Analysis of resting-state networks using fMRI usually ignores high-frequency fluctuations in the BOLD signal – be it because of low TR prohibiting the analysis of fluctuations with frequencies higher than 0.25 Hz (for a typical TR of 2 s), or because of the application of a bandpass filter (commonly restricting the signal to frequencies lower than 0.1 Hz). While the standard model of convolving neuronal activity with a hemodynamic response function suggests that the signal of interest in fMRI is characterized by slow fluctuation, it is in fact unclear whether the high-frequency dynamics of the signal consists of noise only. In this study, 10 subjects were scanned at 3 T during 6 min of rest using a multiband EPI sequence with a TR of 354 ms to critically sample fluctuations of up to 1.4 Hz. Preprocessed data were high-pass filtered to include only frequencies above 0.25 Hz, and voxelwise whole-brain temporal ICA (tICA) was used to identify consistent high-frequency signals. The resulting components include physiological background signal sources, most notably pulsation and heart-beat components, that can be specifically identified and localized with the method presented here. Perhaps more surprisingly, common resting-state networks like the default-mode network also emerge as separate tICA components. This means that high-frequency oscillations sampled with a rather T1-weighted contrast still contain specific information on these resting-state networks to consistently identify them, not consistent with the commonly held view that these networks operate on low-frequency fluctuations alone. Consequently, the use of bandpass filters in resting-state data analysis should be reconsidered, since this step eliminates potentially relevant information. Instead, more specific methods for the elimination of physiological background signals, for example by regression of physiological noise components, might prove to be viable alternatives.

MRSI in the brain at ≥7 T is a technique of great promise, but has been limited mainly by low B/B-homogeneity, specific absorption rate restrictions, long measurement times, and low spatial resolution. To overcome these limitations, we propose an ultra-high resolution (UHR) MRSI sequence that provides a 128×128 matrix with a nominal voxel volume of 1.7×1.7×8mm in a comparatively short measurement time. A clinically feasible scan time of 10-20min is reached via a short TR of 200 ms due to an optimised free induction decay-based acquisition with shortened water suppression as well as parallel imaging (PI) using Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration (CAIPIRINHA). This approach is not limited to a rectangular region of interest in the centre of the brain, but also covers cortical brain regions. Transversal pulse-cascaded Hadamard encoding was able to further extend the coverage to 3D-UHR-MRSI of four slices (100×100×4 matrix size), with a measurement time of 17min. Lipid contamination was removed during post-processing using L2-regularisation. Simulations, phantom and volunteer measurements were performed. The obtained single-slice and 3D-metabolite maps show the brain in unprecedented detail (e.g., hemispheres, ventricles, gyri, and the contrast between grey and white matter). This facilitates the use of UHR-MRSI for clinical applications, such as measurements of the small structures and metabolic pathologic deviations found in small Multiple Sclerosis lesions.

Insufficient default mode network (DMN) suppression was linked to increased rumination in symptomatic Major Depressive Disorder (MDD). Since rumination is known to predict relapse and a more severe course of MDD, we hypothesized that similar DMN alterations might also exist during full remission of MDD (rMDD), a condition known to be associated with increased relapse rates specifically in patients with adolescent onset. Within a cross-sectional functional magnetic resonance imaging study activation and functional connectivity (FC) were investigated in 120 adults comprising 78 drug-free rMDD patients with adolescent- (n = 42) and adult-onset (n = 36) as well as 42 healthy controls (HC), while performing the n-back task. Compared to HC, rMDD patients showed diminished DMN deactivation with strongest differences in the anterior-medial prefrontal cortex (amPFC), which was further linked to increased rumination response style. On a brain systems level, rMDD patients showed an increased FC between the amPFC and the dorsolateral prefrontal cortex, which constitutes a key region of the antagonistic working-memory network. Both whole-brain analyses revealed significant differences between adolescent-onset rMDD patients and HC, while adult-onset rMDD patients showed no significant effects. Results of this study demonstrate that reduced DMN suppression exists even after full recovery of depressive symptoms, which appears to be specifically pronounced in adolescent-onset MDD patients. Our results encourage the investigation of DMN suppression as a putative predictor of relapse in clinical trials, which might eventually lead to important implications for antidepressant maintenance treatment.

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.

Human amygdalae are involved in various behavioral functions such as affective and stress processing. For these behavioral functions, as well as for psychophysiological arousal including cortisol release, sex differences are reported. Here, we assessed cortisol levels and resting-state functional connectivity (rsFC) of left and right amygdalae in 81 healthy participants (42 women) to investigate potential modulation of amygdala rsFC by sex and cortisol concentration. Our analyses revealed that rsFC of the left amygdala significantly differed between women and men: Women showed stronger rsFC than men between the left amygdala and left middle temporal gyrus, inferior frontal gyrus, postcentral gyrus and hippocampus, regions involved in face processing, inner-speech, fear and pain processing. No stronger connections were detected for men and no sex difference emerged for right amygdala rsFC. Also, an interaction of sex and cortisol appeared: In women, cortisol was negatively associated with rsFC of the amygdalae with striatal regions, mid-orbital frontal gyrus, anterior cingulate gyrus, middle and superior frontal gyri, supplementary motor area and the parietal-occipital sulcus. Contrarily in men, positive associations of cortisol with rsFC of the left amygdala and these structures were observed. Functional decoding analyses revealed an association of the amygdalae and these regions with emotion, reward and memory processing, as well as action execution. Our results suggest that functional connectivity of the amygdalae as well as the regulatory effect of cortisol on brain networks differs between women and men. These sex-differences and the mediating and sex-dependent effect of cortisol on brain communication systems should be taken into account in affective and stress-related neuroimaging research. Thus, more studies including both sexes are required.