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Background: 1H‐magnetic resonance spectroscopy (1H‐MRS) may provide a direct index for the testing of medicines for neuroprotection and drug mechanisms in multiple sclerosis (MS) through measures of total N‐acetyl‐aspartate (tNAA), total creatine (tCr), myo‐inositol (mIns), total‐choline (tCho), and glutamate + glutamine (Glx). Neurometabolites may be associated with clinical disability with evidence that baseline neuroaxonal integrity is associated with upper limb function and processing speed in secondary progressive MS (SPMS). Purpose: To assess the effect on neurometabolites from three candidate drugs after 96‐weeks as seen by 1H‐MRS and their association with clinical disability in SPMS. Study‐Type: Longitudinal. Population: 108 participants with SPMS randomized to receive neuroprotective drugs amiloride [mean age 55.4 (SD 7.4), 61% female], fluoxetine [55.6 (6.6), 71%], riluzole [54.6 (6.3), 68%], or placebo [54.8 (7.9), 67%]. Field Strength/Sequence: 3‐Tesla. Chemical‐shift‐imaging 2D‐point‐resolved‐spectroscopy (PRESS), 3DT1. Assessment: Brain metabolites in normal appearing white matter (NAWM) and gray matter (GM), brain volume, lesion load, nine‐hole peg test (9HPT), and paced auditory serial addition test were measured at baseline and at 96‐weeks. Statistical Tests: Paired t‐test was used to analyze metabolite changes in the placebo arm over 96‐weeks. Metabolite differences between treatment arms and placebo; and associations between baseline metabolites and upper limb function/information processing speed at 96‐weeks assessed using multiple linear regression models. P‐value<0.05 was considered statistically significant. Results: In the placebo arm, tCho increased in GM (mean difference = −0.32 IU) but decreased in NAWM (mean difference = 0.13 IU). Compared to placebo, in the fluoxetine arm, mIns/tCr was lower (β = −0.21); in the riluzole arm, GM Glx (β = −0.25) and Glx/tCr (β = −0.29) were reduced. Baseline tNAA(β = 0.22) and tNAA/tCr (β = 0.23) in NAWM were associated with 9HPT scores at 96‐weeks. Data Conclusion: 1H‐MRS demonstrated altered membrane turnover over 96‐weeks in the placebo group. It also distinguished changes in neuro‐metabolites related to gliosis and glutaminergic transmission, due to fluoxetine and riluzole, respectively. Data show tNAA is a potential marker for upper limb function. Level of Evidence: 1 Technical Efficacy: Stage 4 [ABSTRACT FROM AUTHOR]

Background: White matter hyperintensities (WMH) are a common occurrence in ageing populations. Their hypointense appearance on T1‐weighted scans is known to affect automated processing and subsequent extraction of volumetric measurements. While lesion infilling with healthy appearing tissue is a common practice in the context of multiple sclerosis, this is not yet the case in studies of neurodegenerative diseases. We investigated the impact of WMH infilling on the volumetric outputs of two automated parcellation pipelines. Method: T1‐weighted and T2‐FLAIR scans were acquired for 462 individuals in the Insight46 neuroimaging substudy of the MRC National Survey for health and Development (NSHD). WMH were automatically segmented and T1‐weighted scans were subsequently infilled over the lesion masks. Two automated parcellation methods Geodesic Information Flows (GIF) and FastSurfer (FS) were run on the two T1‐weighted scans (original and infilled). Cortex, lateral ventricles, basal ganglia (BG), whole brain and total intracranial volumes (TIV) were derived from the four resulting parcellations. Spearman correlation was used to assess the relationship within and across methods according to infilling status. Correlations were also calculated between the observed volumes or percentage difference (original vs infilled volumes) and the WMH burden expressed as percentage of TIV. Result: The relationship between parcellated volumes and WMH burden varied across methods and tissues/brain areas. While there was little observable relationship for GIF between brain volume or TIV difference and WMH, this relationship was positive for FS. The relationship with cortical volume difference was in opposite directions for GIF (negative relationship) and FS (positive relationship), indicating a tendency to decrease the estimated volume when WMH were visible for GIF and increase it for FS. For lateral ventricles and basal ganglia, volumetric measurements were higher in the presence of visible WMH for GIF and FS (Figure1). Correlations between FS and GIF were slightly stronger after infilling but correlation with WMH burden were attenuated (Table2). Conclusion: The impact of presence of visible WMH on T1‐weighted scan varies according to the chosen technique of analysis and strong correlations were observed between the difference in measured volumes and the WMH burden. Infilling should be considered when T1‐weighted derived volumes are studied. [ABSTRACT FROM AUTHOR]

