Brain Activity In Schizophrenia Research Articles

Illness-related variables and abnormalities of resting-state brain activity in schizophrenia.

The development of neuroimaging biomarkers in patients with schizophrenia (SCZ) requires a refined clinical characterization. A limitation of the neuroimaging literature is the partial uptake of progress in characterizing disease-related features, particularly negative symptoms (NS) and cognitive impairment (CI). In the present study, we assessed NS and CI using up-to-date instruments and investigated the associations of abnormalities in brain resting-state (rs)-activity with disease-related features. Sixty-two community-dwelling SCZ subjects participated in the study. Multiple regression analyses were performed with the rs-activity of nine regions of interest as dependent variables and disease-related features as explanatory variables. Attention/vigilance deficits were negatively associated with dorsal anterior cingulate rs-activity and, together with depression, were positively associated with right dorsolateral prefrontal cortex rs-activity. These deficits and impairment of Reasoning/problem-solving, together with conceptual disorganization, were associated with right inferior parietal lobule and temporal parietal junction rs-activity. Independent of other features, the NS Expressive Deficit domain was associated with the left ventral caudate, while the Motivational Deficit was associated with the dorsal caudate rs-activity. Neurocognitive deficits and the two negative symptom domains are associated with different neural markers. Replications of these findings could foster the identification of clinically actionable biomarkers of poor functional outcomes.

Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis

BackgroundSchizophrenia is a prevalent mental disorder, leading to severe disability. Currently, the absence of objective biomarkers hinders effective diagnosis. This study was conducted to explore the aberrant spontaneous brain activity and investigate the potential of abnormal brain indices as diagnostic biomarkers employing machine learning methods. MethodsA total of sixty-one schizophrenia patients and seventy demographically matched healthy controls were enrolled in this study. The static indices of resting-state functional magnetic resonance imaging (rs-fMRI) including amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated to evaluate spontaneous brain activity. Subsequently, a sliding-window method was then used to conduct temporal dynamic analysis. The comparison of static and dynamic rs-fMRI indices between the patient and control groups was conducted using a two-sample t-test. Finally, the machine learning analysis was applied to estimate the diagnostic value of abnormal indices of brain activity. ResultsSchizophrenia patients exhibited a significant increase ALFF value in inferior frontal gyrus, alongside significant decreases in fALFF values observed in left postcentral gyrus and right cerebellum posterior lobe. Pervasive aberrations in ReHo indices were observed among schizophrenia patients, particularly in frontal lobe and cerebellum. A noteworthy reduction in voxel-wise concordance of dynamic indices was observed across gray matter regions encompassing the bilateral frontal, parietal, occipital, temporal, and insular cortices. The classification analysis achieved the highest values for area under curve at 0.87 and accuracy at 81.28% when applying linear support vector machine and leveraging a combination of abnormal static and dynamic indices in the specified brain regions as features. ConclusionsThe static and dynamic indices of brain activity exhibited as potential neuroimaging biomarkers for the diagnosis of schizophrenia.

Temporal Dynamic Synchronous Functional Brain Network for Schizophrenia Classification and Lateralization Analysis.

Available evidence suggests that dynamic functional connectivity can capture time-varying abnormalities in brain activity in resting-state cerebral functional magnetic resonance imaging (rs-fMRI) data and has a natural advantage in uncovering mechanisms of abnormal brain activity in schizophrenia (SZ) patients. Hence, an advanced dynamic brain network analysis model called the temporal brain category graph convolutional network (Temporal-BCGCN) was employed. Firstly, a unique dynamic brain network analysis module, DSF-BrainNet, was designed to construct dynamic synchronization features. Subsequently, a revolutionary graph convolution method, TemporalConv, was proposed based on the synchronous temporal properties of features. Finally, the first modular test tool for abnormal hemispherical lateralization in deep learning based on rs-fMRI data, named CategoryPool, was proposed. This study was validated on COBRE and UCLA datasets and achieved 83.62% and 89.71% average accuracies, respectively, outperforming the baseline model and other state-of-the-art methods. The ablation results also demonstrate the advantages of TemporalConv over the traditional edge feature graph convolution approach and the improvement of CategoryPool over the classical graph pooling approach. Interestingly, this study showed that the lower-order perceptual system and higher-order network regions in the left hemisphere are more severely dysfunctional than in the right hemisphere in SZ, reaffirmings the importance of the left medial superior frontal gyrus in SZ. Our code was available at: https://github.com/swfen/Temporal-BCGCN.

