maze task performance from the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) battery will serve as a secondary outcome measure. At the same time, changes in Positive and Negative Syndrome Scale scores and other MATRICS domains will be analyzed as exploratory outcomes. Assessments will be administered before and after the intervention, with a follow-up period of 6 months.

RESULTS

This trial was preregistered in The Brazilian Registry of Clinical Trials (RBR-4gzhy4s). By February 2024, 11 participants were enrolled in the training. Recruitment is expected to be completed by May 2024. Data analysis will be conducted between May and September 2024. The results are expected to be published in January 2025.

CONCLUSIONS

This study may establish a protocol for the FIE program that uses mediation techniques for individuals in the early stages of schizophrenia. The results will add to the knowledge about strategies to promote cognitive skills and functional impairment in daily life.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/57031.

Brain subnetworks most sensitive to alterations of functional connectivity in Schizophrenia: a data-driven approach

Functional connectivity (FC) of the brain changes in various brain disorders. Its complexity, however, makes it difficult to obtain a systematic understanding of these alterations, especially when they are found individually and through hypothesis-based methods. It would be easier if the variety of brain connectivity alterations is extracted through data-driven approaches and expressed as variation modules (subnetworks). In the present study, we modified a blind approach to determine inter-group brain variations at the network level and applied it specifically to schizophrenia (SZ) disorder. The analysis is based on the application of independent component analysis (ICA) over the subject's dimension of the FC matrices, obtained from resting-state functional magnetic resonance imaging (rs-fMRI). The dataset included 27 SZ people and 27 completely matched healthy controls (HC). This hypothesis-free approach led to the finding of three brain subnetworks significantly discriminating SZ from HC. The area associated with these subnetworks mostly covers regions in visual, ventral attention, and somatomotor areas, which are in line with previous studies. Moreover, from the graph perspective, significant differences were observed between SZ and HC for these subnetworks, while there was no significant difference when the same parameters (path length, network strength, global/local efficiency, and clustering coefficient) across the same limited data were calculated for the whole brain network. The increased sensitivity of those subnetworks to SZ-induced alterations of connectivity suggested whether an individual scoring method based on their connectivity values can be applied to classify subjects. A simple scoring classifier was then suggested based on two of these subnetworks and resulted in acceptable sensitivity and specificity with an area under the ROC curve of 77.5%. The third subnetwork was found to be a less specific building block (module) for describing SZ alterations. It projected a wider range of inter-individual variations and, therefore, had a lower chance to be considered as a SZ biomarker. These findings confirmed that investigating brain variations from a modular viewpoint can help to find subnetworks that are more sensitive to SZ-induced alterations. Altogether, our study results illustrated the developed method's ability to systematically find brain alterations caused by SZ disorder from a network perspective.

Visual search in neurodevelopmental disorders: evidence towards a continuum of impairment

Disorders with neurodevelopmental aetiology such as Attention-Deficit/Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD) and Schizophrenia share commonalities at many levels of investigation despite phenotypic differences. Evidence of genetic overlap has led to the concept of a continuum of neurodevelopmental impairment along which these disorders can be positioned in aetiological, pathophysiological and developmental features. This concept requires their simultaneous comparison at different levels, which has not been accomplished so far. Given that cognitive impairments are core to the pathophysiology of these disorders, we provide for the first time differentiated head-to-head comparisons in a complex cognitive function, visual search, decomposing the task with eye movement-based process analyses. N = 103 late-adolescents with schizophrenia, ADHD, ASD and healthy controls took a serial visual search task, while their eye movements were recorded. Patients with schizophrenia presented the greatest level of impairment across different phases of search, followed by patients with ADHD, who shared with patients with schizophrenia elevated intra-subject variability in the pre-search stage. ASD was the least impaired group, but similar to schizophrenia in post-search processes and to schizophrenia and ADHD in pre-search processes and fixation duration while scanning the items. Importantly, the profiles of deviancy from controls were highly correlated between all three clinical groups, in line with the continuum idea. Findings suggest the existence of one common neurodevelopmental continuum of performance for the three disorders, while quantitative differences appear in the level of impairment. Given the relevance of cognitive impairments in these three disorders, we argue in favour of overlapping pathophysiological mechanisms.

