Brain network dynamics in people with visual snow syndrome.

Visual snow syndrome (VSS) is a neurological disorder characterized by a range of continuous visual disturbances. Little is known about the functional pathological mechanisms underlying VSS and their effect on brain network topology, studied using high-resolution resting-state (RS) 7 T MRI. Forty VSS patients and 60 healthy controls underwent RS MRI. Functional connectivity matrices were calculated, and global efficiency (network integration), modularity (network segregation), local efficiency (LE, connectedness neighbors) and eigenvector centrality (significance node in network) were derived using a dynamic approach (temporal fluctuations during acquisition). Network measures were compared between groups, with regions of significant difference correlated with known aberrant ocular motor VSS metrics (shortened latencies and higher number of inhibitory errors) in VSS patients. Lastly, nodal co-modularity, a binary measure of node pairs belonging to the same module, was studied. VSS patients had lower modularity, supramarginal centrality and LE dynamics of multiple (sub)cortical regions, centered around occipital and parietal lobules. In VSS patients, lateral occipital cortex LE dynamics correlated positively with shortened prosaccade latencies (p = .041, r = .353). In VSS patients, occipital, parietal, and motor nodes belonged more often to the same module and demonstrated lower nodal co-modularity with temporal and frontal regions. This study revealed reduced dynamic variation in modularity and local efficiency strength in the VSS brain, suggesting that brain network dynamics are less variable in terms of segregation and local clustering. Further investigation of these changes could inform our understanding of the pathogenesis of the disorder and potentially lead to treatment strategies.

Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis.

BACKGROUND: Upper and lower limb disabilities are hypothesized to have partially independent underlying (network) disturbances in multiple sclerosis (MS). OBJECTIVE: This study investigated functional network predictors and longitudinal network changes related to upper and lower limb progression in MS. METHODS: Two-hundred fourteen MS patients and 58 controls underwent functional magnetic resonance imaging (fMRI), dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) measurements (baseline and 5 years). Patients were stratified into progressors (>20% decline) or non-progressors. Functional network efficiency was calculated using static (over entire scan) and dynamic (fluctuations during scan) approaches. Baseline measurements were used to predict progression; significant predictors were explored over time. RESULTS: In both limbs, progression was related to supplementary motor area and caudate efficiency (dynamic and static, respectively). Upper limb progression showed additional specific predictors; cortical grey matter volume, putamen static efficiency and posterior associative sensory (PAS) cortex, putamen, primary somatosensory cortex and thalamus dynamic efficiency. Additional lower limb predictors included motor network grey matter volume, caudate (dynamic) and PAS (static). Only the caudate showed a decline in efficiency over time in one group (non-progressors). CONCLUSION: Disability progression can be predicted using sensorimotor network measures. Upper and lower limb progression showed unique predictors, possibly indicating different network disturbances underlying these types of progression in MS.

Plug-and-play microphones for recording speech and voice with smart devices.

