Juha Nikkinen

Functional segmentation of the brain cortex using high model order group PICA

Baseline activity of resting state brain networks (RSN) in a resting subject has become one of the fastest growing research topics in neuroimaging. It has been shown that up to 12 RSNs can be differentiated using an independent component analysis (ICA) of the blood oxygen level dependent (BOLD) resting state data. In this study, we investigate how many RSN signal sources can be separated from the entire brain cortex using high dimension ICA analysis from a group dataset. Group data from 55 subjects was analyzed using temporal concatenation and a probabilistic independent component analysis algorithm. ICA repeatability testing verified that 60 of the 70 computed components were robustly detectable. Forty‐two independent signal sources were identifiable as RSN, and 28 were related to artifacts or other noninterest sources (non‐RSN). The depicted RSNs bore a closer match to functional neuroanatomy than the previously reported RSN components. The non‐RSN sources have significantly lower temporal intersource connectivity than the RSN (P < 0.0003). We conclude that the high model order ICA of the group BOLD data enables functional segmentation of the brain cortex. The method enables new approaches to causality and connectivity analysis with more specific anatomical details. Hum Brain Mapp, 2009.

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, Parkinsons disease and Alzheimers 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.

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.

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.

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.

Preoperative localization of the sensorimotor area using independent component analysis of resting-state fMRI

Analysis of resting-state functional magnetic resonance imaging (fMRI) data is based on detecting low-frequency signal fluctuations in functionally connected brain areas. These synchronous fluctuations in resting-state networks have been observed in several studies with healthy subjects. In this study, we explored if independent component analysis (ICA) can be used to localize the sensorimotor area from resting-state fMRI data in patients with brain tumors. Finger-tapping activation task and resting-state blood-oxygenation-level-dependent fMRI data were acquired from 8 patients with brain tumors and 10 healthy volunteers. Sensorimotor task independent components (IC(task)) were used to verify resting-state independent components (IC(rest)) individually. In addition, sensorimotor IC(rest)s were compared between the groups and no significant differences were detected in volume, spatial correlation or temporal correlation. These results show that it is possible to localize a sensorimotor area from resting-state data using ICA in patients with brain tumors. This offers a complementary method for assessing the sensorimotor area in subjects with brain tumors who have difficulties in performing motor paradigms.

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.

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.

GroupICA dual regression analysis of resting state networks in a behavioral variant of frontotemporal dementia

Functional MRI studies have revealed changes in default-mode and salience networks in neurodegenerative dementias, especially in Alzheimers disease (AD). The purpose of this study was to analyze the whole brain cortex resting state networks (RSNs) in patients with behavioral variant frontotemporal dementia (bvFTD) by using resting state functional MRI (rfMRI). The group specific RSNs were identified by high model order independent component analysis (ICA) and a dual regression technique was used to detect between-group differences in the RSNs with p < 0.05 threshold corrected for multiple comparisons. A y-concatenation method was used to correct for multiple comparisons for multiple independent components, gray matter differences as well as the voxel level. We found increased connectivity in several networks within patients with bvFTD compared to the control group. The most prominent enhancement was seen in the right frontotemporal area and insula. A significant increase in functional connectivity was also detected in the left dorsal attention network (DAN), in anterior paracingulate—a default mode sub-network as well as in the anterior parts of the frontal pole. Notably the increased patterns of connectivity were seen in areas around atrophic regions. The present results demonstrate abnormal increased connectivity in several important brain networks including the DAN and default-mode network (DMN) in patients with bvFTD. These changes may be associated with decline in executive functions and attention as well as apathy, which are the major cognitive and neuropsychiatric defects in patients with frontotemporal dementia.

White matter in autism spectrum disorders – evidence of impaired fiber formation

Background Diffusion tensor imaging (DTI) enables measurements and visualization of the microstructure of neural fiber tracts. The existing literature on autism spectrum disorders (ASDs) and DTI is heterogenous both regarding methodology and results. Purpose To compare brain white matter of high-functioning individuals with ASDs and controls. Material and Methods Tract-based spatial statistics (TBSS), a voxel-based approach to DTI, was used to compare 27 subjects with ASDs (mean age 14.7 years, range 11.4–17.6 years, 20 boys, 7 girls) and 26 control subjects (mean age 14.5 years, range 11.7–17.3 years, 17 boys, 9 girls). Mean fractional anisotropy (FA) image (skeleton) was created and each subjects aligned FA data were then projected onto this skeleton. Voxelwise cross-subject statistics on the skeletonized FA data, mean diffusivity (MD), and measures of diffusion direction were calculated. Importantly, the data were corrected across the whole image instead of using ROI-based methods. Results The ASD group showed significantly greater FA (P < 0.05, corrected) in the area containing clusters of optic radiation and the right inferior fronto-occipital fasciculus (iFOF). In the same area, λ3 (representing transverse diffusion) was significantly reduced in the ASD group. No age-related changes were found. Conclusion The results suggest that the reduced transverse diffusion within the iFOF is related to abnormal information flow between the insular salience processing areas and occipital visual areas.