Cognitive progression independent of relapse in multiple sclerosis.

BACKGROUND: Substantial physical-disability worsening in relapsing-remitting multiple sclerosis (RRMS) occurs outside of clinically recorded relapse. This phenomenon, termed progression independent of relapse activity (PIRA), is yet to be established for cognitive decline. METHODS: Retrospective analysis of RRMS patients. Cognitive decline was defined using reliable-change-index cut-offs for each test (Symbol Digit Modalities Test, Brief Visuospatial Memory Test-Revised, California Verbal Learning Test-II). Decline was classified as PIRA if the following conditions were met: no relapse observed between assessments nor within 9 months of cognitive decline. RESULTS: The study sample (n = 336) was 80.7% female with a mean (standard deviation (SD)) age, disease duration, and observation period of 43.1 (9.5), 10.8 (8.4), and 8.1 (3.1) years, respectively. A total of 169 (50.3%) subjects were cognitively impaired at baseline relative to age-, sex-, and education-matched HCs. Within subjects who experienced cognitive decline (n = 167), 89% experienced cognitive PIRA. A total of 141 (68.1%) cognitive decline events were observed independent of EDSS worsening. Cognitive PIRA was more likely to be observed with increased assessments (p < 0.001) and lower assessment density (p < 0.001), accounting for baseline clinical factors. CONCLUSION: These results establish the concept of cognitive PIRA and further our understanding of progressive cognitive decline in RRMS.

Longitudinal network-based brain grey matter MRI measures are clinically relevant and sensitive to treatment effects in multiple sclerosis.

In multiple sclerosis clinical trials, MRI outcome measures are typically extracted at a whole-brain level, but pathology is not homogeneous across the brain and so whole-brain measures may overlook regional treatment effects. Data-driven methods, such as independent component analysis, have shown promise in identifying regional disease effects but can only be computed at a group level and cannot be applied prospectively. The aim of this work was to develop a technique to extract longitudinal independent component analysis network-based measures of co-varying grey matter volumes, derived from T1-weighted volumetric MRI, in individual study participants, and assess their association with disability progression and treatment effects in clinical trials. We used longitudinal MRI and clinical data from 5089 participants (22 045 visits) with multiple sclerosis from eight clinical trials. We included people with relapsing-remitting, primary and secondary progressive multiple sclerosis. We used data from five negative clinical trials (2764 participants, 13 222 visits) to extract the independent component analysis-based measures. We then trained and cross-validated a least absolute shrinkage and selection operator regression model (which can be applied prospectively to previously unseen data) to predict the independent component analysis measures from the same regional MRI volume measures and applied it to data from three positive clinical trials (2325 participants, 8823 visits). We used nested mixed-effect models to determine how networks differ across multiple sclerosis phenotypes are associated with disability progression and to test sensitivity to treatment effects. We found 17 consistent patterns of co-varying regional volumes. In the training cohort, volume loss was faster in four networks in people with secondary progressive compared with relapsing-remitting multiple sclerosis and three networks with primary progressive multiple sclerosis. Volume changes were faster in secondary compared with primary progressive multiple sclerosis in four networks. In the combined positive trials cohort, eight independent component analysis networks and whole-brain grey matter volume measures showed treatment effects, and the magnitude of treatment-placebo differences in the network-based measures was consistently greater than with whole-brain grey matter volume measures. Longitudinal network-based analysis of grey matter volume changes is feasible using clinical trial data, showing differences cross-sectionally and longitudinally between multiple sclerosis phenotypes, associated with disability progression, and treatment effects. Future work is required to understand the pathological mechanisms underlying these regional changes.

The neural substrates of social cognition deficits in newly diagnosed multiple sclerosis patients.

OBJECTIVE: Cognitive and affective symptoms in multiple sclerosis (MS) can be independently impaired and have different pathways of progression. Cognitive alterations have been described since the earliest MS stages; by contrast, the social cognition (SC) domain has never been investigated in the first year from MS diagnosis. We aimed to evaluate SC and unravel its neural bases in newly diagnosed MS patients. METHODS: Seventy MS patients underwent at diagnosis a 3 T-MRI and a neuropsychological/SC assessment (median time between diagnosis and MRI/cognitive evaluation = 0 months). We tested two matched reference samples: 31 relapsing-remitting MS patients with longer course (mean ± SD disease duration = 7.0 ± 4.5 years) and 38 healthy controls (HCs). Cortical thicknesses (CTh) and volumes of brain regions were calculated. RESULTS: Newly diagnosed MS patients performed significantly lower than HCs in facial emotion recognition (global: p < 0.001; happiness: p = 0.041, anger: p = 0.007; fear: p < 0.001; disgust: p = 0.004) and theory of mind (p = 0.005), while no difference was found between newly diagnosed and longer MS patients. Compared to lower performers, higher performers in facial emotion recognition showed greater volume of amygdala (p = 0.032) and caudate (p = 0.036); higher performers in theory of mind showed greater CTh in lingual gyrus (p = 0.006), cuneus (p = 0.024), isthmus cingulate (p = 0.038), greater volumes of putamen (p = 0.016), pallidum (p = 0.029), and amygdala (p = 0.032); patients with higher empathy showed lower cuneus CTh (p = 0.042) and putamen volume (p = 0.007). INTERPRETATIONS: SC deficits are present in MS patients since the time of diagnosis and remain persistent along the disease course. Specific basal, limbic, and occipital areas play a significant role in the pathogenesis of these alterations.

