Advanced central nervous system imaging biomarkers in radiologically isolated syndrome: a mini review

Radiologically isolated syndrome is characterised by central nervous system white-matter hyperintensities highly suggestive of multiple sclerosis in individuals without a neurological history of clinical demyelinating episodes. It probably represents the pre-symptomatic phase of clinical multiple sclerosis but is poorly understood. This mini review summarises our current knowledge regarding advanced imaging techniques in radiologically isolated syndrome that provide insights into its pathobiology and prognosis. The imaging covered will include magnetic resonance imaging-derived markers of central nervous system volumetrics, connectivity, and the central vein sign, alongside optical coherence tomography-related metrics

Incidental findings of Cysticercus tenuicollis metacestodes in five oryx species

AbstractObjectiveTo determine the prevalence of Cysticercus tenuicollis (C. tenuicollis) metacestodes in five oryx species kept in Al Ain Zoo animal collection.MethodsThis study was based on a retrospective analysis of post-mortem records covering a four year period (July 2010 to July 2014).ResultsA total of 213 individual animals were recorded dead during the four year period (July 2010 to July 2014). Out of this, 12 (5.6%) were recorded with C. tenuicollis. More females (8) than males (4) were recorded to have C. tenuicollis, although this was not statistically significant (P = 0.3737).ConclusionsThis study shows that, Arabian oryx, beisa oryx, fringe-eared oryx, gemsbok and scimitar-horned oryx are susceptible to C. tenuicollis. Based on the epidemiology and the life cycle of this parasite, it is possible that these captive animals ingested the parasite through contaminated feed which could have happened in the pasture land or stray dogs and wild canidae (e.g. fox) visited the zoo contaminating the oryx feed. Stray dogs and wild canidae should be prevented from visiting pasture land and a captive animal facility

SENSE EPI reconstruction with 2D phase error correction and channel-wise noise removal

Nyquist ghost; Denoising; DiffusionFantasma de Nyquist; Eliminación de ruido; DifusiónFantasma de Nyquist; Eliminació de soroll; DifusióPurpose To develop a robust reconstruction pipeline for EPI data that enables 2D Nyquist phase error correction using sensitivity encoding without incurring major noise artifacts in low SNR data. Methods SENSE with 2D phase error correction (PEC-SENSE) was combined with channel-wise noise removal using Marcenko–Pastur principal component analysis (MPPCA) to simultaneously eliminate Nyquist ghost artifacts in EPI data and mitigate the noise amplification associated with phase correction using parallel imaging. The proposed pipeline (coined SPECTRE) was validated in phantom DW-EPI data using the accuracy and precision of diffusion metrics; ground truth values were obtained from data acquired with a spin echo readout. Results from the SPECTRE pipeline were compared against PEC-SENSE reconstructions with three alternate denoising strategies: (i) no denoising; (ii) denoising of magnitude data after image formation; (iii) denoising of complex data after image formation. SPECTRE was then tested using high -value (i.e., low SNR) diffusion data (up to  s/mm ) in four healthy subjects. Results Noise amplification associated with phase error correction incurred a 23% bias in phantom mean diffusivity (MD) measurements. Phantom MD estimates using the SPECTRE pipeline were within 8% of the ground truth value. In healthy volunteers, the SPECTRE pipeline visibly corrected Nyquist ghost artifacts and reduced associated noise amplification in high -value data. Conclusion The proposed reconstruction pipeline is effective in correcting low SNR data, and improves the accuracy and precision of derived diffusion metrics.EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging, Grant/Award Number: EP/L016478/

Automated detection of hyperreflective foci in the outer nuclear layer of the retina

