[Show abstract][Hide abstract] ABSTRACT: Corrupted gradient directions (GD) in diffusion weighted images may seriously affect reliability of diffusion tensor imaging (DTI)-based comparisons at the group level. In the present study we employed a quality control (QC) algorithm to eliminate corrupted gradient directions from DTI data. We then assessed effects of this procedure on comparisons between Huntington disease (HD) subjects and controls at the group level.
[Show abstract][Hide abstract] ABSTRACT: Although episodic memory impairment is usually the earliest sign of Alzheimer's disease (AD), there are up to 15% of patients presenting with early impairment in non-memory cognitive functions (i.e., atypical AD). Stratifying patients with AD may aid clinical trials. Previous studies divided patients by cognitive profile, focusing on cross-sectional analyses without testing stability of clusters over time. We used principal component analysis followed by cluster analyses in 127 patients with AD based on 24 cognitive scores at 0, 6, 12, and 24 months follow-up. We investigated the definition of clusters and their stability over time as well as interactions of cluster assignment and disease severity. At each time point, six distinct factors and four distinct clusters were extracted that did not differ substantially between time points. Clusters were defined by cognitive profile rather than disease severity. 85% of patients fell into the same cluster twice, 42% three times, and 17% four times. Subjects with focal semantic impairment progressed significantly faster than the other cluster. Longitudinally, focal deficits increased relatively rather than tending toward average disease severity. The observed similar cluster definitions at each time point indicate the validity of the approach. Cluster-specific longitudinal increases of focal impairments and significant between-cluster differences in disease progression make this approach useful for stratified inclusions into clinical trials.
Journal of Alzheimer's disease: JAD 06/2014; · 4.17 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Automated analysis of structural magnetic resonance images is a promising way to improve early detection of neurodegenerative brain diseases. Clinical applications of such methods involve multiple scanners with potentially different hardware and/or acquisition sequences and demographically heterogeneous groups. To improve classification performance, we propose to correct effects of subject-specific covariates (such as age, total intracranial volume, and sex) as well as effects of scanner by using a non-linear Gaussian process model. To test the efficacy of the correction, we performed classification of carriers of the genetic mutation leading to Huntington's disease (HD) versus healthy controls. Half of the HD carriers were free of typical HD symptoms and had an estimated 5 to 20years before onset of clinical symptoms, thus providing a model for preclinical diagnosis of a neurodegenerative disease. Structural magnetic resonance brain images were acquired at four sites with pairs of sites had the identical scanner type, equipment, and acquisition parameters. For automatic classification, we used spatially normalized probabilistic maps of gray matter, then removed confounding effects by Gaussian process regression, and then performed classification with a support vector machine. Voxel-based morphometry of gray matter maps showed disease effects that were spatially wider spread than effects of scanner, but no significant interactions between scanner and disease were found. A model trained with data from a single scanner generalized well to data from a different scanner. When confounding diagnostics groups and scanner during training, e.g. by using controls from one scanner and gene carriers from another, classification accuracy dropped significantly in many cases. By regressing out confounds with Gaussian process regression, the performance levels comparable to those obtained in scenarios without confound. We conclude that models trained on data acquired with a single scanner generalized well to data acquired with a different same-generation scanner even when the vendor differed. When confounding grouping and scanner during training is unavoidable to gather training data, regressing out inter-scanner and between-subject variability can reduce the loss in accuracy due to the confound.
[Show abstract][Hide abstract] ABSTRACT: Diffusion tensor imaging (DTI) allows the simultaneously measurement of several diffusion indices that provide complementary information on the substrate of white matter alterations in neurodegenerative diseases. These indices include fractional anisotropy (FA) as measure of fiber tract integrity, and the mode of anisotropy (Mode) reflecting differences in the shape of the diffusion tensor. We used a multivariate approach based on joint independent component analysis of FA and Mode in a large sample of 138 subjects with Alzheimer's disease (AD) dementia, 37 subjects with cerebrospinal fluid biomarker positive mild cognitive impairment (MCI-AD), and 153 healthy elderly controls from the European DTI Study on Dementia to comprehensively study alterations of microstructural white matter integrity in AD dementia and predementia AD. We found a parallel decrease of FA and Mode in intracortically projecting fiber tracts, and a parallel increase of FA and Mode in the corticospinal tract in AD patients compared to controls. Subjects with MCI-AD showed a similar, but spatially more restricted pattern of diffusion changes. Our findings suggest an early axonal degeneration in intracortical projecting fiber tracts in dementia and predementia stages of AD. An increase of Mode, parallel to an increase of FA, in the corticospinal tract suggests a more linear shape of diffusion due to loss of crossing fibers along relatively preserved cortico-petal and cortico-fugal fiber tracts in AD. Supporting this interpretation, we found three populations of fiber tracts, namely cortico-petal and cortico-fugal, commissural, and intrahemispherically projecting fiber tracts, in the peak area of parallel FA and Mode increase.