The altered network complexity of resting-state functional brain activity in schizophrenia and bipolar disorder patients

Schizophrenia (SZ) and bipolar disorder (BD) are two of the most frequent mental disorders. These disorders exhibit similar psychotic symptoms, making diagnosis challenging and leading to misdiagnosis. Yet, the network complexity changes driving spontaneous brain activity in SZ and BD patients are still unknown. Functional entropy (FE) is a novel way of measuring the dispersion (or spread) of functional connectivities inside the brain. The FE was utilized in this study to examine the network complexity of the resting-state fMRI data of SZ and BD patients at three levels, including global, modules, and nodes. At three levels, the FE of SZ and BD patients was considerably lower than that of normal control (NC). At the intra-module level, the FE of SZ was substantially higher than that of BD in the cingulo-opercular network. Moreover, a strong negative association between FE and clinical measures was discovered in patient groups. Finally, we classified using the FE features and attained an accuracy of 66.7% (BD vs. SZ vs. NC) and an accuracy of 75.0% (SZ vs. BD). These findings proposed that network connectivity’s complexity analyses using FE can provide important insights for the diagnosis of mental illness.

28. Longer Illness Duration is Associated With Greater Individual Variability in Functional Brain Activity in Schizophrenia, but not Bipolar Disorder

28. Longer Illness Duration is Associated With Greater Individual Variability in Functional Brain Activity in Schizophrenia, but not Bipolar Disorder

Functional network interactions in patients with schizophrenia with persistent auditory verbal hallucinations: A multimodal MRI fusion approach using three-way pICA

Over the last decade, there have been an increasing number of functional magnetic resonance imaging (fMRI) studies examining brain activity in schizophrenia (SZ) patients with persistent auditory verbal hallucinations (AVH) using either task-based or resting-state fMRI (rs-fMRI) paradigms. Such data have been conventionally collected and analyzed as distinct modalities, disregarding putative crossmodal interactions. Recently, it has become possible to incorporate two or more modalities in one comprehensive analysis to uncover hidden patterns of neural dysfunction not sufficiently captured by separate analysis. A novel multivariate fusion approach to multimodal data analysis, i.e., parallel independent component analysis (pICA), has been previously shown to be a powerful tool in this regard. We utilized three-way pICA to study covarying components among fractional amplitude of low-frequency fluctuations (fALFF) for rs-MRI and task-based activation computed from an alertness and a working memory (WM) paradigm of 15 SZ patients with AVH, 16 non-hallucinating SZ patients (nAVH), and 19 healthy controls (HC). The strongest connected triplet (false discovery rate (FDR)-corrected pairwise correlations) comprised a frontostriatal/temporal network (fALFF), a temporal/sensorimotor network (alertness task), and a frontoparietal network (WM task). Frontoparietal and frontostriatal/temporal network strength significantly differed between AVH patients and HC. Phenomenological features such as omnipotence and malevolence of AVH were associated with temporal/sensorimotor and frontoparietal network strength. The transmodal data confirm a complex interplay of neural systems subserving attentional processes and cognitive control interacting with speech and language processing networks. In addition, the data emphasize the importance of sensorimotor regions modulating specific symptom dimensions of AVH.

Combining fMRI and DISC1 gene haplotypes to understand working memory-related brain activity in schizophrenia.

