Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting‐state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case–control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case–control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.     

Artifact removal in the context of group ICA: A comparison of single‐subject and group approaches

Independent component analysis (ICA) has been widely applied to identify intrinsic brain networks from fMRI data. Group ICA computes group‐level components from all data and subsequently estimates individual‐level components to recapture intersubject variability. However, the best approach to handle artifacts, which may vary widely among subjects, is not yet clear. In this work, we study and compare two ICA approaches for artifacts removal. One approach, recommended in recent work by the Human Connectome Project, first performs ICA on individual subject data to remove artifacts, and then applies a group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA based artifacts Removal Plus Group ICA (IRPG). A second proposed approach, called Group Information Guided ICA (GIG‐ICA), performs ICA on group data, then removes the group‐level artifact components, and finally performs subject‐specific ICAs using the group‐level non‐artifact components as spatial references. We used simulations to evaluate the two approaches with respect to the effects of data quality, data quantity, variable number of sources among subjects, and spatially unique artifacts. Resting‐state test–retest datasets were also employed to investigate the reliability of functional networks. Results from simulations demonstrate GIG‐ICA has greater performance compared with IRPG, even in the case when single‐subject artifacts removal is perfect and when individual subjects have spatially unique artifacts. Experiments using test–retest data suggest that GIG‐ICA provides more reliable functional networks. Based on high estimation accuracy, ease of implementation, and high reliability of functional networks, we find GIG‐ICA to be a promising approach. Hum Brain Mapp 37:1005–1025, 2016. © 2015 Wiley Periodicals, Inc .     

Identifying dynamic functional connectivity biomarkers using GIG‐ICA: Application to schizophrenia,schizoaffective disorder,and psychotic bipolar disorder

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically‐related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group‐level and subject‐specific connectivity states from DFC. Using resting‐state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole‐brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis‐related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post‐central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD‐unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683–2708, 2017. © 2017 Wiley Periodicals, Inc.     

Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data

The acquisition of both structural MRI (sMRI) and functional MRI (fMRI) data for a given study is a very common practice. However, these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform independent component analysis across image modalities, specifically, gray matter images and fMRI activation images as well as a joint histogram visualization technique. Joint independent component analysis (jICA) is used to decompose a matrix with a given row consisting of an fMRI activation image resulting from auditory oddball target stimuli and an sMRI gray matter segmentation image, collected from the same individual. We analyzed data collected on a group of schizophrenia patients and healthy controls using the jICA approach. Spatially independent joint-components are estimated and resulting components were further analyzed only if they showed a significant difference between patients and controls. The main finding was that group differences in bilateral parietal and frontal as well as posterior temporal regions in gray matter were associated with bilateral temporal regions activated by the auditory oddball target stimuli. A finding of less patient gray matter and less hemodynamic activity for target detection in these bilateral anterior temporal lobe regions was consistent with previous work. An unexpected corollary to this finding was that, in the regions showing the largest group differences, gray matter concentrations were larger in patients vs. controls, suggesting that more gray matter may be related to less functional connectivity in the auditory oddball fMRI task.     

Visual target modulation of functional connectivity networks revealed by self-organizing group ICA

We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study.     

Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data

Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three‐way parallel group independent component analysis (pGICA) fusion method that incorporates the first‐level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject‐wise functional variability and then link it to the mixing matrices of the other two modalities. Simulation results show that the three‐way pGICA provides highly accurate cross‐modality linkage estimation under both weakly and strongly correlated conditions, as well as comparable source estimation under different noise levels. Results using real brain imaging data identified one linked functional–structural–diffusion component associated to differences between schizophrenia and controls. This was replicated in an independent cohort, and the identified components were also correlated with major cognitive domains. Functional network connectivity revealed visual–subcortical and default mode‐cerebellum pairs that discriminate between schizophrenia and controls. Overall, both simulation and real data results support the use of three‐way pGICA to identify multimodal spatiotemporal links and to pursue the study of brain disorders under a single unifying multimodal framework.     

Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia

We present a multivariate alternative to the voxel-based morphometry (VBM) approach called source-based morphometry (SBM), to study gray matter differences between patients and healthy controls. The SBM approach begins with the same preprocessing procedures as VBM. Next, independent component analysis is used to identify naturally grouping, maximally independent sources. Finally, statistical analyses are used to determine the significant sources and their relationship to other variables. The identified "source networks," groups of spatially distinct regions with common covariation among subjects, provide information about localization of gray matter changes and their variation among individuals. In this study, we first compared VBM and SBM via a simulation and then applied both methods to real data obtained from 120 chronic schizophrenia patients and 120 healthy controls. SBM identified five gray matter sources as significantly associated with schizophrenia. These included sources in the bilateral temporal lobes, thalamus, basal ganglia, parietal lobe, and frontotemporal regions. None of these showed an effect of sex. Two sources in the bilateral temporal and parietal lobes showed age-related reductions. The most significant source of schizophrenia-related gray matter changes identified by SBM occurred in the bilateral temporal lobe, while the most significant change found by VBM occurred in the thalamus. The SBM approach found changes not identified by VBM in basal ganglia, parietal, and occipital lobe. These findings show that SBM is a multivariate alternative to VBM, with wide applicability to studying changes in brain structure.     

Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures

Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting‐state fMRI (rs‐fMRI) sample from the PREDICT‐HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole‐brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs‐fMRI data to obtain whole‐brain resting state networks. FNC was defined as the correlation between RSN time‐courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k‐means clustering algorithm. HDgmc individuals spent significantly more time in State‐1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State‐4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State‐1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.     

Four‐way multimodal fusion of 7 T imaging data using an mCCA+jICA model in first‐episode schizophrenia

Acquisition of multimodal brain imaging data for the same subject has become more common leading to a growing interest in determining the intermodal relationships between imaging modalities to further elucidate the pathophysiology of schizophrenia. Multimodal data have previously been individually analyzed and subsequently integrated; however, these analysis techniques lack the ability to examine true modality inter‐relationships. The utilization of a multiset canonical correlation and joint independent component analysis (mCCA + jICA) model for data fusion allows shared or distinct abnormalities between modalities to be examined. In this study, first‐episode schizophrenia patients (n_SZ=19) and matched controls (n_HC=21) completed a resting‐state functional magnetic resonance imaging (fMRI) scan at 7 T. Grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and amplitude of low frequency fluctuation (ALFF) maps were used as features in a mCCA + jICA model. Results of the mCCA + jICA model indicated three joint group‐discriminating components (GM‐CSF, WM‐ALFF, GM‐ALFF) and two modality‐unique group‐discriminating components (GM, WM). The joint component findings are highlighted by GM basal ganglia, somatosensory, parietal lobe, and thalamus abnormalities associated with ventricular CSF volume; WM occipital and frontal lobe abnormalities associated with temporal lobe function; and GM frontal, temporal, parietal, and occipital lobe abnormalities associated with caudate function. These results support and extend major findings throughout the literature using independent single modality analyses. The multimodal fusion of 7 T data in this study provides a more comprehensive illustration of the relationships between underlying neuronal abnormalities associated with schizophrenia than examination of imaging data independently.     

Functional and structural changes in the memory network associated with left temporal lobe epilepsy