Journal of Alzheimer's disease: JAD 02/2014; · 4.17 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Histopathological studies in Alzheimer's disease (AD) suggest severe and region-specific neurodegeneration of the basal forebrain cholinergic system (BFCS). Here, we studied the between-center reliability and diagnostic accuracy of MRI-based BFCS volumetry in a large multicenter data set, including participants with prodromal (n = 41) or clinically manifest AD (n = 134) and 148 cognitively healthy controls. Atrophy was determined using voxel-based and region-of-interest based analyses of high-dimensionally normalized MRI scans using a newly created map of the BFCS based on postmortem in cranio MRI and histology. The AD group showed significant volume reductions of all subregions of the BFCS, which were most pronounced in the posterior nucleus basalis Meynert (NbM). The mild cognitive impairment-AD group showed pronounced volume reductions in the posterior NbM, but preserved volumes of anterior-medial regions. Diagnostic accuracy of posterior NbM volume was superior to hippocampus volume in both groups, despite higher multicenter variability of the BFCS measurements. The data of our study suggest that BFCS morphometry may provide an emerging biomarker in AD.
Journal of Alzheimer's disease: JAD 02/2014; · 4.17 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Long-term potentiation (LTP) is a key element of synaptic plasticity. At the macroscopic level, similar effects can be induced in the human brain using repetitive stimulation with identical stimuli. High-frequency stimulation (HFS) can increase neuronal responses whereas low-frequency stimulation may produce the opposite effect. Optimal stimulation frequencies and characteristics for inducing stimulus-specific response modification differ substantially from those applied to brain tissue slices but have been explored in recent studies. In contrast, the individual manifestation of this effect in terms of its spatial location and extent are unclear. Using functional MRI (fMRI) in 18 subjects (mean age 25.3 years), we attempted to induce LTP-like effects by HFS with checkerboard flashes at 9 Hz for 120 seconds. As expected, flashes induced strong activation in primary and secondary visual cortices. Contrary to our expectations, we found clusters of decreased activations induced by pattern flashes after HFS at the border between primary and secondary visual cortices.. On the level of the individual subject, some showed significantly increased activations in the post-HFS session while the majority showed significant decreases. The locations of areas showing altered activations before and after HFS were only partly overlapping. No association between location, extent and direction of the HFS-effect was observed. The findings are unexpected in the light of existing HFS-studies, but mirror the high inter-subject variability, concerning even the directionality of the induced effects shown for other indices of LTP-like plasticity in the human brain. As this variability is not observed in LTP at the cellular level, a better understanding of LTP-like mechanisms on the macroscopic level is essential for establishing tools to quantify individual synaptic plasticity in-vivo.
Frontiers in Human Neuroscience 01/2014; 8(695). · 2.91 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The early, preferably pre-clinical, identification of neurodegenerative diseases is important as treatment will be most successful before substantial neuronal loss. Here, we reasoned that functional brain changes as measured using functional magnetic resonance imaging (fMRI) will precede neurodegeneration. Three independent cohorts of patients with the genetic mutation leading to Huntington's Disease (HD) but without any clinical symptoms and matched controls performed three different fMRI tasks: Sequential finger tapping engaged the motor system, which is primarily affected by HD, whereas a working-memory task and a task aiming to induce irritation represented the range of low- and high-level cognitive functions also affected by HD. Each diagnostic group of every cohort included 11-16 subjects. After segmentation into 76 cortical and 14 subcortical regions, we extracted functional connectivity patterns through pairwise correlation between the signals in the regions. The resulting coefficients were directly embedded as input to a pattern classifier aiming to separate controls from gene mutation carriers. Alternatively, graph-theory measures such as degree and clustering coefficient were used as features for the discrimination. Classification accuracy never outperformed the accuracy of a grouping based on parameter estimates from a general-linear model approach or a grouping based on features extracted from anatomical images as reported in a previous analysis. Despite good within-subject stability between two runs of the same task, a high between-subject variability led to chance-level accuracy. These results indicate that standard graph-metrics are insufficient to detect subtle disease related changes when within-group variability is high. Developing methods that reduce variability related to noise should be the focus of future studies.