INTRODUCTION Smart devices are widely available and capable of quickly recording and uploading speech segments for health-related analysis. The switch from laboratory recordings with professional-grade microphone set ups to remote, smart device-based recordings offers immense potential for the scalability of voice assessment. Yet, a growing body of literature points to a wide heterogeneity among acoustic metrics for their robustness to variation in recording devices. The addition of consumer-grade plug-and-play microphones has been proposed as a possible solution. Our aim was to assess if the addition of consumer-grade plug-and-play microphones increase the acoustic measurement agreement between ultra-portable devices and a reference microphone. METHODS Speech was simultaneously recorded by a reference high-quality microphone commonly used in research, and by two configurations with plug-and-play microphones. Twelve speech-acoustic features were calculated using recordings from each microphone to determine the agreement intervals in measurements between microphones. Agreement intervals were then compared to expected deviations in speech in various neurological conditions. Each microphone's response to speech and to silence were characterized through acoustic analysis to explore possible reasons for differences in acoustic measurements between microphones. The statistical differentiation of two groups, neurotypical and people with Multiple Sclerosis, using metrics from each tested microphone was compared to that of the reference microphone. RESULTS The two consumer-grade plug-and-play microphones favoured high frequencies (mean centre of gravity difference ≥ +175.3Hz) and recorded more noise (mean difference in signal-to-noise ≤ -4.2dB) when compared to the reference microphone. Between consumer-grade microphones, differences in relative noise were closely related to distance between the microphone and the speaker's mouth. Agreement intervals between the reference and consumer-grade microphones remained under disease-expected deviations only for fundamental frequency (f0, agreement interval ≤0.06Hz), f0 instability (f0 CoV, agreement interval ≤0.05%) and for tracking of second formant movement (agreement interval ≤1.4Hz/millisecond). Agreement between microphones was poor for other metrics, particularly for fine timing metrics (mean pause length and pause length variability for various tasks). The statistical difference between the two groups of speakers was smaller with the plug-and-play than with the reference microphone. CONCLUSION Measurement of f0 and F2 slope were robust to variation in recording equipment while other acoustic metrics were not. Thus, the tested plug-and-play microphones should not be used interchangeably with professional-grade microphones for speech analysis. Plug-and-play microphones may assist in equipment standardization within speech studies, including remote or self-recording, possibly with small loss in accuracy and statistical power as observed in this study.

Gait stability reflects motor tracts damage at early stages of multiple sclerosis.

BACKGROUND: Gait in people with multiple sclerosis (PwMS) is affected even when no changes can be observed on clinical examination. A sensitive measure of gait deterioration is stability; however, its correlation with motor tract damage has not yet been established. OBJECTIVE: To compare stability between PwMS and healthy controls (HCs) and determine associations between stability and diffusion magnetic resonance image (MRI) measures of axonal damage in selected sensorimotor tracts. METHODS: Twenty-five PwMS (Expanded Disability Status Scale (EDSS) < 2.5) and 15 HCs walked on a treadmill. Stability from sacrum (LDESAC), shoulder (LDESHO) and cervical (LDECER) was calculated using the local divergence exponent (LDE). Participants underwent a 7T-MRI brain scan to obtain fibre-specific measures of axonal loss within the corticospinal tract (CST), interhemispheric sensorimotor tract (IHST) and cerebellothalamic tract (CTT). Correlation analyses between LDE and fibre density (FD) within tracts, fibre cross-section (FC) and FD modulated by FC (FDC) were conducted. Between-groups LDE differences were analysed using analysis of variance (ANOVA). RESULTS: Correlations between all stability measures with CSTFD, between CSTFDC with LDESAC and LDECER, and LDECER with IHSTFD and IHSTFDC were significant yet moderate (R < -0.4). Stability was significantly different between groups. CONCLUSIONS: Poorer gait stability is associated with corticospinal tract (CST) axonal loss in PwMS with no-to-low disability and is a sensitive indicator of neurodegeneration.

Functional correlates of motor control impairments in multiple sclerosis: A 7 Tesla task functional MRI study.

Upper and lower limb impairments are common in people with multiple sclerosis (pwMS), yet difficult to clinically identify in early stages of disease progression. Tasks involving complex motor control can potentially reveal more subtle deficits in early stages, and can be performed during functional MRI (fMRI) acquisition, to investigate underlying neural mechanisms, providing markers for early motor progression. We investigated brain activation during visually guided force matching of hand or foot in 28 minimally disabled pwMS (Expanded Disability Status Scale (EDSS) < 4 and pyramidal and cerebellar Kurtzke Functional Systems Scores ≤ 2) and 17 healthy controls (HC) using ultra-high field 7-Tesla fMRI, allowing us to visualise sensorimotor network activity in high detail. Task activations and performance (tracking lag and error) were compared between groups, and correlations were performed. PwMS showed delayed (+124 s, p = .002) and more erroneous (+0.15 N, p = .001) lower limb tracking, together with lower cerebellar, occipital and superior parietal cortical activation compared to HC. Lower activity within these regions correlated with worse EDSS (p = .034), lower force error (p = .006) and higher lesion load (p < .05). Despite no differences in upper limb task performance, pwMS displayed lower inferior occipital cortical activation. These results demonstrate that ultra-high field fMRI during complex hand and foot tracking can identify subtle impairments in lower limb movements and upper and lower limb brain activity, and differentiates upper and lower limb impairments in minimally disabled pwMS.