The DISC1 gene is one of the most relevant susceptibility genes for psychosis. However, the complex genetic landscape of this locus, which includes protective and risk variants in interaction, may have hindered consistent conclusions on how DISC1 contributes to schizophrenia (SZ) liability. Analysis from haplotype approaches and brain-based phenotypes can contribute to understanding DISC1 role in the neurobiology of this disorder. We assessed the brain correlates of DISC1 haplotypes associated with SZ through a functional neuroimaging genetics approach. First, we tested the association of two DISC1 haplotypes, the HEP1 (rs6675281-1000731-rs999710) and the HEP3 (rs151229-rs3738401), with the risk for SZ in a sample of 138 healthy subjects (HS) and 238 patients. This approach allowed the identification of three haplotypes associated with SZ (HEP1-CTG, HEP3-GA and HEP3-AA). Second, we explored whether these haplotypes exerted differential effects on n-back associated brain activity in a subsample of 70 HS compared to 70 patients (diagnosis × haplotype interaction effect). These analyses evidenced that HEP3-GA and HEP3-AA modulated working memory functional response conditional to the health/disease status in the cuneus, precuneus, middle cingulate cortex and the ventrolateral and dorsolateral prefrontal cortices. Our results are the first to show a diagnosis-based effect of DISC1 haplotypes on working memory-related brain activity, emphasising its role in SZ.

Associations between polygenic risk scores and amplitude of low-frequency fluctuation of inferior frontal gyrus in schizophrenia

Schizophrenia (SCZ) is a serious and complex mental disorder with high heritability. Polygenic risk score (PRS) is a useful tool calculating the accumulating effects of multiple common genetic variants of schizophrenia. The amplitude of low-frequency fluctuation (ALFF) is an efficient index to reflect spontaneous, intrinsic neuronal activity. Aberrant ALFF of brain regions were reported in schizophrenia frequently, but the relationship between PRS and ALFF has not been studied. In the present study, we compared PRS and ALFF in 101 schizophrenia patients and 106 age-matched healthy controls to test their associations with schizophrenia. Then, the correlation of PRS with ALFF was measured to reveal the effect of polygenic risk on brain activity in schizophrenia. We found that schizophrenia patients showed significant differences in PRS and ALFF compared with controls. Twenty-six brain regions showed significant difference of ALFF between schizophrenia cases and controls, of which left inferior frontal gyrus, triangular part (IFGtriang.L) showed increased activity in schizophrenia. PRS-SCZ was positively correlated with ALFF in IFGtriang.L in 57 non-chronic patients. Genes involved in synaptic organization and transmission, especially in glutamatergic synapse, were highly enriched in PRS-SCZ genes, suggesting the dysfunction of synapses in schizophrenia. These results help to understand the molecular mechanism underlying schizophrenia and related brain dysfunction.

Longer illness duration is associated with greater individual variability in functional brain activity in Schizophrenia, but not bipolar disorder

BackgroundIndividuals with schizophrenia exhibit greater inter-patient variability in functional brain activity during neurocognitive task performance. Some studies have shown associations of age and illness duration with brain function; however, the association of these variables with variability in brain function activity is not known. In order to better understand the progressive effects of age and illness duration across disorders, we examined the relationship with individual variability in brain activity. MethodsNeuroimaging and behavioural data were extracted from harmonized datasets collectively including 212 control participants, 107 individuals with bipolar disorder, and 232 individuals with schizophrenia (total n = 551). Functional activity in response to an N-back working memory task (2-back vs 1-back) was examined. Individual variability was quantified via the correlational distance of fMRI activity between participants; mean correlational distance of one participant in relation to all others was defined as a ‘variability score’. ResultsGreater individual variability was found in the schizophrenia group compared to the bipolar disorder and control groups (p = 1.52e−09). Individual variability was significantly associated with aging (p = 0.027), however, this relationship was not different across diagnostic groups. In contrast, in the schizophrenia sample only, a longer illness duration was associated with increased variability (p = 0.027). ConclusionAn increase in variability was observed in the schizophrenia group related to illness duration, beyond the effects of normal aging, implying illness-related deterioration of cognitive networks. This has clinical implications for considering long-term trajectories in schizophrenia and progressive neural and cognitive decline which may be amiable to novel treatments.