[Show abstract][Hide abstract] ABSTRACT: Individuals with subjective memory impairment (SMI) report worsening of memory without impairment in cognitive tests. Despite normal cognitive performance, they may be at higher risk of cognitive decline compared with individuals without SMI.
We used a discriminative function (a support vector machine) trained on an independent data set of 226 healthy control subjects and 191 patients with probable Alzheimer's disease (AD) dementia to characterize the baseline gray matter patterns of 24 individuals with SMI and 53 control subjects. We tested for associations of these gray matter patterns with SMI presence, cognitive performance at baseline, and cognitive decline at follow-up.
Individuals with SMI showed greater similarity to an AD gray matter pattern compared with control subjects without SMI. In addition, episodic memory decline was associated with an AD gray matter pattern in the SMI group.
Our results indicate a link between the gray matter atrophy pattern of patients with AD and the presence of SMI. Furthermore, multivariate pattern recognition approaches seem to be a sensitive method for identifying subtle brain changes that correspond to future memory decline in SMI.
Alzheimer's and Dementia 07/2013; · 14.48 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Alzheimer's disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07–1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options. Copyright: ß 2013 Gaser et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by BMBF grant 01EV0709. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wp-content/ uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Data collection and sharing was funded by ADNI (National Institutes of Health (NIH) grant U01AG024904). ADNI itself is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following:-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the United States Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study
PLoS ONE 06/2013; 8(6):e67346. · 3.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Inhibitory deficits contribute to cognitive decline in the aging brain. Separating subcomponents of response inhibition may help to resolve contradictions in the existing literature. A total of 49 healthy participants underwent functional magnetic resonance imaging (fMRI) while performing a Go/no-go-, a Simon-, and a Stop-signal task. Regression analyses were conducted to identify correlations of age and activation patterns. Imaging results revealed a differential effect of age on subcomponents of response inhibition. In a simple Go/no-go task (no spatial discrimination), aging was associated with increased activation of the core inhibitory network and parietal areas. In the Simon task, which required spatial discrimination, increased activation in additional inhibitory control regions was present. However, in the Stop-signal task, the most demanding of the three tasks, aging was associated with decreased activation. This suggests that older adults increasingly recruit the inhibitory network and, with increasing load, additional inhibitory regions. However, if inhibitory load exceeds compensatory capacity, performance declines in concert with decreasing activation. Thus, the present findings may refine current theories of cognitive aging.
Neurobiology of aging 04/2013; · 5.94 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Understanding brain reserve in preclinical stages of neurodegenerative disorders allows determination of which brain regions contribute to normal functioning despite accelerated neuronal loss. Besides the recruitment of additional regions, a reorganisation and shift of relevance between normally engaged regions is a suggested key mechanism. Thus, network analysis methods seem critical for investigation of changes in directed causal interactions between such candidate brain regions. To identify core compensatory regions, fifteen preclinical patients carrying the genetic mutation leading to Huntington's disease and twelve controls underwent fMRI scanning. They accomplished an auditory paced finger sequence tapping task, which challenged cognitive as well as executive aspects of motor functioning by varying speed and complexity of movements. To investigate causal interactions among brain regions a single Dynamic Causal Model (DCM) was constructed and fitted to the data from each subject. The DCM parameters were analysed using statistical methods to assess group differences in connectivity, and the relationship between connectivity patterns and predicted years to clinical onset was assessed in gene carriers. In preclinical patients, we found indications for neural reserve mechanisms predominantly driven by bilateral dorsal premotor cortex, which increasingly activated superior parietal cortices the closer individuals were to estimated clinical onset. This compensatory mechanism was restricted to complex movements characterised by high cognitive demand. Additionally, we identified task-induced connectivity changes in both groups of subjects towards pre- and caudal supplementary motor areas, which were linked to either faster or more complex task conditions. Interestingly, coupling of dorsal premotor cortex and supplementary motor area was more negative in controls compared to gene mutation carriers. Furthermore, changes in the connectivity pattern of gene carriers allowed prediction of the years to estimated disease onset in individuals. Our study characterises the connectivity pattern of core cortical regions maintaining motor function in relation to varying task demand. We identified connections of bilateral dorsal premotor cortex as critical for compensation as well as task-dependent recruitment of pre- and caudal supplementary motor area. The latter finding nicely mirrors a previously published general linear model-based analysis of the same data. Such knowledge about disease specific inter-regional effective connectivity may help identify foci for interventions based on transcranial magnetic stimulation designed to stimulate functioning and also to predict their impact on other regions in motor-associated networks.
