Brain Topography

Functional magnetic resonance imaging (fMRI) at Ultra-high field (UHF, ≥ 7 T) benefits from significant gains in the BOLD contrast-to-noise ratio (CNR) and temporal signal-to-noise ratio (tSNR) compared to conventional field strengths (3 T). Although these improvements enabled researchers to study the human brain to unprecedented spatial resolution, the blood pooling effect reduces the spatial specificity of the widely-used gradient-echo BOLD acquisitions. In this context, vascular space occupancy (VASO-CBV) imaging may be advantageous since it is proposed to have a higher spatial specificity than BOLD. We hypothesized that the assumed higher specificity of VASO-CBV imaging would translate to reduced overlap in fine-scale digit representation maps compared to BOLD-based digit maps. We used sub-millimeter resolution VASO fMRI at 7 T to map VASO-CBV and BOLD responses simultaneously in the motor and somatosensory cortices during individual finger movement tasks. We assessed the cortical overlap in different ways, first by calculating similarity coefficient metrics (DICE and Jaccard) and second by calculating selectivity measures. In addition, we demonstrate a consistent topographical organization of the targeted digit representations (thumb-index-little finger) in the motor areas. We show that the VASO-CBV responses yielded less overlap between the digit clusters than BOLD, and other selectivity measures were higher for VASO-CBV too. In summary, these results were consistent across metrics and participants, confirming the higher spatial specificity of VASO-CBV compared to BOLD.

Rapid progress in neuromagnetic technology has been achieved during the past two years with the introduction of a method for accurately indicating magnetic sensor locations with respect to a head-based coordinate system and the advent of refrigerator-cooled sensors and larger arrays of sensors. These make possible the real-time monitoring of evoked activity at several widely separated locations over the scalp, thus revealing sequential activity in, e.g., sensory-motor tasks. Arrays of magnetic sensors also provide sufficient information to locate the sources of spontaneous activity, such as alpha rhythm. The locations of discrete generators (alphons) of individual alpha spindles is now possible with an array of 14 sensors. Mapping techniques with a 5-sensor system have revealed preferential suppression of alpha activity within certain regions of the occipital lobe to tasks involving mental comparisons of abstract figures. These studies provide evidence that the machinery of visual cortex is involved in mental imagery.

The role of midline frontal theta in human is discussed, with reference to its relation on consciousness. Clinical observations of the appearance of this theta activity under various conditions are reviewed. These include simulated diving, brain tumour, chemical intoxication, exercise, mental calculation, sleep and meditation. The mechanism of generation of this frontal midline theta activity is discussed.

The cortical network subserving language processing is likely to exhibit a high spatial and temporal complexity. Studies using brain imaging methods, like fMRI or PET, succeeded in identifying a number of brain structures that seem to contribute to the processing of syntactic structures, while their dynamic interaction remains unclear due to the low temporal resolution of the methods. On the other hand, ERP studies have revealed a great deal of the temporal dimension of language processing without being able to provide more than very coarse information on the localisation of the underlying generators. MEG has a temporal resolution similar to EEG combined with a better spatial resolution. In this paper, Brain Surface Current Density (BSCD) mapping in a standard brain model was used to identify statistically significant differences between the activity of certain brain regions due to syntactically correct and incorrect auditory language input. The results show that the activity in the first 600 ms after violation onset is mainly concentrated in the temporal cortex and the adjacent frontal and parietal areas of both hemispheres. The statistical analysis reveals significantly different activity mainly in both frontal and temporal cortices. For longer latencies above 250 ms, the differential activity is more prominent in the right hemisphere. These findings confirm other recent results that suggest right hemisphere involvement in auditory language processing. One interpretation might be that right hemisphere regions play an important role in repair and re-analysis processes in order to free the specialised left hemisphere language areas for processing further input.

Multi-channel receive array rf-coils have become widely available for fMRI. The improved SNR and possibility of acquisition acceleration through parallel imaging are especially beneficial for high-resolution studies. In this study, an 8-channel and a 32-channel coil were compared in a high-resolution finger tapping fMRI experiment at 7 T. 1.3 mm3 resolution data acquired with the 32-channel coil provided higher image- and temporal SNR and yielded higher BOLD sensitivity measures, notably higher cluster sizes in MI/SI and increased z-scores, though not an increase in percent signal change. For sub-millimeter resolution fMRI data acquired with the 32-channel coil smaller clusters were found, though percent signal changes were significantly larger, due to reduced partial volume effects. These results demonstrate the utility of the use of an array coil with a large number of receive elements for high-resolution fMRI at ultra-high field.