Longer Illness Duration is Associated with Greater Individual Variability in Functional Brain Activity in Schizophrenia, But Not Bipolar Disorder

Longer Illness Duration is Associated with Greater Individual Variability in Functional Brain Activity in Schizophrenia, But Not Bipolar Disorder

Atypical Antipsychotics Mediate Dynamics of Intrinsic Brain Activity in Early-Stage Schizophrenia? A Preliminary Study

ObjectiveAbnormalities of static brain activity have been reported in schizophrenia, but it remains to be clarified the temporal variability of intrinsic brain activities in schizophrenia and how atypical antipsychotics affect it. MethodsWe employed a resting-state functional magnetic resonance imaging (rs-fMRI) and a sliding-window analysis of dynamic amplitude of low-frequency fluctuation (dALFF) to evaluate the dynamic brain activities in schizophrenia (SZ) patients before and after 8-week antipsychotic treatment. Twenty-six schizophrenia individuals and 26 matched healthy controls (HC) were included in this study. ResultsCompared with HC, SZ showed stronger dALFF in the right inferior temporal gyrus (ITG.R) at baseline. After medication, the SZ group exhibited reduced dALFF in the right middle occipital gyrus (MOG.R) and increased dALFF in the left superior frontal gyrus (SFG.L), right middle frontal gyrus (MFG.R), and right inferior parietal lobule (IPL.R). Dynamic ALFF in IPL.R was found to significant negative correlate with the Scale for the Assessment of Negative Symptoms (SANS) scores at baseline. ConclusionOur results showed dynamic intrinsic brain activities altered in schizophrenia after short term antipsychotic treatment. The findings of this study support and expand the application of dALFF method in the study of the pathological mechanism in psychosis in the future.

Alteration of power law scaling of spontaneous brain activity in schizophrenia

Nonlinear dynamical analysis has been used to quantify the complexity of brain signal at temporal scales. Power law scaling is a well-validated method in physics that has been used to describe the dynamics of a system in the frequency domain, ranging from noisy oscillation to complex fluctuations. In this research, we investigated the power-law characteristics in a large-scale resting-state fMRI data of schizophrenia and healthy participants derived from Taiwan Aging and Mental Illness cohort. We extracted the power spectral density (PSD) of resting signal by Fourier transform. Power law scaling of PSD was estimated by determining the slope of the regression line fitting to the logarithm of PSD. t-Test was used to assess the statistical difference in power law scaling between schizophrenia and healthy participants. The significant differences in power law scaling were found in six brain regions. Schizophrenia patients have significantly more positive power law scaling (i.e., more homogenous frequency components) at four brain regions: left precuneus, left medial dorsal nucleus, right inferior frontal gyrus, and right middle temporal gyrus and less positive power law scaling (i.e., more dominant at lower frequency range) in bilateral putamen compared with healthy participants. Moreover, significant correlations of power law scaling with the severity of psychosis were found. These findings suggest that schizophrenia has abnormal brain signal complexity linked to psychotic symptoms. The power law scaling represents the dynamical properties of resting-state fMRI signal may serve as a novel functional brain imaging marker for evaluating patients with mental illness.