In silico Effects of Synaptic Connections in the Visual Thalamocortical Pathway

We have studied brain connectivity using a biologically inspired in silico model of the visual pathway consisting of the lateral geniculate nucleus (LGN) of the thalamus, and layers 4 and 6 of the primary visual cortex. The connectivity parameters in the model are informed by the existing anatomical parameters from mammals and rodents. In the base state, the LGN and layer 6 populations in the model oscillate with dominant alpha frequency, while the layer 4 oscillates in the theta band. By changing intra-cortical hyperparameters, specifically inhibition from layer 6 to layer 4, we demonstrate a transition to alpha mode for all the populations. Furthermore, by increasing the feedforward connectivities in the thalamo-cortico-thalamic loop, we could transition into the beta band for all the populations. On looking closely, we observed that the origin of this beta band is in the layer 6 (infragranular layers); lesioning the thalamic feedback from layer 6 removed the beta from the LGN and the layer 4. This agrees with existing physiological studies where it is shown that beta rhythm is generated in the infragranular layers. Lastly, we present a case study to demonstrate a neurological condition in the model. By changing connectivities in the network, we could simulate the condition of significant (P < 0.001) decrease in beta band power and a simultaneous increase in the theta band power, similar to that observed in Schizophrenia patients. Overall, we have shown that the connectivity changes in a simple visual thalamocortical in silico model can simulate state changes in the brain corresponding to both health and disease conditions.

Early-stage visual perception impairment in schizophrenia, bottom-up and back again

Visual perception is one of the basic tools for exploring the world. However, in schizophrenia, this modality is disrupted. So far, there has been no clear answer as to whether the disruption occurs primarily within the brain or in the precortical areas of visual perception (the retina, visual pathways, and lateral geniculate nucleus [LGN]). A web-based comprehensive search of peer-reviewed journals was conducted based on various keyword combinations including schizophrenia, saliency, visual cognition, visual pathways, retina, and LGN. Articles were chosen with respect to topic relevance. Searched databases included Google Scholar, PubMed, and Web of Science. This review describes the precortical circuit and the key changes in biochemistry and pathophysiology that affect the creation and characteristics of the retinal signal as well as its subsequent modulation and processing in other parts of this circuit. Changes in the characteristics of the signal and the misinterpretation of visual stimuli associated with them may, as a result, contribute to the development of schizophrenic disease.

Lateralization of Color Discrimination Performance and Lexical Effects in Patients With Chronic Schizophrenia

Introduction: Patients with schizophrenia experience various visual disturbances. However, information regarding color perception in these patients is rare. In this study, we used a lateralized color search task to investigate whether difference in color name affects color recognition in patients with schizophrenia.

Methods: In a color search task, we controlled the position of the target that emerged from the left visual field (LVF) or right visual field (RVF) as well as the color category. In this task, both the target and the distractors had the same or different color name (e.g., blue or green).

Results: Patients with schizophrenia showed faster performance in the color search task with different color names for target-distractors when the target emerged from the LVF than when it emerged from the RVF. However, the same laterality was not observed in healthy controls. This finding indicates that semantic processing for color name differences influenced visual discrimination performance in patients with schizophrenia more profoundly in the LVF than in the RVF.

Conclusion: This lateralized performance could imply the failure of the left hemisphere language processing dominance in schizophrenia. A search paradigm combining target position and category may indicate that automatic language processing depends on imbalanced hemispheric function in schizophrenia.

Contribution of Eye-Tracking to Study Cognitive Impairments Among Clinical Populations

In the field of psychology, the merge of decision-theory and neuroscientific methods produces an array of scientifically recognized paradigms. For example, by exploring consumer’s eye-movement behavior, researchers aim to deepen the understanding of how patterns of retinal activation are being meaningfully transformed into visual experiences and connected with specific reactions (e.g., purchase). Notably, eye-movements provide knowledge of one’s homeostatic balance and gatekeep information that shape decisions. Hence, vision science investigates the quality of observed environments determined under various experimental conditions. Moreover, it answers questions on how human process visual stimuli and use gained information for a successful strategy to achieve certain goals. While capturing cognitive states with the support of the eye-trackers progresses at a relatively fast pace in decision-making research, measuring the visual performance of real-life tasks, which require complex cognitive skills, is tentatively translated into clinical experiments. Nevertheless, the potential of the human eye as a highly valuable source of biomarkers has been underlined. In this article, we aim to draw readers attention to decision-making experimental paradigms supported with eye-tracking technology among clinical populations. Such interdisciplinary approach may become an important component that will (i) help in objectively illustrating patient’s models of beliefs and values, (ii) support clinical interventions, and (iii) contribute to health services. It is possible that shortly, eye-movement data from decision-making experiments will grant the scientific community a greater understanding of mechanisms underlining mental states and consumption practices that medical professionals consider as obsessions, disorders or addiction.