In neuroscience, neural oscillations and other features of brain activity recorded by electroencephalography (EEG) are typically statistically assessed on the basis of the study’s population mean to identify possible blueprints for healthy subjects, or subjects with diagnosable neurological or psychiatric disorders. Despite some inter-individual similarities, there is reason to believe that a discernible portion of the individual brain activity is subject-specific. In order to encompass the potential individual source of variance in EEG data and psychometric parameters, we introduce an innovative application of linear mixed-effects models (LMM) as an alternative procedure for the analysis of resting-state EEG data. Using LMM, individual differences can be modelled through the assumptions of idiosyncrasy of all responses and dependency among data points (e.g., from the same subject within and across units of time) via random effects parameters. This report provides an example of how LMM can be used for the statistical analysis of resting-state EEG data in a heterogeneous group of subjects; namely, people who suffer from tinnitus (ringing in the ear/s). Results from 49 participants (38 male, mean age of 46.69 ± 12.65 years) revealed that EEG signals were not only associated with specific recording sites, but exhibited regional specific oscillations in conjunction to symptom severity. Tinnitus distress targeted the frequency bands beta3 (23.5–35 Hz) and gamma (35.5–45 Hz) in right frontal regions, whereas delta (0.5–4 Hz) exhibited significant changes in temporal-parietal sources. Further, 57.8% of the total variance in EEG power was subject-specific and acknowledged by the LMM framework and its prediction. Thus, a deeper understanding of both the underlying statistical and physiological patterns of EEG data was gained.

How functional magnetic resonance imaging (fMRI) data are analyzed depends on the researcher and the toolbox used. It is not uncommon that the processing pipeline is rewritten for each new dataset. Consequently, code transparency, quality control and objective analysis pipelines are important for improving reproducibility in neuroimaging studies. Toolboxes, such as Nipype and fMRIPrep, have documented the need for and interest in automated pre-processing analysis pipelines. Recent developments in data-driven models combined with high resolution neuroimaging dataset have strengthened the need not only for a standardized preprocessing workflow, but also for a reliable and comparable statistical pipeline. Here, we introduce fMRIflows: a consortium of fully automatic neuroimaging pipelines for fMRI analysis, which performs standard preprocessing, as well as 1st- and 2nd-level univariate and multivariate analyses. In addition to the standardized pre-processing pipelines, fMRIflows provides flexible temporal and spatial filtering to account for datasets with increasingly high temporal resolution and to help appropriately prepare data for advanced machine learning analyses, improving signal decoding accuracy and reliability. This paper first describes fMRIflows’ structure and functionality, then explains its infrastructure and access, and lastly validates the toolbox by comparing it to other neuroimaging processing pipelines such as fMRIPrep, FSL and SPM. This validation was performed on three datasets with varying temporal sampling and acquisition parameters to prove its flexibility and robustness. fMRIflows is a fully automatic fMRI processing pipeline which uniquely offers univariate and multivariate single-subject and group analyses as well as pre-processing.

Objective: Together with spindles, K-complexes are well known hallmarks of stage 2 sleep (S2). However, little is known about their topographical distribution in comparison to delta-waves and to K-complexes superimposed by spindles. Patients and methods: In this study, the topographical distribution of spontaneous K-complexes and delta-waves in S2 and delta-waves in stage 4 sleep (S4) in 10 healthy young adults (aged 20 to 35 years, 7 female) was investigated. K-complexes with and without spindles in S2, delta-waves with and without spindles in S2, and delta-waves in S4 distributed all over the night were visually selected. EEG power maps and statistical parametric maps were calculated. Results: Absolute delta power of S2 K-complexes appeared to be significantly higher than of S2 delta-waves and delta power of S4 delta-waves was higher than of S2 delta-waves. In K-complexes and delta-waves, power was found to be highest over medio-frontal regions in the delta frequency band (0.5 - 4.0 Hz) with a second maximum occipitally in delta-waves, no matter whether superimposed by a spindle or not. Conclusion: K-complexes and delta-waves in S2 differ in topographical distribution. Even though in S2 delta-waves have less power, they have a similar topographical distribution in S2 and S4, supporting the hypothesis that delta-waves in S2 further develop towards delta-waves in slow wave sleep. The delta frequency components of K-complexes and delta-waves are unaffected by spindles.