PURPOSE: Hyperreflective foci are poorly understood transient elements seen on optical coherence tomography (OCT) of the retina in both healthy and diseased eyes. Systematic studies may benefit from the development of automated tools that can map and track such foci. The outer nuclear layer (ONL) of the retina is an attractive layer in which to study hyperreflective foci as it has no fixed hyperreflective elements in healthy eyes. In this study, we intended to evaluate whether automated image analysis can identify, quantify and visualize hyperreflective foci in the ONL of the retina. METHODS: This longitudinal exploratory study investigated 14 eyes of seven patients including six patients with optic neuropathy and one with mild non-proliferative diabetic retinopathy. In total, 2596 OCT B-scan were obtained. An image analysis blob detector algorithm was used to detect candidate foci, and a convolutional neural network (CNN) trained on a manually labelled subset of data was then used to select those candidate foci in the ONL that fitted the characteristics of the reference foci best. RESULTS: In the manually labelled data set, the blob detector found 2548 candidate foci, correctly detecting 350 (89%) out of 391 manually labelled reference foci. The accuracy of CNN classifier was assessed by manually splitting the 2548 candidate foci into a training and validation set. On the validation set, the classifier obtained an accuracy of 96.3%, a sensitivity of 88.4% and a specificity of 97.5% (AUC 0.989). CONCLUSION: This study demonstrated that automated image analysis and machine learning methods can be used to successfully identify, quantify and visualize hyperreflective foci in the ONL of the retina on OCT scans

Visual Snow Syndrome Improves With Modulation of Resting-State Functional MRI Connectivity After Mindfulness-Based Cognitive Therapy: An Open-Label Feasibility Study

BACKGROUND: Visual snow syndrome (VSS) is associated with functional connectivity (FC) dysregulation of visual networks (VNs). We hypothesized that mindfulness-based cognitive therapy, customized for visual symptoms (MBCT-vision), can treat VSS and modulate dysfunctional VNs. METHODS: An open-label feasibility study for an 8-week MBCT-vision treatment program was conducted. Primary (symptom severity; impact on daily life) and secondary (WHO-5; CORE-10) outcomes at Week 9 and Week 20 were compared with baseline. Secondary MRI outcomes in a subcohort compared resting-state functional and diffusion MRI between baseline and Week 20. RESULTS: Twenty-one participants (14 male participants, median 30 years, range 22-56 years) recruited from January 2020 to October 2021. Two (9.5%) dropped out. Self-rated symptom severity (0-10) improved: baseline (median [interquartile range (IQR)] 7 [6-8]) vs Week 9 (5.5 [3-7], P = 0.015) and Week 20 (4 [3-6], P < 0.001), respectively. Self-rated impact of symptoms on daily life (0-10) improved: baseline (6 [5-8]) vs Week 9 (4 [2-5], P = 0.003) and Week 20 (2 [1-3], P < 0.001), respectively. WHO-5 Wellbeing (0-100) improved: baseline (median [IQR] 52 [36-56]) vs Week 9 (median 64 [47-80], P = 0.001) and Week 20 (68 [48-76], P < 0.001), respectively. CORE-10 Distress (0-40) improved: baseline (15 [12-20]) vs Week 9 (12.5 [11-16.5], P = 0.003) and Week 20 (11 [10-14], P = 0.003), respectively. Within-subject fMRI analysis found reductions between baseline and Week 20, within VN-related FC in the i) left lateral occipital cortex (size = 82 mL, familywise error [FWE]-corrected P value = 0.006) and ii) left cerebellar lobules VIIb/VIII (size = 65 mL, FWE-corrected P value = 0.02), and increases within VN-related FC in the precuneus/posterior cingulate cortex (size = 69 mL, cluster-level FWE-corrected P value = 0.02). CONCLUSIONS: MBCT-vision was a feasible treatment for VSS, improved symptoms and modulated FC of VNs. This study also showed proof-of-concept for intensive mindfulness interventions in the treatment of neurological conditions

Visual dysfunction is a better predictor than retinal thickness for dementia in Parkinson's disease

