Kristian Loewe
Otto-von-Guericke University Magdeburg
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Featured researches published by Kristian Loewe.
Neurology | 2015
Judith Machts; Kristian Loewe; Joern Kaufmann; Sibylle Jakubiczka; Susanne Abdulla; Susanne Petri; Reinhard Dengler; Hans-Jochen Heinze; Stefan Vielhaber; Mircea Ariel Schoenfeld; Peter Bede
Objectives: To evaluate basal ganglia changes along the amyotrophic lateral sclerosis (ALS)–ALS–frontotemporal dementia (FTD) continuum using multiple, complementary imaging techniques. Methods: Sixty-seven C9orf72-negative patients with ALS and 39 healthy controls were included in a cross-sectional quantitative MRI study. Seven patients with ALS met criteria for comorbid behavioral variant FTD (ALS-FTD), 18 patients met the Strong criteria for cognitive and/or behavioral impairment (ALS-Plus), and 42 patients had no cognitive impairment (ALS-Nci). Volumetric, shape, and density analyses were performed for the thalamus, amygdala, nucleus accumbens, hippocampus, caudate nucleus, pallidum, and putamen. Results: Significant basal ganglia volume differences were identified between the study groups. Shape analysis revealed distinct atrophy patterns in the amygdala in patients with ALS-Nci and in the hippocampus in patients with ALS-Plus in comparison with controls. Patients with ALS-FTD exhibited pathologic changes in the bilateral thalami, putamina, pallida, hippocampi, caudate, and accumbens nuclei in comparison with all other study groups. A preferential vulnerability has been identified within basal ganglia subregions, which connect directly to key cortical sites of ALS pathology. While the anatomical patterns were analogous, the degree of volumetric, shape, and density changes confirmed incremental pathology through the spectrum of ALS-Nci, ALS-Plus, to ALS-FTD. Performance on verbal memory tests correlated with hippocampal volumes, and accumbens nuclei volumes showed a negative correlation with apathy scores. Conclusions: We demonstrate correlations between basal ganglia measures and structure-specific neuropsychological performance and a gradient of incremental basal ganglia pathology across the ALS–ALS-FTD spectrum, suggesting that the degree of subcortical gray matter pathology in C9orf72-negative ALS is closely associated with neuropsychological changes.
The Journal of Neuroscience | 2017
Mandy V. Bartsch; Kristian Loewe; Christian Merkel; Hans-Jochen Heinze; Mircea Ariel Schoenfeld; John K. Tsotsos; Jens-Max Hopf
Attention can facilitate the selection of elementary object features such as color, orientation, or motion. This is referred to as feature-based attention and it is commonly attributed to a modulation of the gain and tuning of feature-selective units in visual cortex. Although gain mechanisms are well characterized, little is known about the cortical processes underlying the sharpening of feature selectivity. Here, we show with high-resolution magnetoencephalography in human observers (men and women) that sharpened selectivity for a particular color arises from feedback processing in the human visual cortex hierarchy. To assess color selectivity, we analyze the response to a color probe that varies in color distance from an attended color target. We find that attention causes an initial gain enhancement in anterior ventral extrastriate cortex that is coarsely selective for the target color and transitions within ∼100 ms into a sharper tuned profile in more posterior ventral occipital cortex. We conclude that attention sharpens selectivity over time by attenuating the response at lower levels of the cortical hierarchy to color values neighboring the target in color space. These observations support computational models proposing that attention tunes feature selectivity in visual cortex through backward-propagating attenuation of units less tuned to the target. SIGNIFICANCE STATEMENT Whether searching for your car, a particular item of clothing, or just obeying traffic lights, in everyday life, we must select items based on color. But how does attention allow us to select a specific color? Here, we use high spatiotemporal resolution neuromagnetic recordings to examine how color selectivity emerges in the human brain. We find that color selectivity evolves as a coarse to fine process from higher to lower levels within the visual cortex hierarchy. Our observations support computational models proposing that feature selectivity increases over time by attenuating the responses of less-selective cells in lower-level brain areas. These data emphasize that color perception involves multiple areas across a hierarchy of regions, interacting with each other in a complex, recursive manner.
Scientific Reports | 2017
Kristian Loewe; Judith Machts; Jörn Kaufmann; Susanne Petri; Hans-Jochen Heinze; Christian Borgelt; Joseph A. Harris; Stefan Vielhaber; Mircea Ariel Schoenfeld
Recent studies suggest that amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) lie on a single clinical continuum. However, previous neuroimaging studies have found only limited involvement of temporal lobe regions in ALS. To better delineate possible temporal lobe involvement in ALS, the present study aimed to examine changes in functional connectivity across the whole brain, particularly with regard to extra-motor regions, in a group of 64 non-demented ALS patients and 38 healthy controls. To assess between-group differences in connectivity, we computed edge-level statistics across subject-specific graphs derived from resting-state functional MRI data. In addition to expected ALS-related decreases in functional connectivity in motor-related areas, we observed extensive changes in connectivity across the temporo-occipital cortex. Although ALS patients with comorbid FTD were deliberately excluded from this study, the pattern of connectivity alterations closely resembles patterns of cerebral degeneration typically seen in FTD. This evidence for subclinical temporal dysfunction supports the idea of a common pathology in ALS and FTD.
