Long-term antipsychotic use and brain changes in schizophrenia - a systematic review and meta-analysis.

OBJECTIVE: The association between long-term antipsychotic treatment and changes in brain structure in schizophrenia is unclear. Our aim was to conduct a systematic review and a meta-analysis on long-term antipsychotic effects on brain structures in schizophrenia focusing on studies with at least 2 years of follow-up between MRI scans. DESIGN: Studies were systematically collected using 4 databases, and we also contacted authors for unpublished data. We calculated correlations between antipsychotic dose and/or type and brain volumetric changes and used random effect meta-analysis to study correlations by brain area. RESULTS: Thirty-one publications from 16 samples fulfilled our inclusion criteria. In meta-analysis, higher antipsychotic exposure associated statistically significantly with parietal lobe decrease (studies, n = 4; r = -.14, p = .013) and with basal ganglia increase (n = 4; r = .10, p = .044). Most of the reported correlations in the original studies were statistically nonsignificant. There were no clear differences between typical and atypical exposure and brain volume change. The studies were often small and highly heterogeneous in their methods and seldom focused on antipsychotic medication and brain changes as the main subject. CONCLUSIONS: Antipsychotic medication may associate with brain structure changes. More long-term follow-up studies taking into account illness severity measures are needed to make definitive conclusions.

Attention and Working Memory in Adolescents with Autism Spectrum Disorder: A Functional MRI Study.

The present study examined attention and memory load-dependent differences in the brain activation and deactivation patterns between adolescents with autism spectrum disorders (ASDs) and typically developing (TD) controls using functional magnetic resonance imaging. Attentional (0-back) and working memory (WM; 2-back) processing and load differences (0 vs. 2-back) were analysed. WM-related areas activated and default mode network deactivated normally in ASDs as a function of task load. ASDs performed the attentional 0-back task similarly to TD controls but showed increased deactivation in cerebellum and right temporal cortical areas and weaker activation in other cerebellar areas. Increasing task load resulted in multiple responses in ASDs compared to TD and in inadequate modulation of brain activity in right insula, primary somatosensory, motor and auditory cortices. The changes during attentional task may reflect compensatory mechanisms enabling normal behavioral performance. The inadequate memory load-dependent modulation of activity suggests diminished compensatory potential in ASD.

Reading, listening and memory-related brain activity in children with early-stage temporal lobe epilepsy of unknown cause-an fMRI study.

BACKGROUND AND AIMS: The changes in functional brain organization associated with paediatric epilepsy are largely unknown. Since children with epilepsy are at risk of developing learning difficulties even before or shortly after the onset of epilepsy, we assessed the functional organization of memory and language in paediatric patients with temporal lobe epilepsy (TLE) at an early stage in epilepsy. METHODS: Functional magnetic resonance imaging was used to measure the blood oxygenation level-dependent (BOLD) response to four cognitive tasks measuring reading, story listening, memory encoding and retrieval in a population-based group of children with TLE of unknown cause (n = 21) and of normal intelligence and a healthy age and gender-matched control group (n = 21). RESULTS: Significant BOLD response differences were found only in one of the four tasks. In the story listening task, significant differences were found in the right hemispheric temporal structures, thalamus and basal ganglia. Both activation and deactivation differed significantly between the groups, activation being increased and deactivation decreased in the TLE group. Furthermore, the patients with abnormal electroencephalograms (EEGs) showed significantly increased activation bilaterally in the temporal structures, basal ganglia and thalamus relative to those with normal EEGs. The patients with normal interictal EEGs had a significantly stronger deactivation than those with abnormal EEGs or the controls, the differences being located outside the temporal structures. CONCLUSIONS: Our results suggest that TLE entails a widespread disruption of brain networks. This needs to be taken into consideration when evaluating learning abilities in patients with TLE. The thalamus seems to play an active role in TLE. The changes in deactivation may reflect neuronal inhibition.

DTI abnormalities in adults with past history of attention deficit hyperactivity disorder: a tract-based spatial statistics study.

