INTRODUCTION Neuropsychiatric symptoms (NPSs) are common in dementia, but their patterns in preclinical stages remain unclear. This study identified NPS clusters and associated health factors in a geriatric clinical population.METHODS We analyzed 1234 participants from the Italian GERIatric COgnitive evaluation memory clinic cohort with Neuropsychiatric Inventory data. Clusters were derived using machine learning (K-means, Elbow method) separately for dementia and dementia-free groups. Associations were assessed via multinomial logistic regression.RESULTS In the overall cohort, four NPS clusters emerged: minimal NPS, depression-anxiety-apathy, depression-anxiety, and delusions-agitation-irritability. Cluster profiles differed between the dementia and dementia-free groups. Specific clinical and metabolic factors - lipid abnormalities, glycemic control, thyroid dysfunction, and underweight status - were differentially associated with NPS clusters.DISCUSSION Distinct NPS patterns exist across the dementia continuum. These clusters differ in demographic, cognitive, functional, and metabolic profiles, suggesting NPS may precede cognitive decline and represent syndromic entities with diagnostic relevance. Multidimensional, personalized approaches are needed.

Over the past few years, several fluid biomarker candidates have been proposed for frontotemporal dementia (FTD). We have previously identified CSF proteins that could separate individuals with genetic FTD from controls. However, it is unknown whether alterations in these CSF protein levels are associated with neurodegenerative processes. The aim of this study was to explore how these CSF biomarker candidates correlate with symptom severity as well as cortical and subcortical atrophy. The levels of fourteen proteins were measured in CSF from 202 individuals, 131 mutation carriers with mutations in C9orf72, GRN, or MAPT, and 71 controls, in a cross-sectional subset from the GENFI cohort. The association between the levels of these proteins and CDR plus NACC FTLD-NM sum-of-boxes, cortical thickness, and subcortical volumes were estimated in the mutation carriers. Elevated CSF levels of five out of fourteen proteins were associated with an increased CDR score in the mutation carriers. Additionally, elevated levels of three of these proteins, NEFM, PTPRN2 and SERPINA3, were associated with reduced cortical thickness and/or subcortical volume among all mutation carriers. Some mutation-specific associations were also observed, with SPP1 and CTSS being associated with CDR and atrophy only in MAPT mutation carriers, while NPTX2 was specific for GRN mutation carriers. As indicated by the association to brain atrophy, the proposed fluid biomarker candidates continue to show promise and additional studies will further elucidate their relationship to cortical atrophy in genetic FTD, and their potential as biomarkers for diagnosis, prognosis, and disease staging.

Background:Women face greater vulnerability to dementia and Alzheimer's disease (AD), potentially due to estrogen fluctuations across the lifespan. However, its role in vascular brain health is unclear. We investigated associations between lifelong estrogen exposure-endogenous (reproductive span) and exogenous (oral contraceptives [OC], menopausal hormone therapy [MHT])-and late-life vascular brain injury, AD-related atrophy, and APOE-ε4 modification. Methods and findings:We included 352 cognitively unimpaired 70-years-old women from the Gothenburg H70-1944 Birth Cohort with brain MRI and 5-year follow-up. Reproductive lifespan was calculated as age at menopause or oophorectomy minus age at menarche. OC and MHT use were self-reported. Outcomes included cerebral small vessel disease (SVD), AD-related cortical thickness, and white-matter integrity (fractional anisotropy). Linear and multinomial regression and mixed-effects models were adjusted for confounders and stratified by APOE-ε4.Longer reproductive span (OR=0.90 [95%CI 0.83-0.98]) and MHT use (OR=0.43 [95% CI 0.20-0.92]) were linked to lower SVD burden, particularly fewer perivascular spaces and microbleeds. OC and MHT were associated with greater white matter integrity, with additive use throughout life showing the highest fractional anisotropy (OR=0.45 [95% CI 0.12-0.78]). MHT use was associated with greater thickness in areas often affected in AD among APOE-ε4 carriers (β=0.38 [95% CI 0.01-0.76]) but not in non-carriers. Longer estrogen exposure was linked to stable cortical thickness and WMH trajectories over time. Conclusions:Extended estrogen exposure throughout life-both endogenous and exogenous-appear to support late-life cerebrovascular health in women, with potential genotype-specific neuroprotective effects. Given the current absence of sex-specific prevention guidelines for cognitive disorders, future research should clarify estrogen's longterm impact on brain health and cognition to inform personalized medicine.

