Christian Nasel

The spectral diversity of resting-state fluctuations in the human brain.

In order to assess whole-brain resting-state fluctuations at a wide range of frequencies, resting-state fMRI data of 20 healthy subjects were acquired using a multiband EPI sequence with a low TR (354 ms) and compared to 20 resting-state datasets from standard, high-TR (1800 ms) EPI scans. The spatial distribution of fluctuations in various frequency ranges are analyzed along with the spectra of the time-series in voxels from different regions of interest. Functional connectivity specific to different frequency ranges (<0.1 Hz; 0.1–0.25 Hz; 0.25–0.75 Hz; 0.75–1.4 Hz) was computed for both the low-TR and (for the two lower-frequency ranges) the high-TR datasets using bandpass filters. In the low-TR data, cortical regions exhibited highest contribution of low-frequency fluctuations and the most marked low-frequency peak in the spectrum, while the time courses in subcortical grey matter regions as well as the insula were strongly contaminated by high-frequency signals. White matter and CSF regions had highest contribution of high-frequency fluctuations and a mostly flat power spectrum. In the high-TR data, the basic patterns of the low-TR data can be recognized, but the high-frequency proportions of the signal fluctuations are folded into the low frequency range, thus obfuscating the low-frequency dynamics. Regions with higher proportion of high-frequency oscillations in the low-TR data showed flatter power spectra in the high-TR data due to aliasing of the high-frequency signal components, leading to loss of specificity in the signal from these regions in high-TR data. Functional connectivity analyses showed that there are correlations between resting-state signal fluctuations of distant brain regions even at high frequencies, which can be measured using low-TR fMRI. On the other hand, in the high-TR data, loss of specificity of measured fluctuations leads to lower sensitivity in detecting functional connectivity. This underlines the advantages of low-TR EPI sequences for resting-state and potentially also task-related fMRI experiments.

fMRI measurements of amygdala activation are confounded by stimulus correlated signal fluctuation in nearby veins draining distant brain regions

Imaging the amygdala with functional MRI is confounded by multiple averse factors, notably signal dropouts due to magnetic inhomogeneity and low signal-to-noise ratio, making it difficult to obtain consistent activation patterns in this region. However, even when consistent signal changes are identified, they are likely to be due to nearby vessels, most notably the basal vein of rosenthal (BVR). Using an accelerated fMRI sequence with a high temporal resolution (TR = 333 ms) combined with susceptibility-weighted imaging, we show how signal changes in the amygdala region can be related to a venous origin. This finding is confirmed here in both a conventional fMRI dataset (TR = 2000 ms) as well as in information of meta-analyses, implying that “amygdala activations” reported in typical fMRI studies are likely confounded by signals originating in the BVR rather than in the amygdala itself, thus raising concerns about many conclusions on the functioning of the amygdala that rely on fMRI evidence alone.

Separating coordinative and executive dysfunction in cerebellar patients during motor skill acquisition.

OBJECTIVE Patients with cerebellar stroke are impaired in motor skill acquisition and cognitive/executive performance. The aim was to test whether skill acquisition in cerebellar patients is influenced by executive demands such as the intermittent exercise of a conflicting motor task. METHODS Patients with cerebellar stroke and healthy controls were tested in two serial reaction time experiments. In Experiment 1, participants performed practice runs (always same sequence) and interference runs (new sequence for each run) in a strictly alternating fashion. In Experiment 2, participants rested between successive practice runs; the duration of rests was adapted to the duration of interference runs in the other experiment. Participants of Experiment 1 were also tested for cognitive-executive functions (Wisconsin Card Sort, Word Fluency, Trail Making, Digit Span backwards). RESULTS (1) Patients in Experiment 1, although always slower than controls, acquired motor skills in the first run before interference but in contrast to controls failed to improve their performance in subsequent runs. (2) Patients in Experiment 2 improved their performance consistently over several runs. (3) Patients of Experiment 1 were worse than controls in several cognitive-executive functions; however, these deficits did not correlate with the degree of interference in motor skill acquisition. INTERPRETATION Simple movement coordination and higher order context-related movement organisation are separate cerebellar functions. In cerebellar patients, impaired movement coordination is associated with generally slower reaction times whereas organisational deficits are associated with a specific impairment to change between motor sets. Motor-executive functions responsible for the latter impairment might be independent from cognitive-executive functions.

Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project

Technologies for scalable analysis of very large datasets have emerged in the domain of internet computing, but are still rarely used in neuroimaging despite the existence of data and research questions in need of efficient computation tools especially in fMRI. In this work, we present software tools for the application of Apache Spark and Graphics Processing Units (GPUs) to neuroimaging datasets, in particular providing distributed file input for 4D NIfTI fMRI datasets in Scala for use in an Apache Spark environment. Examples for using this Big Data platform in graph analysis of fMRI datasets are shown to illustrate how processing pipelines employing it can be developed. With more tools for the convenient integration of neuroimaging file formats and typical processing steps, big data technologies could find wider endorsement in the community, leading to a range of potentially useful applications especially in view of the current collaborative creation of a wealth of large data repositories including thousands of individual fMRI datasets.

Scanning fast and slow: current limitations of 3 Tesla functional MRI and future potential

Functional MRI at 3T has become a workhorse for the neurosciences, e.g., neurology, psychology, and psychiatry, enabling non-invasive investigation of brain function and connectivity. However, BOLD-based fMRI is a rather indirect measure of brain function, confounded by physiology related signals, e.g., head or brain motion, brain pulsation, blood flow, intermixed with susceptibility differences close or distant to the region of neuronal activity. Even though a plethora of preprocessing strategies have been published to address these confounds, their efficiency is still under discussion. In particular, physiological signal fluctuations closely related to brain supply may mask BOLD signal changes related to “true” neuronal activation. Here we explore recent technical and methodological advancements aimed at disentangling the various components, employing fast multiband vs. standard EPI, in combination with fast temporal ICA. Our preliminary results indicate that fast (TR <0.5 s) scanning may help to identify and eliminate physiologic components, increasing tSNR and functional contrast. In addition, biological variability can be studied and task performance better correlated to other measures. This should increase specificity and reliability in fMRI studies. Furthermore, physiological signal changes during scanning may then be recognized as a source of information rather than a nuisance. As we are currently still undersampling the complexity of the brain, even at a rather coarse macroscopic level, we should be very cautious in the interpretation of neuroscientific findings, in particular when comparing different groups (e.g., age, sex, medication, pathology, etc.). From a technical point of view our goal should be to sample brain activity at layer specific resolution with low TR, covering as much of the brain as possible without violating SAR limits. We hope to stimulate discussion toward a better understanding and a more quantitative use of fMRI.

Perceptual sequence learning is more severely impaired than motor sequence learning in patients with chronic cerebellar stroke

Patients with cerebellar stroke are impaired in procedural learning. Several different learning mechanisms contribute to procedural learning in healthy individuals. The aim was to compare the relative share of different learning mechanisms in patients and healthy controls. Ten patients with cerebellar stroke and 12 healthy controls practiced a visuomotor serial reaction time task. Learning blocks with high stimulus–response compatibility were exercised repeatedly; in between these, participants performed test blocks with the same or a different (mirror-inverted or unrelated) stimulus sequence and/or the same or a different (mirror-inverted) stimulus–response allocation. This design allowed to measure the impact of motor learning and perceptual learning independently and to separate both mechanisms from the learning of stimulus–response pairs. Analysis of the learning blocks showed that, as expected, both patients and controls improved their performance over time, although patients remained significantly slower. Analysis of the test blocks revealed that controls showed significant motor learning as well as significant visual perceptual learning, whereas cerebellar patients showed only significant motor learning. Healthy participants were able to use perceptual information for procedural learning even when the rule linking stimuli and responses had been changed, whereas patients with cerebellar lesions could not recruit this perception-based mechanism. Therefore, the cerebellum appears involved in the accurate processing of perceptual information independent from prelearned stimulus–response mappings.