BACKGROUND: Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique enabling visualization and measurement of white matter tracts. Attention deficit hyperactivity disorder (ADHD) has been studied with DTI earlier with variable results, yet there is little research on remitted ADHD. PURPOSE: To compare the brain white matter between ADHD drug naïve subjects whose ADHD symptoms have mostly subsided and healthy controls. MATERIAL AND METHODS: Tract-based spatial statistics (TBSS) was used to compare 30 subjects with adolescent ADHD with control subjects at the age of 22-23 years. The study population was derived from a population-based Northern Finland Birth Cohort 1986. Fractional anisotropy (FA), mean diffusivity (MD), and measures of diffusion direction (λ1-3) were calculated. Permutation testing was used to test for differences in mean values of FA, MD, and λ1-3 between the groups. The results were corrected for multiple comparisons across the whole white matter skeleton. RESULTS: The ADHD group showed increased FA related to decreased radial diffusivity in the left forceps minor (P < 0.05). In the vicinity along the same tract, axial diffusion was significantly decreased without any significant effect on FA. No between-group difference in MD was observed. Regressor analysis revealed no gender-, IQ- or GAF-related changes. After removal of left handed subjects the statistical significance was only barely lost. CONCLUSION: In a setting with remitted ADHD, the results may represent a compensatory mechanism in the left forceps minor.

Longitudinal changes in total brain volume in schizophrenia: relation to symptom severity, cognition and antipsychotic medication.

Studies show evidence of longitudinal brain volume decreases in schizophrenia. We studied brain volume changes and their relation to symptom severity, level of function, cognition, and antipsychotic medication in participants with schizophrenia and control participants from a general population based birth cohort sample in a relatively long follow-up period of almost a decade. All members of the Northern Finland Birth Cohort 1966 with any psychotic disorder and a random sample not having psychosis were invited for a MRI brain scan, and clinical and cognitive assessment during 1999-2001 at the age of 33-35 years. A follow-up was conducted 9 years later during 2008-2010. Brain scans at both time points were obtained from 33 participants with schizophrenia and 71 control participants. Regression models were used to examine whether brain volume changes predicted clinical and cognitive changes over time, and whether antipsychotic medication predicted brain volume changes. The mean annual whole brain volume reduction was 0.69% in schizophrenia, and 0.49% in controls (p = 0.003, adjusted for gender, educational level, alcohol use and weight gain). The brain volume reduction in schizophrenia patients was found especially in the temporal lobe and periventricular area. Symptom severity, functioning level, and decline in cognition were not associated with brain volume reduction in schizophrenia. The amount of antipsychotic medication (dose years of equivalent to 100 mg daily chlorpromazine) over the follow-up period predicted brain volume loss (p = 0.003 adjusted for symptom level, alcohol use and weight gain). In this population based sample, brain volume reduction continues in schizophrenia patients after the onset of illness, and antipsychotic medications may contribute to these reductions.

Brain structure in different psychosis risk groups in the Northern Finland 1986 birth cohort.

We tested the hypothesis that family risk for psychosis (FR) and clinical risk for psychosis (CR) are associated with structural brain abnormalities, with increased deficits in those at both family risk and clinical risk for psychosis (FRCR). The study setting was the Oulu Brain and Mind Study, with subjects drawn from the Northern Finland 1986 Birth Cohort (n=9479) using register and questionnaire based screening, and interviews using the Structured Interview for Prodromal Symptoms. After this procedure, 172 subjects were included in the study, classified as controls (n=73) and three risk groups: FR excluding CR (FR, n=60), CR without FR (CR, n=26), and individuals at both FR and CR (FRCR, n=13). T1-weighted brain scans were acquired and processed in a voxel-based analysis using permutation-based statistics. In the comparison between FRCR versus controls, we found lower grey matter volume (GMV) in a cluster (1689 voxels at -4.00, -72.00, -18.00mm) covering both cerebellar hemispheres and the vermis. This cluster was subsequently used as a mask to extract mean GMV in all four groups: FR had a volume intermediate between controls and FRCR. Within FRCR there was an association between cerebellar cluster brain volume and motor function. These findings are consistent with an evolving pattern of cerebellar deficits in psychosis risk with the most pronounced deficits in those at highest risk of psychosis.