Background: Optic neuropathy is a near ubiquitous feature of Friedreich's ataxia (FRDA). Previous studies have examined varying aspects of the anterior and posterior visual pathways but none so far have comprehensively evaluated the heterogeneity of degeneration across different areas of the retina, changes to the macula layers and combined these with volumetric MRI studies of the visual cortex and frataxin level. Methods: We investigated 62 genetically confirmed FRDA patients using an integrated approach as part of an observational cohort study. We included measurement of frataxin protein levels, clinical evaluation of visual and neurological function, optical coherence tomography to determine retinal nerve fibre layer thickness and macular layer volume and volumetric brain MRI. Results: We demonstrate that frataxin level correlates with peripapillary retinal nerve fibre layer thickness and that retinal sectors differ in their degree of degeneration. We also shown that retinal nerve fibre layer is thinner in FRDA patients than controls and that this thinning is influenced by the AAO and GAA1. Furthermore we show that the ganglion cell and inner plexiform layers are affected in FRDA. Our MRI data indicate that there are borderline correlations between retinal layers and areas of the cortex involved in visual processing. Conclusion: Our study demonstrates the uneven distribution of the axonopathy in the retinal nerve fibre layer and highlight the relative sparing of the papillomacular bundle and temporal sectors. We show that thinning of the retinal nerve fibre layer is associated with frataxin levels, supporting the use the two biomarkers in future clinical trials design. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Background: Neuroimaging biomarkers in large‐scale multimodal studies have proven effective for early diagnosis. Image‐derived phenotypes (IDPs) are summary features derived from modalities such as magnetic resonance imaging (MRI). We provide an overview of the IDPs computed from the European Prevention of Alzheimer Dementia (EPAD) cohort study, a multi‐center European study including multimodal brain MRI. Method: Baseline data from the first 1500 participants of the EPAD LCS were included. The imaging protocol consists of core (3D‐T1w, 3D‐FLAIR, 2D‐T2w, 2D‐T2*), and advanced (resting‐state fMRI, SWI, diffusion MRI, and ASL) sequences. 3D‐T1w IDPs consisted of regional volumes derived from FreeSurfer v6.0 and the Learn Embeddings for Atlas Propagation (LEAP) segmentation pipelines and 3D‐FLAIR IDPs were white matter hyperintensity (WMH) volumes measured by Bayesian Model Selection (BaMoS). Mean functional connectivity of rs‐fMRI networks was extracted with FSL MELODIC and dual‐regression analyses. For diffusion‐weighted imaging, we used FSL TBSS to quantify 48 regional fractional anisotropy (FA) values according to the JHU atlas of white matter tracts. ExploreASL was used to calculate mean Cerebral blood flow (CBF) and spatial coefficient‐of‐variation (sCoV) from ASL images. To evaluate the biological relevance of IDPs, we assessed their relationship with non‐imaging phenotypes. Result: A total of 358 core and 119 advanced IDPs were derived. GM volume was inversely correlated with age, with stronger effects in medio‐temporal areas (Figure 1). Regional WMH volumetrics showed mostly frontal and parietal WM lesions that were associated with aging (Figure 2). Heterogeneous changes in within‐network functional connectivity were observed with older age, with mild differences between CDR 0 and 0.5 contrasts, mostly related to the default‐mode and frontoparietal networks (Figure 3). Age was also associated inversely with skeletonised FA values. Stronger reductions appeared in amyloid positive participants (Figure 4). Conclusion: We show the relevance of IDPs from the EPAD neuroimaging dataset. The observed relationships with non‐imaging phenotypes confirm their biological relevance and are in agreement with previous studies. The proposed IDP framework may constitute a valuable resource for researchers using EPAD data, promoting reproducibility of results and easily adaptable for other studies and cohorts. [ABSTRACT FROM AUTHOR]

