In multiple sclerosis (MS), iron accumulates inside activated microglia/macrophages at edges of some chronic demyelinated lesions, forming rims. In susceptibility-based magnetic resonance imaging at 7 T, iron-laden microglia/macrophages induce a rim of decreased signal at lesion edges and have been associated with slowly expanding lesions. We aimed to determine (1) what lesion types and stages are associated with iron accumulation at their edges, (2) what cells at the lesion edges accumulate iron and what is their activation status, (3) how reliably can iron accumulation at the lesion edge be detected by 7 T magnetic resonance imaging (MRI), and (4) if lesions with rims enlarge over time in vivo, when compared to lesions without rims. Double-hemispheric brain sections of 28 MS cases were stained for iron, myelin, and microglia/macrophages. Prior to histology, 4 of these 28 cases were imaged at 7 T using post-mortem susceptibility-weighted imaging. In vivo, seven MS patients underwent annual neurological examinations and 7 T MRI for 3.5 years, using a fluid attenuated inversion recovery/susceptibility-weighted imaging fusion sequence. Pathologically, we found iron rims around slowly expanding and some inactive lesions but hardly around remyelinated shadow plaques. Iron in rims was mainly present in microglia/macrophages with a pro-inflammatory activation status, but only very rarely in astrocytes. Histological validation of post-mortem susceptibility-weighted imaging revealed a quantitative threshold of iron-laden microglia when a rim was visible. Slowly expanding lesions significantly exceeded this threshold, when compared with inactive lesions (p = 0.003). We show for the first time that rim lesions significantly expanded in vivo after 3.5 years, compared to lesions without rims (p = 0.003). Thus, slow expansion of MS lesions with rims, which reflects chronic lesion activity, may, in the future, become an MRI marker for disease activity in MS.Electronic supplementary materialThe online version of this article (doi:10.1007/s00401-016-1636-z) contains supplementary material, which is available to authorized users.

CLP1 is a RNA kinase involved in tRNA splicing. Recently, CLP1 kinase-dead mice were shown to display a neuromuscular disorder with loss of motor neurons and muscle paralysis. Human genome analyses now identified a CLP1 homozygous missense mutation (p.R140H) in five unrelated families, leading to a loss of CLP1 interaction with the tRNA splicing endonuclease (TSEN) complex, largely reduced pre-tRNA cleavage activity, and accumulation of linear tRNA introns. The affected individuals develop severe motor-sensory defects, cortical dysgenesis and microcephaly. Mice carrying kinase-dead CLP1 also displayed microcephaly and reduced cortical brain volume due to the enhanced cell death of neuronal progenitors that is associated with reduced numbers of cortical neurons. Our data elucidate a novel neurological syndrome defined by CLP1 mutations that impair tRNA splicing. Reduction of a founder mutation to homozygosity illustrates the importance of rare variations in disease and supports the clan genomics hypothesis.

Recent data suggest that multiple sclerosis white matter lesions surrounded by a rim of iron containing microglia, termed iron rim lesions, signify patients with more severe disease course and a propensity to develop progressive multiple sclerosis. So far, however, little is known regarding the dynamics of iron rim lesions over long-time follow-up. In a prospective longitudinal cohort study in 33 patients (17 females; 30 relapsing-remitting, three secondary progressive multiple sclerosis; median age 36.6 years (18.6–62.6), we characterized the evolution of iron rim lesions by MRI at 7 T with annual scanning. The longest follow-up was 7 years in a subgroup of eight patients. Median and mean observation period were 1 (0–7) and 2.9 (±2.6) years, respectively. Images were acquired using a fluid-attenuated inversion recovery sequence fused with iron-sensitive MRI phase data, termed FLAIR-SWI, as well as a magnetization prepared two rapid acquisition gradient echoes, termed MP2RAGE. Volumes and T1 relaxation times of lesions with and without iron rims were assessed by manual segmentation. The pathological substrates of periplaque signal changes outside the iron rims were corroborated by targeted histological analysis on 17 post-mortem cases (10 females; two relapsing-remitting, 13 secondary progressive and two primary progressive multiple sclerosis; median age 66 years (34–88), four of them with available post-mortem 7 T MRI data. We observed 16 nascent iron rim lesions, which mainly formed in relapsing-remitting multiple sclerosis. Iron rim lesion fraction was significantly higher in relapsing-remitting than progressive disease (17.8 versus 7.2%; P < 0.001). In secondary progressive multiple sclerosis only, iron rim lesions showed significantly different volume dynamics (P < 0.034) compared with non-rim lesions, which significantly shrank with time in both relapsing-remitting (P < 0.001) and secondary progressive multiple sclerosis (P < 0.004). The iron rims themselves gradually diminished with time (P < 0.008). Compared with relapsing-remitting multiple sclerosis, iron rim lesions in secondary progressive multiple sclerosis were significantly more destructive than non-iron rim lesions (P < 0.001), reflected by prolonged lesional T1 relaxation times and by progressively increasing changes ascribed to secondary axonal degeneration in the periplaque white matter. Our study for the first time shows that chronic active lesions in multiple sclerosis patients evolve over many years after their initial formation. The dynamics of iron rim lesions thus provide one explanation for progressive brain damage and disability accrual in patients. Their systematic recording might become useful as a tool for predicting disease progression and monitoring treatment in progressive multiple sclerosis.

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.

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.