Transcranial direct current stimulation (tDCS) has been proposed for experimental and therapeutic modulation of regional brain function. Specifically, anodal tDCS of the dorsolateral prefrontal ...cortex (DLPFC) together with cathodal tDCS of the supraorbital region have been associated with improvement of cognition and mood, and have been suggested for the treatment of several neurological and psychiatric disorders. Although modeled mathematically, the distribution, direction, and extent of tDCS-mediated effects on brain physiology are not well understood. The current study investigates whether tDCS of the human prefrontal cortex modulates resting-state network (RSN) connectivity measured by functional magnetic resonance imaging (fMRI). Thirteen healthy subjects underwent real and sham tDCS in random order on separate days. tDCS was applied for 20 min at 2 mA with the anode positioned over the left DLPFC and the cathode over the right supraorbital region. Patterns of resting-state brain connectivity were assessed before and after tDCS with 3 T fMRI, and changes were analyzed for relevant networks related to the stimulation-electrode localizations. At baseline, four RSNs were detected, corresponding to the default mode network (DMN), the left and right frontal-parietal networks (FPNs) and the self-referential network. After real tDCS and compared with sham tDCS, significant changes of regional brain connectivity were found for the DMN and the FPNs both close to the primary stimulation site and in connected brain regions. These findings show that prefrontal tDCS modulates resting-state functional connectivity in distinct functional networks of the human brain.
Abstract Functional magnetic resonance imaging (fMRI) of default mode network (DMN) brain activity during resting is recently gaining attention as a potential noninvasive biomarker to diagnose ...incipient Alzheimer's disease. The aim of this study was to determine which method of data processing provides highest diagnostic power and to define metrics to further optimize the diagnostic value. fMRI was acquired in 21 healthy subjects, 17 subjects with mild cognitive impairment and 15 patients with Alzheimer's disease (AD) and data evaluated both with volumes of interest (VOI)-based signal time course evaluations and independent component analyses (ICA). The first approach determines the amount of DMN region interconnectivity (as expressed with correlation coefficients); the second method determines the magnitude of DMN coactivation. Apolipoprotein E (ApoE) genotyping was available in 41 of the subjects examined. Diagnostic power (expressed as accuracy) of data of a single DMN region in independent component analyses was 64%, that of a single correlation of time courses between 2 DMN regions was 71%, respectively. With multivariate analyses combining both methods of analysis and data from various regions, accuracy could be increased to 97% (sensitivity 100%, specificity 95%). In nondemented subjects, no significant differences in activity within DMN could be detected comparing ApoE ε4 allele carriers and ApoE ε4 allele noncarriers. However, there were some indications that fMRI might yield useful information given a larger sample. Time course correlation analyses seem to outperform independent component analyses in the identification of patients with Alzheimer's disease. However, multivariate analyses combining both methods of analysis by considering the activity of various parts of the DMN as well as the interconnectivity between these regions are required to achieve optimal and clinically acceptable diagnostic power.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical ...brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Brain tissue changes in autism spectrum disorders seem to be rather subtle and widespread than anatomically distinct. Therefore a multimodal, whole brain imaging technique appears to be an ...appropriate approach to investigate whether alterations in white and gray matter integrity relate to consistent changes in functional resting state connectivity in individuals with high functioning autism (HFA). We applied diffusion tensor imaging (DTI), voxel-based morphometry (VBM) and resting state functional connectivity magnetic resonance imaging (fcMRI) to assess differences in brain structure and function between 12 individuals with HFA (mean age 35.5, SD 11.4, 9 male) and 12 healthy controls (mean age 33.3, SD 9.0, 8 male). Psychological measures of empathy and emotionality were obtained and correlated with the most significant DTI, VBM and fcMRI findings. We found three regions of convergent structural and functional differences between HFA participants and controls. The right temporo-parietal junction area and the left frontal lobe showed decreased fractional anisotropy (FA) values along with decreased functional connectivity and a trend towards decreased gray matter volume. The bilateral superior temporal gyrus displayed significantly decreased functional connectivity that was accompanied by the strongest trend of gray matter volume decrease in the temporal lobe of HFA individuals. FA decrease in the right temporo-parietal region was correlated with psychological measurements of decreased emotionality. In conclusion, our results indicate common sites of structural and functional alterations in higher order association cortex areas and may therefore provide multimodal imaging support to the long-standing hypothesis of autism as a disorder of impaired higher-order multisensory integration.