Understanding functional plasticity in memory networks associated with temporal lobe epilepsy (TLE) is central to predicting memory decline following surgery. However, the extent of functional reorganization within memory networks remains unclear. In this preliminary study, we used novel analysis methods assessing network‐level changes across the brain during memory task performance in patients with TLE to test the hypothesis that hippocampal functions may not readily shift between hemispheres, but instead may show altered intra‐hemispheric organization with unilateral damage. In addition, we wished to relate functional differences to structural changes along specific fibre pathways associated with memory function. Nine pre‐operative patients with intractable left TLE and 10 healthy controls underwent functional MRI during complex scene encoding. Diffusion tensor imaging was additionally performed in the same patients. In our study, we found no evidence of inter‐hemispheric shifts in memory‐related activity in TLE using standard general linear model analysis. However, tensor independent component analysis revealed significant reductions in functional connectivity between bilateral MTL, occipital and left orbitofrontal regions among others in left TLE. This altered orbitofrontal activity was directly related to measures of fornix tract coherence in patients (P < 0.05). Our results suggest that specific fibre pathways, potentially affected by MTL neurodegeneration, may play a central role in functional plasticity in TLE and highlight the importance of network‐based analysis approaches. Relative to standard model‐based methods, novel objective functional connectivity analyses may offer improved sensitivity to subtle changes in the distribution of memory functions relevant for surgical planning in TLE. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.     

Shared functional network abnormality in patients with temporal lobe epilepsy and their siblings

Aim

Temporal lobe epilepsy is a neurological network disease in which genetics played a greater role than previously appreciated. This study aimed to explore shared functional network abnormalities in patients with sporadic temporal lobe epilepsy and their unaffected siblings.

Methods

Fifty-eight patients with sporadic temporal lobe epilepsy, 13 unaffected siblings, and 30 healthy controls participated in this cross-sectional study. We examined the task-based whole-brain functional network topology and the effective functional connectivity between networks identified by group-independent component analysis.

Results

We observed increased global efficiency, decreased clustering coefficiency, and decreased small-worldness in patients and siblings (p < 0.05, false discovery rate-corrected). The effective network connectivity from the ventral attention network to the limbic system was impaired (p < 0.001, false discovery rate-corrected). These features had higher prevalence in unaffected siblings than in normal population and was not correlated with disease burden. In addition, topological abnormalities had a high intraclass correlation between patients and their siblings.

Conclusion

Patients with temporal lobe epilepsy and their unaffected siblings showed shared topological functional disturbance and the effective functional network connectivity impairment. These abnormalities may contribute to the pathogenesis that promotes the susceptibility of seizures and language decline in temporal lobe epilepsy.     

Altered resting‐state functional network connectivity in profound sensorineural hearing loss infants within an early sensitive period: A group ICA study

Data from both animal models and deaf children provide evidence for that the maturation of auditory cortex has a sensitive period during the first 2–4 years of life. During this period, the auditory stimulation can affect the development of cortical function to the greatest extent. Thus far, little is known about the brain development trajectory after early auditory deprivation within this period. In this study, independent component analysis (ICA) technique was used to detect the characteristics of brain network development in children with bilateral profound sensorineural hearing loss (SNHL) before 3 years old. Seven resting‐state networks (RSN) were identified in 50 SNHL and 36 healthy controls using ICA method, and further their intra‐and inter‐network functional connectivity (FC) were compared between two groups. Compared with the control group, SNHL group showed decreased FC within default mode network, while enhanced FC within auditory network (AUN) and salience network. No significant changes in FC were found in the visual network (VN) and sensorimotor network (SMN). Furthermore, the inter‐network FC between SMN and AUN, frontal network and AUN, SMN and VN, frontal network and VN were significantly increased in SNHL group. The results implicate that the loss and the compensatory reorganization of brain network FC coexist in SNHL infants. It provides a network basis for understanding the brain development trajectory after hearing loss within early sensitive period.     