Increased functional sensorimotor network efficiency relates to disability in multiple sclerosis.

BACKGROUND: Network abnormalities could help explain physical disability in multiple sclerosis (MS), which remains poorly understood. OBJECTIVE: This study investigates functional network efficiency changes in the sensorimotor system. METHODS: We included 222 MS patients, divided into low disability (LD, Expanded Disability Status Scale (EDSS) ⩽3.5, n = 185) and high disability (HD, EDSS ⩾6, n = 37), and 82 healthy controls (HC). Functional connectivity was assessed between 23 sensorimotor regions. Measures of efficiency were computed and compared between groups using general linear models corrected for age and sex. Binary logistic regression models related disability status to local functional network efficiency (LE), brain volumes and demographics. Functional connectivity patterns of regions important for disability were explored. RESULTS: HD patients demonstrated significantly higher LE of the left primary somatosensory cortex (S1) and right pallidum compared to LD and HC, and left premotor cortex compared to HC only. The logistic regression model for disability (R2 = 0.38) included age, deep grey matter volume and left S1 LE. S1 functional connectivity was increased with prefrontal and secondary sensory areas in HD patients, compared to LD and HC. CONCLUSION: Clinical disability in MS associates with functional sensorimotor increases in efficiency and connectivity, centred around S1, independent of structural damage.

Microstructural correlates of 23Na relaxation in human brain at 7 Tesla.

23Na provides the second strongest MR-observable signal in biological tissue and exhibits bi-exponential T2∗ relaxation in micro-environments such as the brain. There is significant interest in developing 23Na biomarkers for neurological diseases that are associated with sodium channel dysfunction such as multiple sclerosis and epilepsy. We have previously reported methods for acquisition of multi-echo sodium MRI and continuous distribution modelling of sodium relaxation properties as surrogate markers of brain microstructure. This study aimed to compare 23Na T2∗ relaxation times to more established measures of tissue microstructure derived from advanced diffusion MRI at 7 â€‹T. Six healthy volunteers were scanned using a 3D multi-echo radial ultra-short TE sequence using a dual-tuned 1H/23Na birdcage coil, and a high-resolution multi-shell, high angular resolution diffusion imaging sequence using a 32-channel 1H receive coil. 23Na T2∗ relaxation parameters [mean T2∗ (T2∗mean) and fast relaxation fraction (T2∗ff)] were calculated from a voxel-wise continuous gamma distribution signal model. White matter (restricted anisotropic diffusion) and grey matter (restricted isotropic diffusion) density were calculated from multi-shell multi-tissue constrained spherical deconvolution. Sodium parameters were compared with white and grey matter diffusion properties. Sodium T2∗mean and T2∗ff showed little variation across a range of white matter axonal fibre and grey matter densities. We conclude that sodium T2∗ relaxation parameters are not greatly influenced by relative differences in intra- and extracellular partial volumes. We suggest that care be taken when interpreting sodium relaxation changes in terms of tissue microstructure in healthy tissue.

Compressed sensing effects on quantitative analysis of undersampled human brain sodium MRI.

PURPOSE: The clinical application of sodium MRI is hampered due to relatively low image quality and associated long acquisition times. Compressed sensing (CS) aims at a reduction of measurement time, but has been found to encompass quantitative estimation bias when used in low SNR x-Nuclei imaging. This work analyses CS in quantitative human brain sodium MRI from undersampled acquisitions and provides recommendations for tissue sodium concentration (TSC) estimation. METHODS: CS reconstructions from 3D radial acquisitions of 5 healthy volunteers were investigated over varying undersampling factors (USFs) and CS penalty weights on different sparsity domains, Wavelet, Discrete Cosine Transform (DCT), and Identity. Resulting images were compared with highly sampled and undersampled NUFFT-based images and evaluated for image quality (i.e. structural similarity), image intensity bias, and its effect on TSC estimates in gray and white matter. RESULTS: Wavelet-based CS reconstructions show highest image quality with stable TSC estimates for most USFs. Up to an USF of 4, images showed good structural detail. DCT and Identity-based CS enable good image quality, however show a bias in TSC with a reduction in estimates across USFs. CONCLUSIONS: The image intensity bias is lowest in Wavelet-based reconstructions and enables an up to fourfold acquisition speed up while maintaining good structural detail. The associated acquisition time reduction can facilitate a translation of sodium MRI into clinical routine.