BACKGROUND: Dementia is a common and devastating symptom of Parkinson's disease (PD). Visual function and retinal structure are both emerging as potentially predictive for dementia in Parkinson's but lack longitudinal evidence. METHODS: We prospectively examined higher order vision (skew tolerance and biological motion) and retinal thickness (spectral domain optical coherence tomography) in 100 people with PD and 29 controls, with longitudinal cognitive assessments at baseline, 18 months and 36 months. We examined whether visual and retinal baseline measures predicted longitudinal cognitive scores using linear mixed effects models and whether they predicted onset of dementia, death and frailty using time-to-outcome methods. RESULTS: Patients with PD with poorer baseline visual performance scored lower on a composite cognitive score (β=0.178, SE=0.05, p=0.0005) and showed greater decreases in cognition over time (β=0.024, SE=0.001, p=0.013). Poorer visual performance also predicted greater probability of dementia (χ² (1)=5.2, p=0.022) and poor outcomes (χ² (1) =10.0, p=0.002). Baseline retinal thickness of the ganglion cell-inner plexiform layer did not predict cognitive scores or change in cognition with time in PD (β=-0.013, SE=0.080, p=0.87; β=0.024, SE=0.001, p=0.12). CONCLUSIONS: In our deeply phenotyped longitudinal cohort, visual dysfunction predicted dementia and poor outcomes in PD. Conversely, retinal thickness had less power to predict dementia. This supports mechanistic models for Parkinson's dementia progression with onset in cortical structures and shows potential for visual tests to enable stratification for clinical trials

Prognostic value of single-subject grey matter networks in early multiple sclerosis

The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict five-year EDSS progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from magnetic resonance imaging (MRI), outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for five years (mean follow-up = 5.0 ± 0.6 years). Expanded Disability Status Scale (EDSS) was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again one year after baseline. Grey matter (GM) atrophy over one year and white matter (WM) lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on GM atrophy measures derived from a statistical parameter mapping (SPM)-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for GM atrophy, WM lesion load and the network measures, and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over five years through lower values for network degree [H(2)=30.0, p<0.001] and global efficiency [H(2)=31.3, p<0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups (H(2)= 1.5, p=0.474). Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of GM atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over GM atrophy and WM lesion load in predicting EDSS worsening (all p-values < 0.05). Our findings provide evidence that GM network reorganization over one year discloses relevant information about subsequent clinical worsening in RRMS. Early GM restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors

Differentiating Multiple Sclerosis From AQP4-Neuromyelitis Optica Spectrum Disorder and MOG-Antibody Disease With Imaging

Background and objectives: Relapsing remitting multiple sclerosis (RRMS), aquaporin4 antibody-positive neuromyelitis optica spectrum disorder (AQP4-NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) may have overlapping clinical features. There is an unmet need for imaging markers that differentiate between them when serologic testing is unavailable or ambiguous. We assessed whether imaging characteristics typical of MS discriminate RRMS from AQP4-NMOSD and MOGAD, alone and in combination. Methods: Adult, non-acute patients with RRMS, APQ4-NMOSD, MOGAD and healthy controls, were prospectively recruited at the National Hospital for Neurology and Neurosurgery (London, UK), and the Walton Centre (Liverpool, UK) between 2014 and 2019. They underwent conventional and advanced brain, cord and optic nerve MRI, and optical coherence tomography. Results: A total of 91 consecutive patients (31 RRMS, 30 APQ4-NMOSD, 30 MOGAD) and 34 healthy controls were recruited. The most accurate measures differentiating RRMS from AQP4-NMOSD were the proportion of lesions with the central vein sign (CVS) (84% vs. 33%, accuracy/specificity/sensitivity: 91/88/93%, p&lt;0.001), followed by cortical lesions (median: 2 [range: 1-14] vs. 1 [0-1], accuracy/specificity/sensitivity: 84/90/77%, p=0.002), and white matter lesions (mean: 39.07 [±25.8] vs. 9.5 [±14], accuracy/specificity/sensitivity: 78/84/73%, p=0.001). The combination of higher proportion of CVS, cortical lesions and optic nerve magnetization transfer ratio reached the highest accuracy in distinguishing RRMS from AQP4-NMOSD (accuracy/specificity/sensitivity: 95/92/97%, p&lt;0.001).The most accurate measures favouring RRMS over MOGAD were: white matter lesions (39.07 [±25.8] vs. 1 [±2.3], accuracy/specificity/sensitivity: 94/94/93%, p=0.006), followed by cortical lesions (2 [1-14] vs. 1 [0-1], accuracy/specificity/sensitivity: 84/97/71%, p=0.004), and retinal nerve fibre layer thickness (RNFL) (mean: 87.54 [±13.83] vs 75.54 [±20.33], accuracy/specificity/sensitivity: 80/79/81%, p=0.009). Higher cortical lesion number combined with higher RNFL thickness best differentiated RRMS from MOGAD (accuracy/specificity/sensitivity: 84/92/77%, p&lt;0.001). Discussion: Cortical lesions, CVS and optic nerve markers achieve a high accuracy in distinguishing RRMS from APQ4-NMOSD and MOGAD. This information may be useful in clinical practice, especially outside the acute phase and when serologic testing is ambiguous or not promptly available. Classification of evidence: This study provides Class II evidence that selected conventional and advanced brain, cord, and optic nerve MRI and OCT markers distinguish adult patients with RRMS from APQ4-NMOSD and MOGAD