Infra-slow EEG fluctuations are correlated with resting-state network dynamics in fMRI.

Ongoing neuronal activity in the CNS waxes and wanes continuously across widespread spatial and temporal scales. In the human brain, these spontaneous fluctuations are salient in blood oxygenation level-dependent (BOLD) signals and correlated within specific brain systems or "intrinsic-connectivity networks." In electrophysiological recordings, both the amplitude dynamics of fast (1-100 Hz) oscillations and the scalp potentials per se exhibit fluctuations in the same infra-slow (0.01-0.1 Hz) frequency range where the BOLD fluctuations are conspicuous. While several lines of evidence show that the BOLD fluctuations are correlated with fast-amplitude dynamics, it has remained unclear whether the infra-slow scalp potential fluctuations in full-band electroencephalography (fbEEG) are related to the resting-state BOLD signals. We used concurrent fbEEG and functional magnetic resonance imaging (fMRI) recordings to address the relationship of infra-slow fluctuations (ISFs) in scalp potentials and BOLD signals. We show here that independent components of fbEEG recordings are selectively correlated with subsets of cortical BOLD signals in specific task-positive and task-negative, fMRI-defined resting-state networks. This brain system-specific association indicates that infra-slow scalp potentials are directly associated with the endogenous fluctuations in neuronal activity levels. fbEEG thus yields a noninvasive, high-temporal resolution window into the dynamics of intrinsic connectivity networks. These results support the view that the slow potentials reflect changes in cortical excitability and shed light on neuronal substrates underlying both electrophysiological and behavioral ISFs.

Altered resting-state activity in seasonal affective disorder.

At present, our knowledge about seasonal affective disorder (SAD) is based mainly up on clinical symptoms, epidemiology, behavioral characteristics and light therapy. Recently developed measures of resting-state functional brain activity might provide neurobiological markers of brain disorders. Studying functional brain activity in SAD could enhance our understanding of its nature and possible treatment strategies. Functional network connectivity (measured using ICA-dual regression), and amplitude of low-frequency fluctuations (ALFF) were measured in 45 antidepressant-free patients (39.78 ± 10.64, 30 ♀, 15 ♂) diagnosed with SAD and compared with age-, gender- and ethnicity-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging. After correcting for Type 1 error at high model orders (inter-RSN correction), SAD patients showed significantly increased functional connectivity in 11 of the 47 identified RSNs. Increased functional connectivity involved RSNs such as visual, sensorimotor, and attentional networks. Moreover, our results revealed that SAD patients compared with HCs showed significant higher ALFF in the visual and right sensorimotor cortex. Abnormally altered functional activity detected in SAD supports previously reported attentional and psychomotor symptoms in patients suffering from SAD. Further studies, particularly under task conditions, are needed in order to specifically investigate cognitive deficits in SAD.

Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing.

In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.

On applicability of PCA, voxel-wise variance normalization and dimensionality assumptions for sliding temporal window sICA in resting-state fMRI.

Subject-level resting-state fMRI (RS-fMRI) spatial independent component analysis (sICA) may provide new ways to analyze the data when performed in the sliding time window. However, whether principal component analysis (PCA) and voxel-wise variance normalization (VN) are applicable pre-processing procedures in the sliding-window context, as they are for regular sICA, has not been addressed so far. Also model order selection requires further studies concerning sliding-window sICA. In this paper we have addressed these concerns. First, we compared PCA-retained subspaces concerning overlapping parts of consecutive temporal windows to answer whether in-window PCA and VN can confound comparisons between sICA analyses in consecutive windows. Second, we compared the PCA subspaces between windowed and full data to assess expected comparability between windowed and full-data sICA results. Third, temporal evolution of dimensionality estimates in RS-fMRI data sets was monitored to identify potential challenges in model order selection in a sliding-window sICA context. Our results illustrate that in-window VN can be safely used, in-window PCA is applicable with most window widths and that comparisons between windowed and full data should not be performed from a subspace similarity point of view. In addition, our studies on dimensionality estimates demonstrated that there are sustained, periodic and very case-specific changes in signal-to-noise ratio within RS-fMRI data sets. Consequently, dimensionality estimation is needed for well-founded model order determination in the sliding-window case. The observed periodic changes correspond to a frequency band of ≤0.1 Hz, which is commonly associated with brain activity in RS-fMRI and become on average most pronounced at window widths of 80 and 60 time points (144 and 108 s, respectively). Wider windows provided only slightly better comparability between consecutive windows, and 60 time point or shorter windows also provided the best comparability with full-data results. Further studies are needed to determine the cause for dimensionality variations.