Background: Whole‐brain volume loss using T1‐weighted imaging is an established measurement of neurodegeneration. The Boundary Shift Integral (BSI) produces reliable metrics of longitudinal volume loss, used in research studies and clinical trials. With the increased acquisition of volumetric T2‐FLAIR imaging in studies, there is opportunity to produce atrophy metrics using a composite atrophy measure (T1‐weighted and T2‐FLAIR) that is more precise than either modality alone. Method: Data from Alzheimer's Disease Neuroimaging Initiative (ADNI3) were used. Volumetric T1 and T2‐FLAIR weighted imaging were obtained (n = 269), for baseline and follow‐up; Table 1. T1‐weighted images were bias corrected and underwent Geodesic Information Flow (GIF), producing probabilistic brain masks. Masks were resampled to T2‐FLAIR space. The BSI was performed on serial T1‐weighted (gBSI), and T2‐FLAIR (fBSI) separately. We tested differences by diagnosis (control (CN), mild cognitive impairment (MCI) and Alzheimer's disease (AD)) between indirect volume change, gBSI, fBSI, and composite BSI, using linear regression and effect size. We further compared metric differences within diagnosis, using pairwise correlation, paired samples t‐test, and Pitman's test of variance. Result: Demographic information is reported in Table 1. fBSI showed good Pearson's correlation (r) with both gBSI (CN 0.7, p < 0.001; MCI 0.8, p < 0.001; AD 0.9, p < 0.001), and indirect change (CN 0.6, p < 0.001; MCI 0.6, < 0.001; AD 0.7, < 0.001). The composite BSI further showed good correlation with indirect change (CN 0.8, p < 0.001; MCI 0.8, p < 0.001; AD 0.8, 0.004). Mean values by diagnosis are shown in figure 1, along with paired samples t and variance tests. Pairwise comparisons reported in table 2, highlight good diagnostic group separation in all BSI metrics, with overall strong effect sizes for fBSI, composite BSI, and gBSI respectively. Conclusion: Calculating atrophy using T2‐weighted imaging is possible using fBSI. This can be used in a composite atrophy measure using fBSI and gBSI. Both fBSI and composite BSI perform well with group separation, and with good correlations with classical atrophy metrics. Future work will explore the influence of White Matter Hyperintensities on BSI. [ABSTRACT FROM AUTHOR]

Introduction: To date, the clinical relevance of comorbid amyloid‐β (Aβ) pathology in patients with vascular cognitive disorders (VCD) is largely unknown. Methods: We included 218 VCD patients with available cerebrospinal fluid Aβ42 levels. Patients were divided into Aβ+ mild‐VCD (n = 84), Aβ− mild‐VCD (n = 68), Aβ+ major‐VCD (n = 31), and Aβ− major‐VCD (n = 35). We measured depression with the Geriatric Depression Scale, cognition with a neuropsychological test battery and derived white matter hyperintensities (WMH) and gray matter atrophy from MRI. Results: Aβ− patients showed more depressive symptoms than Aβ+. In the major‐VCD group, Aβ− patients performed worse on attention (P =.02) and executive functioning (P =.008) than Aβ+. We found no cognitive differences in patients with mild VCD. In the mild‐VCD group, Aβ− patients had more WMH than Aβ+ patients, whereas conversely, in the major‐VCD group, Aβ+ patients had more WMH. Atrophy patterns did not differ between Aβ+ and Aβ− VCD group. Discussion: Comorbid Aβ pathology affects the manifestation of VCD, but effects differ by severity of VCD. [ABSTRACT FROM AUTHOR]

Background: Research studies of Alzheimer's disease (AD) are increasingly using biomarker‐based criteria to define disease. One useful definition is amyloid positivity since it identifies those on a pathological pathway that likely results in AD. However, within amyloid positive individuals, there is substantial heterogeneity in terms of when biomarkers that measure different aspects of the disease become abnormal. Here we examine this heterogeneity amongst biomarkers that reflect various aspects of the disease process. Method: CSF Amyloid positive individuals' data from Alzheimer's Disease Neuroimaging Initiative (ADNIGO/2/3) were used (n = 376). Demographics were compared across diagnostic group using linear regression and Fisher's exact tests where appropriate, table 1. CSF phosphorylated tau (ptau), whole‐brain and hippocampal volume, Logical memory (LM) and Trails A and B, and white matter hyperintensity (WMH) volumes were used to derive data‐driven subtypes using the z‐score version of the SuStaIn algorithm. The algorithm produced subtypes with differing patterns of biomarker orderings. SuStaIn calculated z‐scores using 86 CSF amyloid negative, APOE e4 non‐carrier healthy controls, selected from ADNIGO/2/3. Proportions of APOE e4 carriers, and clinical diagnosis were compared between subtypes using Fisher's exact test. Result: 359 individuals were grouped into three subtypes (figure 1); determined using cross‐validation. Subtype one (n = 136) had an initial event of WMH, followed by LM, trails A and B, and whole‐brain volume (hippocampal sparing). Subtype two (n = 115) had initial events of ptau, followed by LM and hippocampal volume (tau‐led). Subtype three (n = 108) had initial events of LM total, followed by ptau and hippocampal volume (tau‐late). 17 individuals were not subtyped, as their biomarker data showed no significant abnormalities and would suggest that they are not on an AD pathway. There was a trend towards a higher proportion APOE e4 carriers in subtype one and three (p =.06). There was a significant difference in clinical diagnosis between subtypes (p <.001), with subtype one having a lower proportion of CNs and a higher proportion of MCIs than subtypes two and three (figure 1). Conclusion: We found considerable heterogeneity in biomarker progression sequences in individuals with elevated amyloid. [ABSTRACT FROM AUTHOR]