Thomas Meindl

Prefrontal Transcranial Direct Current Stimulation Changes Connectivity of Resting-State Networks during fMRI

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.

Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimers 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.

Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer's disease

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 Alzheimers 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 Alzheimers 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 Alzheimers 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.

White matter microstructure underlying default mode network connectivity in the human brain

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.

Test–retest reproducibility of the default-mode network in healthy individuals

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) time‐series reveals distinct coactivation patterns in the resting brain representing spatially coherent spontaneous fluctuations of the fMRI signal. Among these patterns, the so‐called default‐mode network (DMN) has been attributed to the ongoing mental activity of the brain during wakeful resting state. Studies suggest that many neuropsychiatric diseases disconnect brain areas belonging to the DMN. The potential use of the DMN as functional imaging marker for individuals at risk for these diseases, however, requires that the components of the DMN are reproducible over time in healthy individuals. In this study, we assessed the reproducibility of the DMN components within and between imaging sessions in 18 healthy young subjects (mean age, 27.5 years) who were scanned three times with two resting state scans during each session at 3.0T field strength. Statistical analysis of fMRI time‐series was done using ICA implemented with BrainVoyager QX. At all three sessions the essential components of the DMN could be identified in each individual. Spatial extent of DMN activity and size of overlap within and between sessions were most reproducible for the anterior and posterior cingulate gyrus. The degree of reproducibility of the DMN agrees with the degree of reproducibility found with motor paradigms. We conclude that DMN coactivation patterns are reproducible in healthy young subjects. Therefore, these data can serve as basis to further explore the effects of aging and neuropsychiatric diseases on the DMN of the brain. Hum Brain Mapp, 2010.

Effects of aging on default mode network activity in resting state fMRI: Does the method of analysis matter?

UNLABELLED Functional MRI (fMRI) of default mode network (DMN) brain activity during resting state is gaining attention as a potential non-invasive biomarker to diagnose incipient Alzheimers disease. The aim of this study was to identify effects of normal aging on the DMN using different methods of fMRI processing and evaluation. METHODS fMRI was acquired in 17 young and 21 old healthy subjects and the data were analyzed with (a) volumes of interest (VOI)-based signal time course and (b) independent component analyses (ICA). In the first approach, the strength of DMN region inter-connectivity (as expressed with correlation coefficients) was of primary interest, the second method provided a measure of the magnitude of DMN co-activation. RESULTS The older subjects exhibited significantly lower DMN activity in the posterior cingulate (PCC, t-test P<.001) as well as a tendency to lower activity in all other DMN regions in comparison to the younger subjects. We found no significant effect of age on DMN inter-connectivity. CONCLUSION Effects of normal aging such as loss of PCC co-activity could be detected by ICA, but not by signal time course correlation analyses of DMN inter-connectivity. This either indicates lower sensitivity of inter-connectivity measures to detect subtle DMN changes or indicate that ICA and time course analyses determine different properties of DMN co-activation. Our results, therefore, provide fundamental knowledge for a potential future use of functional MRI as biomarker for neurodegenerative dementias where diminished DMN activity needs to be reliably differentiated from that observed in health aging.

Longitudinal Changes in Fiber Tract Integrity in Healthy Aging and Mild Cognitive Impairment: A DTI Follow-Up Study

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 Alzheimers 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.

Diagnostic value of muscle MRI in differentiating LGMD2I from other LGMDs

Mutations in the fukutin–related protein (FKRP) have recently been demonstrated to cause limb girdle muscular dystrophy type 2I (LGMD2I), one of the most common forms of the autosomal recessive LGMDs in Europe. We performed a systematic clinical and muscle MRI assessment in 6 LGMD2I patients and compared these findings with those of 14 patients with genetically confirmed diagnosis of other forms of autosomal recessive LGMDs or dystrophinopathies. All LGMD2I patients had a characteristic clinical phenotype with predominant weakness of hip flexion and adduction, knee flexion and ankle dorsiflexion. These findings were also mirrored on MRI of the lower extremities which demonstrated marked signal changes in the adductor muscles, the posterior thigh and posterior calf muscles. This characteristic clinical and MRI phenotype was also seen in LGMD2A. However, in LGMD2A there was a selective involvement of the medial gastrocnemius and soleus muscle in the lower legs which was not seen in LGMD2I. The pattern in LGMD2A and LGMD2I were clearly different from the one seen in alpha–sarcoglycanopathy and dystrophinopathy type Becker which showed marked signal abnormalities in the anterior thigh muscles. Our results indicate that muscular MRI is a powerful tool for differentiating LGMD2I from other forms of autosomal recessive LGMDs and dystrophinopathies.

Multicenter stability of diffusion tensor imaging measures: A European clinical and physical phantom study

Diffusion tensor imaging (DTI) detects white matter damage in neuro-psychiatric disorders, but data on reliability of DTI measures across more than two scanners are still missing. In this study we assessed multicenter reproducibility of DTI acquisitions based on a physical phantom as well as brain scans across 16 scanners. In addition, we performed DTI scans in a group of 26 patients with clinically probable Alzheimers disease (AD) and 12 healthy elderly controls at one single center. We determined the variability of fractional anisotropy (FA) measures using manually placed regions of interest as well as automated tract based spatial statistics and deformation based analysis. The coefficient of variation (CV) of FA was 6.9% for the physical phantom data. The mean CV across the multicenter brain scans was 14% for tract based statistics, and 29% for deformation based analysis. The degree of variation was higher in less organized fiber tracts. Our findings suggest that a clinical and physical phantom study involving more than two scanners is indispensable to detect potential sources of bias and to reliably estimate effect size in multicenter diagnostic trials using DTI.

Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study

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.