Connectivity disruptions in resting-state functional brain networks in children with temporal lobe epilepsy.

Functional resting-state connectivity has been shown to be altered in certain adult epilepsy populations, but few connectivity studies have been performed on pediatric epilepsy patients. Here functional connectivity was measured in pediatric, non-lesional temporal lobe epilepsy patients with normal intelligence and compared with that in age and gender-matched healthy controls using the independent component analysis method. We hypothesized that children with non-lesional temporal lobe epilepsy have disrupted functional connectivity within resting-state networks. Significant differences were demonstrated between the two groups, pointing to a decrease in connectivity. When the results were analyzed according to the interictal electroencephalogram findings, however, the connectivity disruptions were seen in different networks. In addition, increased connectivity and abnormally anti-correlated thalamic activity was detected only in the patients with abnormal electroencephalograms. In summary, connectivity disruptions are already to be seen at an early stage of epilepsy, and epileptiform activity seems to affect connectivity differently. The results indicate that interictal epileptiform activity may lead to reorganization of the resting-state brain networks, but further studies would be needed in order to understand the pathophysiology behind this phenomenon.

Valence scaling of dynamic facial expressions is altered in high-functioning subjects with autism spectrum disorders: an fMRI study.

FMRI was performed with the dynamic facial expressions fear and happiness. This was done to detect differences in valence processing between 25 subjects with autism spectrum disorders (ASDs) and 27 typically developing controls. Valence scaling was abnormal in ASDs. Positive valence induces lower deactivation and abnormally strong activity in ASD in multiple regions. Negative valence increased deactivation in visual areas in subjects with ASDs. The most marked differences between valences focus on fronto-insular and temporal regions. This supports the idea that subjects with ASDs may have difficulty in passive processing of the salience and mirroring of expressions. When the valence scaling of brain activity fails, in contrast to controls, these areas activate and/or deactivate inappropriately during facial stimuli presented dynamically.

Directional connectivity of resting state human fMRI data using cascaded ICA-PDC analysis.

BACKGROUND: Directional connectivity measures, such as partial directed coherence (PDC), give us means to explore effective connectivity in the human brain. By utilizing independent component analysis (ICA), the original data-set reduction was performed for further PDC analysis. PURPOSE: To test this cascaded ICA-PDC approach in causality studies of human functional magnetic resonance imaging (fMRI) data. MATERIAL AND METHODS: Resting state group data was imaged from 55 subjects using a 1.5 T scanner (TR 1800 ms, 250 volumes). Temporal concatenation group ICA in a probabilistic ICA and further repeatability runs (n = 200) were overtaken. The reduced data-set included the time series presentation of the following nine ICA components: secondary somatosensory cortex, inferior temporal gyrus, intracalcarine cortex, primary auditory cortex, amygdala, putamen and the frontal medial cortex, posterior cingulate cortex and precuneus, comprising the default mode network components. Re-normalized PDC (rPDC) values were computed to determine directional connectivity at the group level at each frequency. RESULTS: The integrative role was suggested for precuneus while the role of major divergence region may be proposed to primary auditory cortex and amygdala. CONCLUSION: This study demonstrates the potential of the cascaded ICA-PDC approach in directional connectivity studies of human fMRI.

Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity.

Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. Altering ICA dimensionality (model order) estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD) patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference) then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.