Normalised time-to-peak-distribution curves correlate with cerebral white matter hyperintensities – Could this improve early diagnosis?

Parameter-free assessment of the time-to-peak (TTP) histogram, termed ‘TTP-distribution curve’ (TDC), of dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI) was introduced as a robust method to evaluate cerebral perfusion. TDC-assessment works fully automatically without the need of an arterial input function, thereby providing full comparability between different measurements. In the investigated sample of 106 patients, a strong dependency of TDC on the hemodynamic state of cerebral microvessels and the arterio-venous bolus-transit time T av was demonstrated. Accordingly, TDC-derived T av was 3.3–3.7 s for control patients and 4.4 s for cerebral small vessel disease patients. Measurements of associated bolus spread velocities ν and accelerations a additionally revealed a direct effect from spin–spin relaxation time T2-weighted white matter hyperintensity volume, considered to indicate microangiopathy in cerebral small vessel disease, on the TDC-measurements. This strongly supports the prevailing hypothesis that cerebral small vessel disease directly influences DSC-measurements, where the degree could be estimated from an analysis of TDC. While this may be used to correct DSC-parameters for undesirable effects from cerebral small vessel disease, it could also serve to potentially identify patients at risk for cerebral small vessel disease at an early stage, since a subset of patients without yet significant WHM-volume, but clearly altered hemodynamics in TDC-measurements, was identified in this study.

Visual Quantification of the Circle of Willis: An Automated Identification and Standardized Representation

This paper presents a method for the visual quantification of cerebral arteries, known as the Circle of Willis (CoW). It is an arterial structure with the responsibility of supplying the brain with blood, however, dysfunctions can lead to strokes. The diagnosis of such a time‐critical/urgent event depends on the expertise of radiologists and the applied software tools. They use basic display methods of the volumetric data without any support of advanced image processing and visualization techniques. The goal of this paper is to present an automated method for the standardized description of cerebral arteries in stroke patients in order to provide an overview of the CoWs configuration. This novel representation provides visual indications of problematic areas as well as straightforward comparisons between multiple patients. Additionally, we offer a pipeline for extracting the CoW from Time‐of‐Flight Magnetic Resonance Angiography (TOF‐MRA) data sets together with an enumeration technique for labelling the arterial segments by detecting the main supplying arteries of the CoW. We evaluated the feasibility of our visual quantification approach in a study of 63 TOF‐MRA data sets and compared our findings to those of three radiologists. The obtained results demonstrate that our proposed techniques are effective in detecting the arteries and visually capturing the overall configuration of the CoW.

Identification of Voxels Confounded by Venous Signals Using Resting-State fMRI Functional Connectivity Graph Community Identification.

Identifying venous voxels in fMRI datasets is important to increase the specificity of fMRI analyses to microvasculature in the vicinity of the neural processes triggering the BOLD response. This is, however, difficult to achieve in particular in typical studies where magnitude images of BOLD EPI are the only data available. In this study, voxelwise functional connectivity graphs were computed on minimally preprocessed low TR (333 ms) multiband resting-state fMRI data, using both high positive and negative correlations to define edges between nodes (voxels). A high correlation threshold for binarization ensures that most edges in the resulting sparse graph reflect the high coherence of signals in medium to large veins. Graph clustering based on the optimization of modularity was then employed to identify clusters of coherent voxels in this graph, and all clusters of 50 or more voxels were then interpreted as corresponding to medium to large veins. Indeed, a comparison with SWI reveals that 75.6±5.9% of voxels within these large clusters overlap with veins visible in the SWI image or lie outside the brain parenchyma. Some of the remaining differences between the two modalities can be explained by imperfect alignment or geometric distortions between the two images. Overall, the graph clustering based method for identifying venous voxels has a high specificity as well as the additional advantages of being computed in the same voxel grid as the fMRI dataset itself and not needing any additional data beyond what is usually acquired (and exported) in standard fMRI experiments.