[Show abstract][Hide abstract] ABSTRACT: Converging evidence from neuroimaging studies and computational modelling suggests an organization of language in a dual dorsal-ventral brain network: a dorsal stream connects temporoparietal with frontal premotor regions through the superior longitudinal and arcuate fasciculus and integrates sensorimotor processing, e.g. in repetition of speech. A ventral stream connects temporal and prefrontal regions via the extreme capsule and mediates meaning, e.g. in auditory comprehension. The aim of our study was to test, in a large sample of 100 aphasic stroke patients, how well acute impairments of repetition and comprehension correlate with lesions of either the dorsal or ventral stream. We combined voxelwise lesion-behaviour mapping with the dorsal and ventral white matter fibre tracts determined by probabilistic fibre tracking in our previous study in healthy subjects. We found that repetition impairments were mainly associated with lesions located in the posterior temporoparietal region with a statistical lesion maximum in the periventricular white matter in projection of the dorsal superior longitudinal and arcuate fasciculus. In contrast, lesions associated with comprehension deficits were found more ventral-anterior in the temporoprefrontal region with a statistical lesion maximum between the insular cortex and the putamen in projection of the ventral extreme capsule. Individual lesion overlap with the dorsal fibre tract showed a significant negative correlation with repetition performance, whereas lesion overlap with the ventral fibre tract revealed a significant negative correlation with comprehension performance. To summarize, our results from patients with acute stroke lesions support the claim that language is organized along two segregated dorsal-ventral streams. Particularly, this is the first lesion study demonstrating that task performance on auditory comprehension measures requires an interaction between temporal and prefrontal brain regions via the ventral extreme capsule pathway.
[Show abstract][Hide abstract] ABSTRACT: Sleep has been demonstrated to significantly modulate brain plasticity and the manifestation of mental disorders. However, previous studies on the effect of disrupted sleep on brain structure have reported inconsistent results. The goal of the current study was to investigate brain morphometry in a well-characterized large sample of patients with primary insomnia (PI) in comparison with good sleeper controls.
Automated parcellation and pattern recognition approaches were supplemented by voxelwise analyses of gray and white matter volumes to analyze magnetic resonance images. All analyses included age, sex, and total intracranial volume as covariates.
Department of Psychiatry and Psychotherapy of the University of Freiburg Medical Center.
There were 28 patients with PI (10 males; 18 females; age 43.7 ± 14.2 y) and 38 healthy, good sleepers (17 males; 21 females; age 39.6 ± 8.9 y).
No significant between-group differences were observed in any of the investigated brain morphometry variables.
Altered brain function in insomnia does not appear to have a substantial effect on brain morphometry on a macroscopic level. CITATION: Spiegelhalder K; Regen W; Baglioni C; Klöppel S; Abdulkadir A; Hennig J; Nissen C; Riemann D; Feige B. Insomnia does not appear to be associated with substantial structural brain changes. SLEEP 2013;36(5):731-737.