Resting state and task‐induced deactivation: A methodological comparison in patients with schizophrenia and healthy controls

Changes in the default mode network (DMN) have been linked to multiple neurological disorders including schizophrenia. The anticorrelated relationship the DMN shares with task‐related networks permits the quantification of this network both during task (task‐induced deactivations: TID) and during periods of passive mental activity (extended rest). However, the effects of different methodologies (TID vs. extended rest) for quantifying the DMN in the same clinical population are currently not well understood. Moreover, several different analytic techniques, including independent component analyses (ICA) and seed‐based correlation analyses, exist for examining functional connectivity during extended resting states. The current study compared both methodologies and analytic techniques in a group of patients with schizophrenia (SP) and matched healthy controls. Results indicated that TID analyses, ICA, and seed‐based correlation all consistently identified the midline (anterior and posterior cingulate gyrus) and lateral parietal cortex as core regions of the DMN, as well as more variable involvement of temporal lobe structures. In addition, SP exhibited increased deactivation during task, as well as decreased functional connectivity with frontal regions and increased connectivity with posterior and subcortical areas during periods of extended rest. The increased posterior and reduced anterior connectivity may partially explain some of the cognitive dysfunction and clinical symptoms that are frequently associated with schizophrenia. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.     

Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis

The Functional Image Analysis Contest (FIAC) 2005 dataset was analyzed using BrainVoyager QX. First, we performed a standard analysis of the functional and anatomical data that includes preprocessing, spatial normalization into Talairach space, hypothesis-driven statistics (one- and two-factorial, single-subject and group-level random effects, General Linear Model [GLM]) of the block- and event-related paradigms. Strong sentence and weak speaker group-level effects were detected in temporal and frontal regions. Following this standard analysis, we performed single-subject and group-level (Talairach-based) Independent Component Analysis (ICA) that highlights the presence of functionally connected clusters in temporal and frontal regions for sentence processing, besides revealing other networks related to auditory stimulation or to the default state of the brain. Finally, we applied a high-resolution cortical alignment method to improve the spatial correspondence across brains and re-run the random effects group GLM as well as the group-level ICA in this space. Using spatially and temporally unsmoothed data, this cortex-based analysis revealed comparable results but with a set of spatially more confined group clusters and more differential group region of interest time courses.     

The obese brain: association of body mass index and insulin sensitivity with resting state network functional connectivity

Obesity is a key risk factor for the development of insulin resistance, Type 2 diabetes and associated diseases; thus, it has become a major public health concern. In this context, a detailed understanding of brain networks regulating food intake, including hormonal modulation, is crucial. At present, little is known about potential alterations of cerebral networks regulating ingestive behavior. We used "resting state" functional magnetic resonance imaging to investigate the functional connectivity integrity of resting state networks (RSNs) related to food intake in lean and obese subjects using independent component analysis. Our results showed altered functional connectivity strength in obese compared to lean subjects in the default mode network (DMN) and temporal lobe network. In the DMN, obese subjects showed in the precuneus bilaterally increased and in the right anterior cingulate decreased functional connectivity strength. Furthermore, in the temporal lobe network, obese subjects showed decreased functional connectivity strength in the left insular cortex. The functional connectivity magnitude significantly correlated with body mass index (BMI). Two further RSNs, including brain regions associated with food and reward processing, did not show BMI, but insulin associated functional connectivity strength. Here, the left orbitofrontal cortex and right putamen functional connectivity strength was positively correlated with fasting insulin levels and negatively correlated with insulin sensitivity index. Taken together, these results complement and expand previous functional neuroimaging findings by demonstrating that obesity and insulin levels influence brain function during rest in networks supporting reward and food regulation.     

Investigation of morphometric variability of subthalamic nucleus,red nucleus,and substantia nigra in advanced Parkinson's disease patients using automatic segmentation and PCA‐based analysis

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective surgical therapy to treat Parkinson's disease (PD). Conventional methods employ standard atlas coordinates to target the STN, which, along with the adjacent red nucleus (RN) and substantia nigra (SN), are not well visualized on conventional T1w MRIs. However, the positions and sizes of the nuclei may be more variable than the standard atlas, thus making the pre‐surgical plans inaccurate. We investigated the morphometric variability of the STN, RN and SN by using label‐fusion segmentation results from 3T high resolution T2w MRIs of 33 advanced PD patients. In addition to comparing the size and position measurements of the cohort to the Talairach atlas, principal component analysis (PCA) was performed to acquire more intuitive and detailed perspectives of the measured variability. Lastly, the potential correlation between the variability shown by PCA results and the clinical scores was explored. Hum Brain Mapp 35:4330–4344, 2014. © 2014 Wiley Periodicals, Inc .     