Target selection for deep brain stimulation in treatment resistant schizophrenia

The use of deep brain stimulation (DBS) in treatment resistant patients with schizophrenia is of considerable current interest, but where to site the electrodes is challenging. This article reviews rationales for electrode placement in schizophrenia based on evidence for localized brain abnormality in the disorder and the targets that have been proposed and employed to date. The nucleus accumbens and the subgenual anterior cingulate cortex are of interest on the grounds that they are sites of potential pathologically increased brain activity in schizophrenia and so susceptible to the local inhibitory effects of DBS; both sites have been employed in trials of DBS in schizophrenia. Based on other lines of reasoning, the ventral tegmental area, the substantia nigra pars reticulata and the habenula have also been proposed and in some cases employed. The dorsolateral prefrontal cortex has not been suggested, probably reflecting evidence that it is underactive rather than overactive in schizophrenia. The hippocampus is also of theoretical interest but there is no clear functional imaging evidence that it shows overactivity in schizophrenia. On current evidence, the nucleus accumbens may represent the strongest candidate for DBS electrode placement in schizophrenia, with the substantia nigra pars reticulata also showing promise in a single case report; the ventral tegmental area is also of potential interest, though it remains untried.

Regional Homogeneity Brain Alterations in Schizophrenia: An Activation Likelihood Estimation Meta-Analysis.

ObjectiveResting state functional magnetic resonance imaging (rsfMRI) provides a lot of evidence for local abnormal brain activity in schizophrenia, but the results are not consistent. Our aim is to find out the consistent abnormal brain regions of the patients with schizophrenia by using regional homogeneity (ReHo), and indirectly understand the degree of brain damage of the patients with drug-naive first episode schizophrenia (Dn-FES) and chronic schizophrenia. MethodsWe performed the experiment by activation likelihood estimation (ALE) software to analysis the differences between people with schizophrenia group (all schizophrenia group and chronic schizophrenia group) and healthy controls. ResultsThirteen functional imaging studies were included in quantitative meta-analysis. All schizophrenia group showed decreased ReHo in bilateral precentral gyrus (PreCG) and left middle occipital gyrus (MOG), and increased ReHo in bilateral superior frontal gyrus (SFG) and right insula. Chronic schizophrenia group showed decreased ReHo in bilateral MOG, right fusiform gyrus, left PreCG, left cerebellum, right precuneus, left medial frontal gyrus and left anterior cingulate cortex (ACC). No significant increased brain areas were found in patients with chronic schizophrenia. ConclusionOur findings suggest that patients with chronic schizophrenia have more extensive brain damage than FES, which may contribute to our understanding of the progressive pathophysiology of schizophrenia.

Altered Complexity of Spontaneous Brain Activity in Schizophrenia and Bipolar Disorder Patients

Slice images of the brain regions with significant difference Slice images showed the differences of (a) the effect of group and (b) the effect of interaction between the control and patient groups after adjusting for age, sex, and education (P < 0.05, GRF corrected). CAL.L: Calcarine fissure, DCG.L: Median Cingulate, LING.L: Lingual Gyrus, PCUN.L: Precuneus, IOG.R: Inferior Occipital Gyrus, MTG.R: Middle Temporal Gyrus, HIP.R: Hippocampus, THA.R: Thalamus, MFG.R: Middle Frontal Gyrus, MTG.L: Middle Temporal Gyrus by N. Zhang et al (586–595)image

Predicting individual variability in task-evoked brain activity in schizophrenia.

What goes wrong in a schizophrenia patient's brain that makes it so different from a healthy brain? In this study, we tested the hypothesis that the abnormal brain activity in schizophrenia is tightly related to alterations in brain connectivity. Using functional magnetic resonance imaging (fMRI), we demonstrated that both resting‐state functional connectivity and brain activity during the well‐validated N‐back task differed significantly between schizophrenia patients and healthy controls. Nevertheless, using a machine‐learning approach we were able to use resting‐state functional connectivity measures extracted from healthy controls to accurately predict individual variability in the task‐evoked brain activation in the schizophrenia patients. The predictions were highly accurate, sensitive, and specific, offering novel insights regarding the strong coupling between brain connectivity and activity in schizophrenia. On a practical perspective, these findings may allow to generate task activity maps for clinical populations without the need to actually perform any tasks, thereby reducing patients inconvenience while saving time and money.