Association of Levels of CSF Osteopontin With Cortical Atrophy and Disability in Early Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: To evaluate CSF inflammatory markers with accumulation of cortical damage as well as disease activity in patients with early relapsing-remitting MS (RRMS). METHODS: CSF levels of osteopontin (OPN) and 66 inflammatory markers were assessed using an immune-assay multiplex technique in 107 patients with RRMS (82 F/25 M, mean age 35.7 ± 11.8 years). All patients underwent regular clinical assessment and yearly 3T MRI scans for 2 years while 39 patients had a 4-year follow-up. White matter lesion number and volume, cortical lesions (CLs) and volume, and global cortical thickness (CTh) were evaluated together with the 'no evidence of disease activity' (NEDA-3) status, defined by no relapses, no disability worsening, and no MRI activity, including CLs. RESULTS: The random forest algorithm selected OPN, CXCL13, TWEAK, TNF, IL19, sCD30, sTNFR1, IL35, IL16, and sCD163 as significantly associated with changes in global CTh. OPN and CXCL13 were most related to accumulation of atrophy after 2 and 4 years. In a multivariate linear regression model on CSF markers, OPN (p < 0.001), CXCL13 (p = 0.001), and sTNFR1 (p = 0.024) were increased in those patients with accumulating atrophy (adjusted R-squared 0.615). The 10 markers were added in a model that included all clinical, demographic, and MRI variables: OPN (p = 0.002) and IL19 (p = 0.022) levels were confirmed to be significantly increased in patients developing more CTh change over the follow-up (adjusted R-squared 0.619). CXCL13 and OPN also revealed the best association with NEDA-3 after 2 years, with OPN significantly linked to disability accumulation (OR 2.468 [1.46-5.034], p = 0.004) at the multivariate logistic regression model. DISCUSSION: These data confirm and expand our knowledge on the prognostic role of the CSF inflammatory profile in predicting changes in cortical pathology and disease activity in early MS. The data emphasize a crucial role of OPN.

Determinants and Biomarkers of Progression Independent of Relapses in Multiple Sclerosis.

Clinical, pathological, and imaging evidence in multiple sclerosis (MS) suggests that a smoldering inflammatory activity is present from the earliest stages of the disease and underlies the progression of disability, which proceeds relentlessly and independently of clinical and radiological relapses (PIRA). The complex system of pathological events driving "chronic" worsening is likely linked with the early accumulation of compartmentalized inflammation within the central nervous system as well as insufficient repair phenomena and mitochondrial failure. These mechanisms are partially lesion-independent and differ from those causing clinical relapses and the formation of new focal demyelinating lesions; they lead to neuroaxonal dysfunction and death, myelin loss, glia alterations, and finally, a neuronal network dysfunction outweighing central nervous system (CNS) compensatory mechanisms. This review aims to provide an overview of the state of the art of neuropathological, immunological, and imaging knowledge about the mechanisms underlying the smoldering disease activity, focusing on possible early biomarkers and their translation into clinical practice. ANN NEUROL 2024;96:1-20.

Networks of microstructural damage predict disability in multiple sclerosis.

BACKGROUND: Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods. METHODS: We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures. RESULTS: We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001). CONCLUSIONS: GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials.

Treatment Effect on Brain Atrophy Correlates with Treatment Effect on Cognition in Multiple Sclerosis.

OBJECTIVE: The purpose of this study was to evaluate the extent to which treatment effect on magnetic resonance imaging (MRI)-derived measures of brain atrophy and focal lesions can mediate, at the trial level, the treatment effect on cognitive outcomes in multiple sclerosis (MS). METHODS: We collected all published randomized clinical trials in MS lasting at least 2 years and including as end points: active MRI lesions (defined as new/enlarging T2 lesions), brain atrophy (defined as a change in brain volume between month 12 and month 24), and change in cognitive performance (assessed by the Paced Auditory Serial Addition Test [PASAT]). Relative reductions were used to quantify the treatment effect on MRI markers (lesions and atrophy), whereas the standardized mean difference (Hedges g) between baseline and follow-up cognitive assessment was used to quantify the treatment effects on cognition. A linear regression, weighted for trial size, was used to assess the relationship between the treatment effects on MRI markers and cognition. RESULTS: Fourteen trials including more than 8,813 patients with MS were included in the meta-regression. Treatment effect on cognition was strongly associated with the treatment effect on brain atrophy (R2 = 0.79, p < 0.001), but was not correlated with the treatment effect on active MRI lesions (R2 = 0.16, p = 0.14). INTERPRETATION: Results reported here suggest that brain atrophy, a well-established MRI marker in MS clinical trials, can be used as a main outcome for clinical trials with drugs targeting cognitive impairment and neurodegeneration. ANN NEUROL 2023;94:925-932.