[Show abstract][Hide abstract] ABSTRACT: Response inhibition is disturbed in several disorders sharing impulse control deficits as a core symptom. Since response inhibition is a cognitively and neurally multifaceted function which has been shown to rely on differing neural subprocesses and neurotransmitter systems, further differentiation to define neurophysiological endophenotypes is essential. Response inhibition may involve at least three separable cognitive subcomponents, i.e. interference inhibition, action withholding, and action cancelation. Here, we introduce a novel paradigm – the Hybrid Response Inhibition task – to disentangle interference inhibition, action withholding and action cancelation and their neural subprocesses within one task setting during functional magnetic resonance imaging (fMRI). To validate the novel task, results were compared to a battery of separate, standard response inhibition tasks independently capturing these subcomponents and subprocesses. Across all subcomponents, mutual activation was present in the right inferior frontal cortex (rIFC), pre-supplementary motor area (pre-SMA) and parietal regions. Interference inhibition revealed stronger activation in pre-motor and parietal regions. Action cancelation resulted in stronger activation in fronto-striatal regions. Our results show that all subcomponents share a common neural network and thus all constitute different subprocesses of response inhibition. Subprocesses, however, differ to the degree of regional involvement: interference inhibition relies more pronouncedly on a fronto-parietal–pre-motor network suggesting its close relation to response selection processes. Action cancelation, in turn, is more strongly associated with the fronto-striatal pathway implicating it as a late subcomponent of response inhibition. The new paradigm reliably captures three putatively subsequent subprocesses of response inhibition and might be a promising tool to differentially assess disturbed neural networks in disorders showing impulse control deficits.
[Show abstract][Hide abstract] ABSTRACT: Diffusion tensor imaging (DTI) detects microstructural changes of the cerebral white matter in Alzheimer's disease (AD). The use of DTI for the diagnosis of AD in a multicenter setting has not yet been investigated. We used voxel-based analysis of fractional anisotropy, mean diffusivity, and grey matter volumes from multimodal magnetic resonance imaging data of 137 AD patients and 143 healthy elderly controls collected across 9 different scanners. We compared different univariate analysis approaches to model the effect of scanner, including a linear model across all scans with a scanner covariate, a random effects model with scanner as a random variable as well as a voxel-based meta-analysis. We found significant reduction of fractional anisotropy and significant increase of mean diffusivity in core areas of AD pathology including corpus callosum, medial and lateral temporal lobes, as well as fornix, cingulate gyrus, precuneus, and prefrontal lobe white matter. Grey matter atrophy was most pronounced in medial and lateral temporal lobe as well as parietal and prefrontal association cortex. The effects of group were spatially more restricted with random effects modeling of scanner effects compared to simple pooled analysis. All three analysis approaches yielded similar accuracy of group separation in block-wise cross-validated logistic regression. Our results suggest similar effects of center on group separation based on different analysis approaches and confirm a typical pattern of cortical and subcortical microstructural changes in AD using a large multimodal multicenter data set.
Journal of Alzheimer's disease: JAD 08/2012; · 4.17 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The availability of an accurate genetic test to identify Huntington�s Disease (HD) in the pre-symptomatic stage makes HD an important model to develop biomarkers for other neurodegenerative diseases, such as pre-clinical Alzheimer�s Disease. We reasoned that functional changes, measured by functional MRI (fMRI), would precede gray matter changes and that performing a task specifically affected by the disease would carry the clearest signature. Separate cohorts of HD gene mutations carriers and controls performed four different fMRI tasks, probing functions either primarly affected by the disease (i.e. motor control), higher cognitive functions (i.e. working memory and irritability), or basic sensory functions (i.e. auditory system). With the aim to compare fMRI and structural MRI biomarkers, all subjects underwent an additional high-resolution T1-weighted MRI. Best classification performance was achived from fMRI-based activations with motor sequence tapping and task-induced irritation. Classification performance based on gray matter probability maps was also significantly above chance and similar to that of fMRI. Both were sufficiently informative to separate gene mutation carriers that were on average 17 years before predicted disease onset from controls with up to 80% accuracy. Further analyses showed that classification accuracy was best in regions of interest with low within-group heterogeneity in relation to disease specific changes. Our study indicates that structural and some functional markers can accurately detect pre-clinical neurodegeneration. However, the lower variability and easier processing of the strucutral MRI data make latter the more useful tool for disease detection in a clinical setting.
Current Alzheimer research 06/2012; · 4.97 Impact Factor