Recent updates of eye movement abnormalities in patients with schizophrenia: A scoping review

AIM

Although eye-tracking technology expands beyond capturing eye data just for the sole purpose of ensuring participants maintain their gaze at the presented fixation cross, gaze technology remains of less importance in clinical research. Recently, impairments in visual information encoding processes indexed by novel gaze metrics have been frequently reported in patients with schizophrenia. This work undertakes a scoping review of research on saccadic dysfunctions and exploratory eye movement deficits among patients with schizophrenia. It gathers promising pieces of evidence of eye movement abnormalities in attention-demanding tasks on the schizophrenia spectrum that have mounted in recent years and their outcomes as potential biological markers.

METHODS

The protocol was drafted based on PRISMA for scoping review guidelines. Electronic databases were systematically searched to identify articles published between 2010 and 2020 that examined visual processing in patients with schizophrenia and reported eye movement characteristics as potential biomarkers for this mental illness.

RESULTS

The use of modern eye-tracking instrumentation has been reported by numerous neuroscientific studies to successfully and non-invasively improve the detection of visual information processing impairments among the screened population at risk of and identified with schizophrenia.

CONCLUSIONS

Eye-tracking technology has the potential to contribute to the process of early intervention and more apparent separation of the diagnostic entities, being put together by the syndrome-based approach to the diagnosis of schizophrenia. However, context-processing paradigms should be conducted and reported in equally accessible publications to build comprehensive models.

ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia

We construct Functional Connectivity Networks (FCN) from resting state fMRI (rsfMRI) recordings towards the classification of brain activity between healthy and schizophrenic subjects using a publicly available dataset (the COBRE dataset) of 145 subjects (74 healthy controls and 71 schizophrenic subjects). First, we match the anatomy of the brain of each individual to the Desikan-Killiany brain atlas. Then, we use the conventional approach of correlating the parcellated time series to construct FCN and ISOMAP, a nonlinear manifold learning algorithm to produce low-dimensional embeddings of the correlation matrices. For the classification analysis, we computed five key local graph-theoretic measures of the FCN and used the LASSO and Random Forest (RF) algorithms for feature selection. For the classification we used standard linear Support Vector Machines. The classification performance is tested by a double cross-validation scheme (consisting of an outer and an inner loop of "Leave one out" cross-validation (LOOCV)). The standard cross-correlation methodology produced a classification rate of 73.1%, while ISOMAP resulted in 79.3%, thus providing a simpler model with a smaller number of features as chosen from LASSO and RF, namely the participation coefficient of the right thalamus and the strength of the right lingual gyrus.

Identification of common neural substrates with connectomic abnormalities in four major psychiatric disorders: A connectome-wide association study

Background

Recent imaging studies of large datasets suggested that psychiatric disorders have common biological substrates. This study aimed to identify all the common neural substrates with connectomic abnormalities across four major psychiatric disorders by using the data-driven connectome-wide association method of multivariate distance matrix regression (MDMR).

Methods

This study analyzed a resting functional magnetic resonance imaging dataset of 100 patients with schizophrenia, 100 patients with bipolar I disorder, 100 patients with bipolar II disorder, 100 patients with major depressive disorder, and 100 healthy controls (HCs). We calculated a voxel-wise 4,330 × 4,330 matrix of whole-brain functional connectivity (FC) with 8-mm isotropic resolution for each participant and then performed MDMR to identify structures where the overall multivariate pattern of FC was significantly different between each patient group and the HC group. A conjunction analysis was performed to identify common neural regions with FC abnormalities across these four psychiatric disorders.

Results

The conjunction of the MDMR maps revealed that the four groups of patients shared connectomic abnormalities in distributed cortical and subcortical structures, which included bilateral thalamus, cerebellum, frontal pole, supramarginal gyrus, postcentral gyrus, lingual gyrus, lateral occipital cortex, and parahippocampus. The follow-up analysis based on pair-wise FC of these regions demonstrated that these psychiatric disorders also shared similar patterns of FC abnormalities characterized by sensory/subcortical hyperconnectivity, association/subcortical hypoconnectivity, and sensory/association hyperconnectivity.

Conclusions

These findings suggest that major psychiatric disorders share common connectomic abnormalities in distributed cortical and subcortical regions and provide crucial support for the common network hypothesis of major psychiatric disorders.

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites

Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.