BMC Neuroscience | 2014
Kristian Loewe; Marcus Grueschow; Christian Stoppel; Rudolf Kruse; Christian Borgelt
BackgroundGraph-based analysis of fMRI data has recently emerged as a promising approach to study brain networks. Based on the assessment of synchronous fMRI activity at separate brain sites, functional connectivity graphs are constructed and analyzed using graph-theoretical concepts. Most previous studies investigated region-level graphs, which are computationally inexpensive, but bring along the problem of choosing sensible regions and involve blurring of more detailed information. In contrast, voxel-level graphs provide the finest granularity attainable from the data, enabling analyses at superior spatial resolution. They are, however, associated with considerable computational demands, which can render high-resolution analyses infeasible. In response, many existing studies investigating functional connectivity at the voxel-level reduced the computational burden by sacrificing spatial resolution.MethodsHere, a novel, time-efficient method for graph construction is presented that retains the original spatial resolution. Performance gains are instead achieved through data reduction in the temporal domain based on dichotomization of voxel time series combined with tetrachoric correlation estimation and efficient implementation.ResultsBy comparison with graph construction based on Pearson’s r, the technique used by the majority of previous studies, we find that the novel approach produces highly similar results an order of magnitude faster.ConclusionsIts demonstrated performance makes the proposed approach a sensible and efficient alternative to customary practice. An open source software package containing the created programs is freely available for download.
Frontiers in Neuroinformatics | 2016
Kristian Loewe; Sarah E. Donohue; Mircea Ariel Schoenfeld; Rudolf Kruse; Christian Borgelt
The functioning of the human brain relies on the interplay and integration of numerous individual units within a complex network. To identify network configurations characteristic of specific cognitive tasks or mental illnesses, functional connectomes can be constructed based on the assessment of synchronous fMRI activity at separate brain sites, and then analyzed using graph-theoretical concepts. In most previous studies, relatively coarse parcellations of the brain were used to define regions as graphical nodes. Such parcellated connectomes are highly dependent on parcellation quality because regional and functional boundaries need to be relatively consistent for the results to be interpretable. In contrast, dense connectomes are not subject to this limitation, since the parcellation inherent to the data is used to define graphical nodes, also allowing for a more detailed spatial mapping of connectivity patterns. However, dense connectomes are associated with considerable computational demands in terms of both time and memory requirements. The memory required to explicitly store dense connectomes in main memory can render their analysis infeasible, especially when considering high-resolution data or analyses across multiple subjects or conditions. Here, we present an object-based matrix representation that achieves a very low memory footprint by computing matrix elements on demand instead of explicitly storing them. In doing so, memory required for a dense connectome is reduced to the amount needed to store the underlying time series data. Based on theoretical considerations and benchmarks, different matrix object implementations and additional programs (based on available Matlab functions and Matlab-based third-party software) are compared with regard to their computational efficiency. The matrix implementation based on on-demand computations has very low memory requirements, thus enabling analyses that would be otherwise infeasible to conduct due to insufficient memory. An open source software package containing the created programs is available for download.
Archive | 2013
Kristian Loewe; Marcus Grueschow; Christian Borgelt
A core task in the analysis of functional magnetic resonance imaging (fMRI) data is to detect groups of voxels that exhibit synchronous activity while the subject is performing a certain task. Synchronous activity is typically interpreted as functional connectivity between brain regions. We compare classical approaches like statistical parametric mapping (SPM) and some new approaches that are loosely based on frequent pattern mining principles, but restricted to the local neighborhood of a voxel. In particular, we examine how a soft notion of activity (rather than a binary one) can be modeled and exploited in the analysis process. In addition, we explore a fault-tolerant notion of synchronous activity of groups of voxels in both the binary and the soft/fuzzy activity setting. We apply the methods to fMRI data from a visual stimulus experiment to demonstrate their usefulness.
international joint conference on computational intelligence | 2015
Salatiel Ezennaya-Gomez; Christian Borgelt; Christian Braune; Kristian Loewe; Rudolf Kruse
With the objective to detect neuron assemblies in recorded parallel spike trains, we develop methods to find frequent parallel episodes in parallel point processes (or event sequences) that allow for imprecise synchrony of the events constituting occurrences (temporal imprecision) as well as incomplete occurrences (selective participation). The temporal imprecision problem is tackled by frequent pattern mining using two different notions of synchrony: a binary notion that captures only the number of instances of a pattern and a graded notion that captures both the number of instances as well as the precision of synchrony of its events. To cope with selective participation, which is the main focus of this paper, a reduction sequence of items (or event types) is formed based on found frequent patterns and guided by pattern overlap, for which we explore different concept. We demonstrate the performance of our methods on a large number of (artificially generated) data sets with injected parallel episodes, which mimic actually recorded parallel spike trains.
Journal of Neurology | 2015
Robert Steinbach; Kristian Loewe; Joern Kaufmann; Judith Machts; Katja Kollewe; Susanne Petri; Reinhard Dengler; Hans-Jochen Heinze; Stefan Vielhaber; Mircea Ariel Schoenfeld; Christian Michael Stoppel
conference of international fuzzy systems association and european society for fuzzy logic and technology | 2015
Christian Borgelt; Christian Braune; Kristian Loewe; Rudolf Kruse
Clinical Neurophysiology | 2015
Judith Machts; Kristian Loewe; Jörn Kaufmann; Sibylle Jakubiczka; Susanne Abdulla; Susanne Petri; Reinhard Dengler; Hans-Jochen Heinze; Mircea Ariel Schoenfeld; Stefan Vielhaber; Peter Bede