Assessment of Tau Pathology as Measured by 18F-THK5317 and 18F-Flortaucipir PET and Their Relation to Brain Atrophy and Cognition in Alzheimer's Disease.

BACKGROUND: In Alzheimer's disease (AD), the abnormal aggregation of hyperphosphorylated tau leads to synaptic dysfunction and neurodegeneration. Recently developed tau PET imaging tracers are candidate biomarkers for diagnosis and staging of AD. OBJECTIVE: We aimed to investigate the discriminative ability of 18F-THK5317 and 18F-flortaucipir tracers and brain atrophy at different stages of AD, and their respective associations with cognition. METHODS: Two cohorts, each including 29 participants (healthy controls [HC], prodromal AD, and AD dementia patients), underwent 18F-THK5317 or 18F-flortaucipir PET, T1-weighted MRI, and neuropsychological assessment. For each subject, we quantified regional 18F-THK5317 and 18F-flortaucipir uptake within six bilateral and two composite regions of interest. We assessed global brain atrophy for each individual by quantifying the brain volume index, a measure of brain volume-to-cerebrospinal fluid ratio. We then quantified the discriminative ability of regional 18F-THK5317, 18F-flortaucipir, and brain volume index between diagnostic groups, and their associations with cognition in patients. RESULTS: Both 18F-THK5317 and 18F-flortaucipir outperformed global brain atrophy in discriminating between HC and both prodromal AD and AD dementia groups. 18F-THK5317 provided the highest discriminative ability between HC and prodromal AD groups. 18F-flortaucipir performed best at discriminating between prodromal and dementia stages of AD. Across all patients, both tau tracers were predictive of RAVL learning, but only 18F-flortaucipir predicted MMSE. CONCLUSION: Our results warrant further in vivo head-to-head and antemortem-postmortem evaluations. These validation studies are needed to select tracers with high clinical validity as biomarkers for early diagnosis, prognosis, and disease staging, which will facilitate their incorporation in clinical practice and therapeutic trials.

Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes.

OBJECTIVE: In multiple sclerosis (MS), MRI measures at the whole brain or regional level are only modestly associated with disability, while network-based measures are emerging as promising prognostic markers. We sought to demonstrate whether data-driven patterns of covarying regional grey matter (GM) volumes predict future disability in secondary progressive MS (SPMS). METHODS: We used cross-sectional structural MRI, and baseline and longitudinal data of Expanded Disability Status Scale, Nine-Hole Peg Test (9HPT) and Symbol Digit Modalities Test (SDMT), from a clinical trial in 988 people with SPMS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA). We repeated ICA on 400 healthy controls. We used survival models to determine whether baseline patterns of covarying GM volume measures predict cognitive and motor worsening. RESULTS: We identified 15 patterns of regionally covarying GM features. Compared with whole brain GM, deep GM and lesion volumes, some ICA components correlated more closely with clinical outcomes. A mainly basal ganglia component had the highest correlations at baseline with the SDMT and was associated with cognitive worsening (HR=1.29, 95% CI 1.09 to 1.52, p<0.005). Two ICA components were associated with 9HPT worsening (HR=1.30, 95% CI 1.06 to 1.60, p<0.01 and HR=1.21, 95% CI 1.01 to 1.45, p<0.05). ICA measures could better predict SDMT and 9HPT worsening (C-index=0.69-0.71) compared with models including only whole and regional MRI measures (C-index=0.65-0.69, p value for all comparison <0.05). CONCLUSIONS: The disability progression was better predicted by some of the covarying GM regions patterns, than by single regional or whole-brain measures. ICA, which may represent structural brain networks, can be applied to clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect.

A study of within-subject reliability of the brain's default-mode network.

OBJECTIVE: Resting-state functional magnetic resonance imaging (fMRI) is promising for Alzheimer's disease (AD). This study aimed to examine short-term reliability of the default-mode network (DMN), one of the main haemodynamic patterns of the brain. MATERIALS AND METHODS: Using a 1.5 T Philips Achieva scanner, two consecutive resting-state fMRI runs were acquired on 69 healthy adults, 62 patients with mild cognitive impairment (MCI) due to AD, and 28 patients with AD dementia. The anterior and posterior DMN and, as control, the visual-processing network (VPN) were computed using two different methodologies: connectivity of predetermined seeds (theory-driven) and dual regression (data-driven). Divergence and convergence in network strength and topography were calculated with paired t tests, global correlation coefficients, voxel-based correlation maps, and indices of reliability. RESULTS: No topographical differences were found in any of the networks. High correlations and reliability were found in the posterior DMN of healthy adults and MCI patients. Lower reliability was found in the anterior DMN and in the VPN, and in the posterior DMN of dementia patients. DISCUSSION: Strength and topography of the posterior DMN appear relatively stable and reliable over a short-term period of acquisition but with some degree of variability across clinical samples.