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer's disease (AD). The use of DTI as a biomarker, however, depends on its ...applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Resting state functional magnetic resonance imaging (fMRI) reveals a distinct network of correlated brain function representing a default mode state of the human brain. The underlying structural ...basis of this functional connectivity pattern is still widely unexplored. We combined fractional anisotropy measures of fiber tract integrity derived from diffusion tensor imaging (DTI) and resting state fMRI data obtained at 3 Tesla from 20 healthy elderly subjects (56 to 83 years of age) to determine white matter microstructure underlying default mode connectivity. We hypothesized that the functional connectivity between the posterior cingulate and hippocampus from resting state fMRI data would be associated with the white matter microstructure in the cingulate bundle and fiber tracts connecting posterior cingulate gyrus with lateral temporal lobes, medial temporal lobes, and precuneus. This was demonstrated at the p<0.001 level using a voxel-based multivariate analysis of covariance (MANCOVA) approach. In addition, we used a data-driven technique of joint independent component analysis (ICA) that uncovers spatial pattern that are linked across modalities. It revealed a pattern of white matter tracts including cingulate bundle and associated fiber tracts resembling the findings from the hypothesis-driven analysis and was linked to the pattern of default mode network (DMN) connectivity in the resting state fMRI data. Our findings support the notion that the functional connectivity between the posterior cingulate and hippocampus and the functional connectivity across the entire DMN is based on distinct pattern of anatomical connectivity within the cerebral white matter.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•We tested the relationship between cardiac interoceptive awareness and neural responses preceding decision-making.•Cardiac interoceptive awareness was associated with activity in the right anterior ...insula and the left postcentral gyrus.•Right anterior insula activity was related to decision-making performance in accurate cardiac interoceptive awareness.•These findings support a relationship between body feedback and decision-making.
The current study examined the relationship between conscious perception of somatic feedback (interoceptive awareness) and neural responses preceding decision-making. Previous research has suggested that decision-making is influenced by body signals from the periphery or the central representation of the periphery. Using event-related fMRI, participants whose interoceptive awareness was assessed using a heartbeat perception paradigm performed the Iowa Gambling Task. The results show a positive relationship between the degree of interoceptive awareness and selection related activity in the right anterior insula and the left postcentral gyrus. Neural activity within the right anterior insula was associated with decision-making performance only in individuals with accurate but not in those with non-accurate interoceptive awareness. These findings support the role of somatic feedback in decision-making processes. They indicate that the right anterior insula holds a representation of somatic markers and that these are more strongly processed with increased interoceptive awareness.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Cross-sectional studies using diffusion tensor imaging (DTI) suggest decline of the integrity of intracortically projecting fiber tracts with aging and in neurodegenerative diseases, such as ...Alzheimer's disease (AD). Longitudinal studies on the change of fiber tract integrity in normal and pathological aging are still rare. Here, we prospectively studied 11 healthy elderly subjects and 14 subjects with amnestic mild cognitive impairment (MCI), a clinical risk group for AD, using high-resolution DTI and MRI at baseline and after 13 to 16 months follow-up. Fractional anisotropy (FA), a DTI measure of fiber tract integrity, was compared across time points and groups using a repeated measures linear model and tract based spatial statistics. Additionally, we determined rates of grey matter and white matter atrophy using automated deformation based morphometry. Healthy elderly subjects showed decline of FA in intracortical projecting fiber tracts, such as corpus callosum, superior longitudinal fasciculus, uncinate fasciculus, inferior fronto-occipital fasciculus, and cingulate bundle (p < 0.05, corrected for multiple comparisons). MCI subjects showed significant FA decline predominantly in the anterior corpus callosum (p < 0.05, corrected for multiple comparisons). Grey and white matter atrophy involved prefrontal, parietal, and temporal lobe areas in controls and prefrontal, cingulate, and parietal lobe areas in MCI subjects and agreed with the pattern of fiber tract changes. Our findings indicate that DTI allows detection of microstructural changes in subcortical fiber tracts over time that are related to aging as well as to early stages of AD type neurodegeneration. The underlying mechanisms for these changes are unknown.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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