Altered Resting-State Brain Activity in Schizophrenia and Obsessive-Compulsive Disorder Compared With Non-psychiatric Controls: Commonalities and Distinctions Across Disorders.

Backgrounds: Schizophrenia (SCZ) and obsessive-compulsive disorder (OCD) are classified as two chronic psychiatric disorders with high comorbidity rate and shared clinical symptoms. Abnormal spontaneous brain activity within the cortical–striatal neural circuits has been observed in both disorders. However, it is unclear if the common or distinct neural abnormalities underlie the neurobiological substrates in the resting state.Methods: Resting-state fMRI data were collected from 88 patients with SCZ, 58 patients with OCD, and 72 healthy control subjects. First, we examined differences in amplitude of low-frequency fluctuations (ALFF) among three groups. Resting-state functional connectivity (rsFC) analysis with the brain region that showed different ALFF as the seed was then conducted to identify the changes in brain networks. Finally, we examined the correlation between the altered activities and clinical symptoms.Results: Both the patients with SCZ and OCD showed increased ALFF in the right hippocampus and decreased ALFF in the left posterior cingulate cortex (PCC). SCZ patients exhibited increased ALFF in the left caudate [voxel-level family-wise error (FWE) P < 0.05] and decreased rsFC between the left caudate and right cerebellum, which correlated with positive symptoms. The left caudate showed increased rsFC with the right thalamus and bilateral supplementary motor complex (SMC) in OCD patients (cluster-level FWE P < 0.05).Conclusions: The hippocampus and PCC are common regions presenting abnormal local spontaneous neuronal activities in both SCZ and OCD, while the abnormality of the striatum can reflect the differences. Increased ALFF in the striatum and symptom-related weakened rsFC between the caudate and cerebellum showed SCZ specificity. Enhanced rsFC between the caudate and SMC may be a key characteristic in OCD. Our research shows the similarities and differences between the two diseases from the perspective of resting-state fMRI, which provides clues to understand the disease and find methods for treatment.

Altered Complexity of Spontaneous Brain Activity in Schizophrenia and Bipolar Disorder Patients.

Schizophrenia (SC) and bipolar disorder (BP) share elements of symptoms and the underlying neural mechanisms for both remain unclear. Recently, the complexity of spontaneous functional MRI (fMRI) signals in brain activity has been investigated in SC and BP using multiscale sample entropy (MSE) with inconsistent results. To perform MSE analysis across five time scales to assess differences in resting-state fMRI signal complexity in SC, BP, and normal controls (NC). Retrospective. Fifty SC, 49 BP, and 49 NC. A 3 T, T2* weighted echo planar imaging (EPI) sequence. The mean MSEs of all gray matter (GM) and of 12 regions of interest (ROIs) were extracted using masks across the five scales. The regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) in these ROIs were also determined and the relationship between the three measures was investigated. The correlations between cognitive assessment scores and MSE values were also explored. Bonferroni correction, One-way ANOVA, Spearman rank correlation coefficient (r), Gaussian random field (GRF) correction. There were decreased GM MSE values in the patient groups (F=9.629, P < 0.05). SC and BP patients demonstrated lower complexity than NCs in the calcarine fissure, precuneus, inferior occipital gyrus, lingual gyrus and cerebellum, and higher complexity in the median cingulate, thalamus, hippocampus, middle temporal gyrus and middle frontal gyrus. There were significant differences between SC and BP patients in the precuneus (F=4.890, P < 0.05) and inferior occipital gyrus (F=5.820, P < 0.05). Calcarine fissure, cingulate, temporal gyrus, occipital gyrus, hippocampus, precuneus, frontal gyrus, and lingual gyrus MSE values were significantly correlated with both ReHo (r > 0.282, P < 0.05) and ALFF (r > 0.278, P < 0.05). Furthermore, median temporal MSE (r=-0.321, P < 0.05) on scale 3 and (r=-0.307, P < 0.05) on scale 4 and median cingulate MSE (r=-0.337, P < 0.05) on scale 5 was significantly negatively correlated with cognitive assessment scores. These data highlight different patterns of brain signal intensity complexity in SC and BP. 1 TECHNICAL EFFICACY: Stage 1.