Clinical relevance of cortical network dynamics in early primary progressive MS.

BACKGROUND: Structural cortical networks (SCNs) reflect the covariance between the cortical thickness of different brain regions, which may share common functions and a common developmental evolution. SCNs appear abnormal in neurodegenerative conditions such as Alzheimer's and Parkinson's diseases, but have never been assessed in primary progressive multiple sclerosis (PPMS). OBJECTIVE: The aim of this study was to test whether SCNs are abnormal in early PPMS and change over 5 years, and correlate with disability worsening. METHODS: A total of 29 PPMS patients and 13 healthy controls underwent clinical and brain magnetic resonance imaging (MRI) assessments for 5 years. Baseline and 5-year follow-up cortical thickness values were obtained and used to build correlation matrices, considered as weighted graphs to obtain network metrics. Bootstrap-based statistics assessed SCN differences between patients and controls and between patients with fast and slow progression. RESULTS: At baseline, patients showed features of lower connectivity (p = 0.02) and efficiency (p < 0.001) than controls. Over 5 years, patients, especially those with fastest clinical progression, showed significant changes suggesting an increase in network connectivity (p < 0.001) and efficiency (p < 0.02), not observed in controls. CONCLUSION: SCNs are abnormal in early PPMS. Longitudinal SCN changes demonstrated a switch from low- to high-efficiency networks especially among fast progressors, indicating their clinical relevance

Visual dysfunction is a better predictor than retinal thickness for dementia in Parkinson's disease.

BACKGROUND: Dementia is a common and devastating symptom of Parkinson's disease (PD). Visual function and retinal structure are both emerging as potentially predictive for dementia in Parkinson's but lack longitudinal evidence. METHODS: We prospectively examined higher order vision (skew tolerance and biological motion) and retinal thickness (spectral domain optical coherence tomography) in 100 people with PD and 29 controls, with longitudinal cognitive assessments at baseline, 18 months and 36 months. We examined whether visual and retinal baseline measures predicted longitudinal cognitive scores using linear mixed effects models and whether they predicted onset of dementia, death and frailty using time-to-outcome methods. RESULTS: Patients with PD with poorer baseline visual performance scored lower on a composite cognitive score (β=0.178, SE=0.05, p=0.0005) and showed greater decreases in cognition over time (β=0.024, SE=0.001, p=0.013). Poorer visual performance also predicted greater probability of dementia (χ² (1)=5.2, p=0.022) and poor outcomes (χ² (1) =10.0, p=0.002). Baseline retinal thickness of the ganglion cell-inner plexiform layer did not predict cognitive scores or change in cognition with time in PD (β=-0.013, SE=0.080, p=0.87; β=0.024, SE=0.001, p=0.12). CONCLUSIONS: In our deeply phenotyped longitudinal cohort, visual dysfunction predicted dementia and poor outcomes in PD. Conversely, retinal thickness had less power to predict dementia. This supports mechanistic models for Parkinson's dementia progression with onset in cortical structures and shows potential for visual tests to enable stratification for clinical trials