Effects of repeatability measures on results of fMRI sICA: a study on simulated and real resting-state effects.

Spatial independent components analysis (sICA) has become a widely applied data-driven method for fMRI data, especially for resting-state studies. These sICA approaches are often based on iterative estimation algorithms and there are concerns about accuracy due to noise. Repeatability measures such as ICASSO, RAICAR and ARABICA have been introduced as remedies but information on their effects on estimates is limited. The contribution of this study was to provide more of such information and test if the repeatability analyses are necessary. We compared FastICA-based ordinary and repeatability approaches concerning mixing vector estimates. Comparisons included original FastICA, FSL4 Melodic FastICA and original and modified ICASSO. The effects of bootstrapping and convergence threshold were evaluated. The results show that there is only moderate improvement due to repeatability measures and only in the bootstrapping case. Bootstrapping attenuated power from time courses of resting-state network related ICs at frequencies higher than 0.1 Hz and made subsets of low frequency oscillations more emphasized IC-wise. The convergence threshold did not have a significant role concerning the accuracy of estimates. The performance results suggest that repeatability measures or strict converge criteria might not be needed in sICA analyses of fMRI data. Consequently, the results in existing sICA fMRI literature are probably valid in this sense. A decreased accuracy of original bootstrapping ICASSO was observed and corrected by using centrotype mixing estimates but the results warrant for thorough evaluations of data-driven methods in general. Also, given the fMRI-specific considerations, further development of sICA methods is strongly encouraged.

A sliding time-window ICA reveals spatial variability of the default mode network in time.

Recent evidence on resting-state networks in functional (connectivity) magnetic resonance imaging (fcMRI) suggests that there may be significant spatial variability of activity foci over time. This study used a sliding time window approach with the spatial domain-independent component analysis (SliTICA) to detect spatial maps of resting-state networks over time. The study hypothesis was that the spatial distribution of a functionally connected network would present marked variability over time. The spatial stability of successive sliding-window maps of the default mode network (DMN) from fcMRI data of 12 participants imaged in the resting state was analyzed. Control measures support previous findings on the stability of independent component analysis in measuring sliding-window sources accurately. The spatial similarity of successive DMN maps varied over time at low frequencies and presented a 1/f power spectral pattern. SliTICA maps show marked temporal variation within the DMN; a single voxel was detected inside a group DMN map in maximally 82% of time windows. Mapping of incidental connectivity reveals centrifugally increasing connectivity to the brain cortex outside the DMN core areas. In conclusion, SliTICA shows marked spatial variance of DMN activity in time, which may offer a more comprehensive measurement of the overall functional activity of a network.

Alterations in regional homogeneity of baseline brain activity in pediatric temporal lobe epilepsy.

Recent findings on intracortical EEG measurements show that the synchrony of localized neuronal networks is altered in epileptogenesis, leading to generalized seizure activity via connector hubs in the neuronal networks. Regional homogeneity (ReHo) analysis of blood oxygen level-dependent (BOLD) signals has demonstrated localized signal synchrony and disease-related alterations in a number of instances. We wanted to find out whether the ReHo of resting-state activity can be used to detect regional signal synchrony alterations in children with non-lesional temporal lobe epilepsy (TLE). Twenty-one TLE patients were compared with age and gender-matched healthy controls. Significantly increased ReHo was discovered in the posterior cingulate gyrus and the right medial temporal lobe of the patients, and they also had significantly decreased ReHo in the cerebellum compared with the healthy controls. However, the alterations in ReHo differed between the patients with normal and abnormal interictal EEGs, the latter showing significantly increased ReHo in the right fusiform gyrus and significantly decreased ReHo in the right medial frontal gyrus relative to the controls, while those with normal EEGs had significantly increased ReHo in the right inferior temporal gyrus and the left posterior cingulate gyrus. We conclude that altered BOLD signal synchrony can be detected in the cerebral and cerebellar cortices of children with TLE even in the absence of interictal EEG abnormalities.

Age-Related Differences in Functional Nodes of the Brain Cortex - A High Model Order Group ICA Study.