Altered intra‐ and inter‐network functional coupling of resting‐state networks associated with motor dysfunction in stroke

Motor functions are supported through functional integration across the extended motor system network. Individuals following stroke often show deficits on motor performance requiring coordination of multiple brain networks; however, the assessment of connectivity patterns after stroke was still unclear. This study aimed to investigate the changes in intra‐ and inter‐network functional connectivity (FC) of multiple networks following stroke and further correlate FC with motor performance. Thirty‐three left subcortical chronic stroke patients and 34 healthy controls underwent resting‐state functional magnetic resonance imaging. Eleven resting‐state networks were identified via independent component analysis (ICA). Compared with healthy controls, the stroke group showed abnormal FC within the motor network (MN), visual network (VN), dorsal attention network (DAN), and executive control network (ECN). Additionally, the FC values of the ipsilesional inferior parietal lobule (IPL) within the ECN were negatively correlated with the Fugl‐Meyer Assessment (FMA) scores (hand + wrist). With respect to inter‐network interactions, the ipsilesional frontoparietal network (FPN) decreased FC with the MN and DAN; the contralesional FPN decreased FC with the ECN, but it increased FC with the default mode network (DMN); and the posterior DMN decreased FC with the VN. In sum, this study demonstrated the coexistence of intra‐ and inter‐network alterations associated with motor‐visual attention and high‐order cognitive control function in chronic stroke, which might provide insights into brain network plasticity following stroke.     

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time‐varying brain function

The analysis of time‐varying activity and connectivity patterns (i.e., the chronnectome) using resting‐state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high‐order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.     

Electrophysiological basis for the ability of olanzapine to improve verbal memory and functional outcome in patients with schizophrenia: a LORETA analysis of P300

Abnormality of P300 waveforms of event-related potentials (ERPs) has been suggested to represent an aspect of the pathophysiology of schizophrenia. Previous work points to the contribution of altered neural function in discrete brain regions in the left hemisphere to psychotic symptoms and cognitive deficits of schizophrenia. In this study, we sought to determine: 1) if patients with schizophrenia elicit a decreased P300 current source density in brain areas, such as the superior temporal gyrus (STG); 2) if decreased P300 generator density in the left STG is recovered by treatment with the most widely-used antipsychotic drug olanzapine; and 3) if the recovery of P300 source density is associated with improvements of cognitive and functional status. P300 in response to an auditory oddball task, as well as verbal learning memory, psychopathology, and quality of life were evaluated in 16 right-handed patients with schizophrenia before and after treatment with olanzapine for 6 months. ERP data were also obtained from 16 right-handed age and gender-matched normal volunteers. Low resolution electromagnetic tomography (LORETA) analysis was used to obtain current density images of P300. Patients with schizophrenia showed significantly smaller LORETA values in several brain regions in the left side, particularly STG, middle frontal gyrus, and precentral gyrus, compared with control subjects. Six-month treatment with olanzapine significantly increased P300 source density only in the left STG. Positive symptoms, negative symptoms, verbal learning memory, and quality of life were also improved during treatment. Significant correlations were found between the increase in LORETA values of left STG vs. improvements of negative symptoms, as measured by Scale for the Assessment of the Negative Symptoms, and verbal learning memory, as measured by the Japanese Verbal Learning Test. Improvement of quality of life, as evaluated by the Quality of Life Scale, were significantly associated with an increase in LORETA values of middle frontal gyrus, and tended to correlate with that of precentral gyrus. The results of this study suggest that changes in cortical activity, as measured by ERPs, are responsible for the ability of some antipsychotic drugs to improve cognition and functional outcome in patients with schizophrenia.