Abnormalities of intrinsic regional brain activity in first-episode and chronic schizophrenia: a meta-analysis of resting-state functional MRI

Resting-state functional MRI (fMRI) studies have provided much evidence for abnormal intrinsic brain activity in schizophrenia, but results have been inconsistent. We conducted a meta-analysis of whole-brain, resting-state fMRI studies that explored differences in amplitude of low-frequency fluctuation (ALFF) between people with schizophrenia (including first episode and chronic) and healthy controls. A systematic literature search identified 24 studies comparing a total of 1249 people with schizophrenia and 1179 healthy controls. Overall, patients with schizophrenia displayed decreased ALFF in the bilateral postcentral gyrus, bilateral precuneus, left inferior parietal gyri and right occipital lobe, and increased ALFF in the right putamen, right inferior frontal gyrus, left inferior temporal gyrus and right anterior cingulate cortex. In the subgroup analysis, patients with first-episode schizophrenia demonstrated decreased ALFF in the bilateral inferior parietal gyri, right precuneus and left medial prefrontal cortex, and increased ALFF in the bilateral putamen and bilateral occipital gyrus. Patients with chronic schizophrenia showed decreased ALFF in the bilateral postcentral gyrus, left precuneus and right occipital gyrus, and increased ALFF in the bilateral inferior frontal gyri, bilateral superior frontal gyrus, left amygdala, left inferior temporal gyrus, right anterior cingulate cortex and left insula. The small sample size of our subgroup analysis, predominantly Asian samples, processing steps and publication bias could have limited the accuracy of the results. Our comprehensive meta-analysis suggests that findings of aberrant regional intrinsic brain activity during the initial stages of schizophrenia, and much more widespread damage with the progression of disease, may contribute to our understanding of the progressive pathophysiology of schizophrenia.

Manipulating visual scanpaths during facial emotion perception modulates functional brain activation in schizophrenia patients and controls.

Individuals with schizophrenia exhibit deficits in facial emotion processing, which have been associated with abnormalities in visual gaze behavior and functional brain activation. However, the relationship between gaze behavior and brain activation in schizophrenia remains unexamined. Studies in healthy individuals and other clinical samples indicate a relationship between gaze behavior and functional activation in brain regions implicated in facial emotion processing deficits in schizophrenia (e.g., fusiform gyrus), prompting the question of whether a similar relationship exists in schizophrenia. This study examined whether manipulating visual scanpaths during facial emotion perception would modulate functional brain activation in a sample of 23 schizophrenia patients and 26 community controls. Participants underwent functional magnetic resonance imaging (MRI) while viewing pictures of emotional faces. During the typical viewing condition, a fixation cue directed participants' gaze primarily to the eyes and mouth, whereas during the atypical viewing condition gaze was directed to peripheral features. Both viewing conditions elicited a robust response throughout face-processing regions. Typical viewing led to greater activation in visual association cortex including the right inferior occipital gyrus/occipital face area, whereas atypical viewing elicited greater activation in primary visual cortex and regions involved in attentional control. There were no between-groups activation differences in response to faces or interaction between group and gaze manipulation. The results indicate that gaze behavior modulates functional activation in early face-processing regions in individuals with and without schizophrenia, suggesting that abnormal gaze behavior in schizophrenia may contribute to activation abnormalities during facial emotion perception. (PsycINFO Database Record (c) 2019 APA, all rights reserved).