Functional MRI measured with blood oxygen dependent (BOLD) contrast in the absence of intermittent tasks reflects spontaneous activity of so-called resting state networks (RSN) of the brain. Group level independent component analysis (ICA) of BOLD data can separate the human brain cortex into 42 independent RSNs. In this study we evaluated age-related effects from primary motor and sensory, and, higher level control RSNs. One hundred sixty-eight healthy subjects were scanned and divided into three groups: 55 adolescents (ADO, 13.2 ± 2.4 years), 59 young adults (YA, 22.2 ± 0.6 years), and 54 older adults (OA, 42.7 ± 0.5 years), all with normal IQ. High model order group probabilistic ICA components (70) were calculated and dual-regression analysis was used to compare 21 RSN's spatial differences between groups. The power spectra were derived from individual ICA mixing matrix time series of the group analyses for frequency domain analysis. We show that primary sensory and motor networks tend to alter more in younger age groups, whereas associative and higher level cognitive networks consolidate and re-arrange until older adulthood. The change has a common trend: both spatial extent and the low frequency power of the RSN's reduce with increasing age. We interpret these result as a sign of normal pruning via focusing of activity to less distributed local hubs.

Functional mapping of dynamic happy and fearful facial expression processing in adolescents.

This paper assessed the neural systems involved in processing of dynamic facial expressions in adolescents. The processing of facial expressions changes as a function of age, and it is thus important to understand how healthy adolescent subjects process dynamic facial expressions prior to analyzing disease-related changes. We hypothesized that viewing of dynamic facial expressions with opposing valences (happy vs. fearful) induces differential activations and deactivations in the brain. 27 healthy adolescents (9 female, 18 male, mean age = 14.5 years; age range 11.6-17.3 years) were examined by using the ASSQ and K-SADS-PL and scanned with 1.5-T fMRI during viewing of dynamic facial expressions and mosaic control images. The stimuli activated the same areas as previously seen in dynamic facial expression in adults. Our results indicated that opposing-valence dynamic facial expressions had differential effects on many cortical structures but not on subcortical limbic structures. The mirror neuron system is activated more during viewing of fearful compared to happy expressions in bilateral inferior frontal gyrus (IFG) and superior temporal sulcus (STS) left dominantly. We also detected more deactivation in the ventral anterior cingulate gyrus (ACG), showing more automated attentional processing of fearful expressions during passive viewing. Females were found to deactivate the right frontal pole more than male adolescents during happy facial expressions, while there were no differences in fear processing between genders. No clear gender or age effects were detected. In conclusion fear induces stronger responses in attention and mirror neurons probably related to fear contagion.

Alterations in regional homogeneity of resting-state brain activity in autism spectrum disorders.

Measures assessing resting-state brain activity with blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) can reveal cognitive disorders at an early stage. Analysis of regional homogeneity (ReHo) measures the local synchronization of spontaneous fMRI signals and has been successfully utilized in detecting alterations in subjects with attention-deficit hyperactivity disorder (ADHD), depression, schizophrenia, Parkinson's disease and Alzheimer's dementia. Resting-state brain activity was investigated in 28 adolescents with autism spectrum disorders (ASD) and 27 typically developing controls being imaged with BOLD fMRI and analyzed with the ReHo method. The hypothesis was that ReHo of resting-state brain activity would be different between ASD subjects and controls in brain areas previously shown to display functional alterations in stimulus or task based fMRI studies. Compared with the controls, the subjects with ASD had significantly decreased ReHo in right superior temporal sulcus region, right inferior and middle frontal gyri, bilateral cerebellar crus I, right insula and right postcentral gyrus. Significantly increased ReHo was discovered in right thalamus, left inferior frontal and anterior subcallosal gyrus and bilateral cerebellar lobule VIII. We conclude that subjects with ASD have right dominant ReHo alterations of resting-state brain activity, i.e., areas known to exhibit abnormal stimulus or task related functionality. Our results demonstrate that there is potential in utilizing the ReHo method in fMRI analyses of ASD.