Extracting more for less: multi-echo MP2RAGE for simultaneous T1 -weighted imaging, T1 mapping, R2∗ mapping, SWI, and QSM from a single acquisition.

PURPOSE: To demonstrate simultaneous T1 -weighted imaging, T1 mapping, R2∗ mapping, SWI, and QSM from a single multi-echo (ME) MP2RAGE acquisition. METHODS: A single-echo (SE) MP2RAGE sequence at 7 tesla was extended to ME with 4 bipolar gradient echo readouts. T1 -weighted images and T1 maps calculated from individual echoes were combined using sum of squares and averaged, respectively. ME-combined SWI and associated minimum intensity projection images were generated with TE-adjusted homodyne filters. A QSM reconstruction pipeline was used, including a phase-offsets correction and coil combination method to properly combine the phase images from the 32 receiver channels. Measurements of susceptibility, R2∗ , and T1 of brain tissue from ME-MP2RAGE were compared with those from standard ME-gradient echo and SE-MP2RAGE. RESULTS: The ME combined T1 -weighted, T1 map, SWI, and minimum intensity projection images showed increased SNRs compared to the SE results. The proposed coil combination method led to QSM results free of phase-singularity artifacts, which were present in the standard adaptive combination method. T1 -weighted, T1 , and susceptibility maps from ME-MP2RAGE were comparable to those obtained from SE-MP2RAGE and ME-gradient echo, whereas R2∗ maps showed increased blurring and reduced SNR. T1 , R2∗ , and susceptibility values of brain tissue from ME-MP2RAGE were consistent with those from SE-MP2RAGE and ME-gradient echo. CONCLUSION: High-resolution structural T1 weighted imaging, T1 mapping, R2∗ mapping, SWI, and QSM can be extracted from a single 8.5-min ME-MP2RAGE acquisition using a customized reconstruction pipeline. This method can be applied to replace separate SE-MP2RAGE and ME-gradient echo acquisitions to significantly shorten total scan time.

Acoustic Speech Analytics Are Predictive of Cerebellar Dysfunction in Multiple Sclerosis.

Speech production relies on motor control and cognitive processing and is linked to cerebellar function. In diseases where the cerebellum is impaired, such as multiple sclerosis (MS), speech abnormalities are common and can be detected by instrumental assessments. However, the potential of speech assessments to be used to monitor cerebellar impairment in MS remains unexplored. The aim of this study is to build an objectively measured speech score that reflects cerebellar function, pathology and quality of life in MS. Eighty-five people with MS and 21 controls participated in the study. Speech was independently assessed through objective acoustic analysis and blind expert listener ratings. Cerebellar function and overall disease disability were measured through validated clinical scores; cerebellar pathology was assessed via magnetic resonance imaging, and validated questionnaires informed quality of life. Selected speech variables were entered in a regression model to predict cerebellar function. The resulting model was condensed into one composite speech score and tested for prediction of abnormal 9-hole peg test (9HPT), and for correlations with the remaining cerebellar scores, imaging measurements and self-assessed quality of life. Slow rate of syllable repetition and increased free speech pause percentage were the strongest predictors of cerebellar impairment, complemented by phonatory instability. Those variables formed the acoustic composite score that accounted for 54% of variation in cerebellar function, correlated with cerebellar white matter volume (r = 0.3, p = 0.017), quality of life (r = 0.5, p < 0.001) and predicted an abnormal 9HPT with 85% accuracy. An objective multi-feature speech metric was highly representative of motor cerebellar impairment in MS.