Brain tissue segmentation is vital in Alzheimer's and dementia research for creating detailed neuroanatomical maps, diagnosing early-stage neurodegeneration, and guiding interventions. Although MRI remains the standard approach for its superior soft-tissue contrast, CT is a more accessible imaging modality in acute and resource-constrained settings. This study utilized paired CT-MRI datasets from the Gothenburg H70 Birth Cohort ( N  = 733) and the Memory Clinic Cohort of the National University Hospital, Singapore (NUS Dementia Cohort, N  = 210) to train and evaluate advanced segmentation models— nnUNet (2D & 3D models for 300-1000 epochs) and MedNeXt (3D- Small, Base, Medium and Large models for 3x3x3 & 5x5x5 kernels). MRI-derived labels were employed to guide CT segmentation, allowing accurate delineation of brain tissue segmentation (Gray Matter: GM, White Matter: WM and Cerebrospinal Fluid: CSF). Evaluation was conducted on all axial datasets for all variations of the models and for coronal & sagittal orientations the best performing models were utilized for inference. The 3D nnU-Net achieved average Dice Similarity Coefficients (DSCs) of 0.82, 0.72, and 0.76 for axial, coronal, and sagittal orientations, respectively, while MedNeXt demonstrated slightly superior performance with DSCs of 0.83, 0.73, and 0.78. MedNeXt also exhibited improved volumetric similarity in axial datasets, with scores ranging from 0.842 (CSF, sagittal) to 0.992 (WM, axial). When applied to dementia cohorts, MedNeXt achieved higher generalizability with an average DSC and volumetric similarity of 0.73 and 0.912, compared to 0.70 and 0.854 for nnU-Net. Extended training (1000 epochs) enhanced nnU-Net's performance, yet MedNeXt displayed superior scalability, handling larger kernel sizes and multi-modal imaging scenarios. However, significantly longer training times of up to 288 hours was required for the largest model. Automated CT brain segmentation guided by MRI-derived labels demonstrates clinically acceptable segmentation performance on untrained dementia cohort. nnU-Net is more resource-efficient and suitable for limited-resource settings, while MedNeXt has higher accuracy excelling in multi-orientation and multi-modal datasets. These findings validate the feasibility of using CT imaging with advanced segmentation frameworks to develop accessible neuroimaging tools for Alzheimer's and dementia research, addressing diagnostic challenges across diverse clinical contexts.

Alzheimer's disease (AD) is neuropathologically defined by amyloid-beta (Aβ) plaques and tau neurofibrillary tangles. However, co-pathologies and other pathobiological processes are involved in the pathogenesis of AD, contributing to neurodegeneration and clinical symptoms. The most common co-pathologies in people with AD are alpha-synucleinopathy, vascular brain injury and transactive response DNA-binding protein of 43 kDa-related pathology. Neuroinflammation, iron accumulation, cholinergic dysfunction and cellular senescence are recognized pathobiological processes beyond Aβ- and tau-related pathology. However, the exact mechanisms by which these co-pathologies and pathobiological processes contribute to the neurodegeneration and clinical symptoms in people with AD remain unclear. The individual combination of these co-pathologies and pathobiological processes increases phenotypical heterogeneity in people with AD. This highlights the unmet need to advance their current understanding, and the field strives to develop accurate biomarkers for personalized assessment and investigation. Elucidating this biologic-clinical complexity and heterogeneity is crucial for increasing our current understanding of AD, with implications for diagnosis, prognosis and therapeutics.

Lecanemab and donanemab are the first anti-Aβ treatments to receive approval in Europe. Eligibility criteria are strict, eg., APOE ε4/4 carriers are excluded. Successful implementation in public healthcare hinges on accurate estimates of eligibility rates in settings which will be the first to roll out the treatments (specialized memory clinics with early disease stages). We applied the appropriate use recommendations (AUR) to assess treatment eligibility in a Swedish tertiary memory clinic where Aβ and APOE assessments are routinely performed. Of the full cohort (N = 410), 26 and 25 patients met the AUR criteria for lecanemab and donanemab, respectively (6 %; partial overlap between the groups). After excluding APOE ε4/4 carriers in line with the European guidelines, only 14 and 13 patients remained eligible (3 %). In clinics with younger populations, a significant percentage of potentially eligible patients are likely to have the APOE ε4/4 genotype. These findings are important to inform the implementation of anti-Aβ treatments.

Background and Objectives:Interpretation of brain atrophy in multiple sclerosis (MS) relies on group-level research and lacks individualized reference standards. Normative modelling can enable patient-level assessments of regional brain volumes relative to population expectations. Methods:We constructed age-, sex-, and intracranial volume-adjusted normative models of regional cortical and subcortical FreeSurfer-estimated brain volumes and applied to data from a concluded clinical trial and routine hospital examinations to derive regional deviation Z-scores and counts of critical deviations (Z < -1.96). Associations with disability (Expanded Disability Status Scale [EDSS]), cognitive performance (Paced Auditory Serial Addition Test [PASAT]), and fatigue (Fatigue Severity Scale [FSS]) were examined cross-sectionally and longitudinally using fixed and random effects models. A deviation-based stratification rule was evaluated for disability progression and relapse risk using survival analyses. Results:Models were trained on 62,444 MRI datasets from healthy individuals across the lifespan (50.8% females, age range 6.0-90.1) and applied to 953 longitudinal MRI scans from 362 people with MS (mean age = 38.8±9.7, 70.5% females, follow-up up to 12 years). People with MS exhibited a higher number of critical deviations than matched controls (incidence rate ratio 2.70, 95% CI 2.21-3.30), most prominently in the thalamus (approximately 25% of patients). A higher number of deviations was associated with higher disability (EDSS) indicated by a cross-sectional (β=0.24, 95% CI 0.14-0.34) and longitudinal main effect (β=0.07, 95% CI 0.02-0.13). Lower-than-reference volumes in the thalamus, hippocampus, and putamen were consistently associated with higher disability cross-sectionally (β standardized = -0.17 to -0.23) and over time (β standardized = -0.14 to -0.18). Deviation-based risk stratification identified patients with modestly higher disability trajectories (β=0.13, 95% CI 0.03-0.24). Discussion:Normative modelling reveals a heterogeneous morphometric deviation profile in MS, centred on deep grey matter structures and associated with disability accumulation. These findings support the use of population-referenced MRI metrics for individual-level phenotyping in MS and warrant validation in independent cohorts.

Brain imaging is essential in the diagnostic workup of cognitive disorders. Computed tomography (CT) is usually the first-line method due to accessibility and patient comfort, whereas magnetic resonance imaging (MRI) offers higher diagnostic precision and is required before anti-amyloid therapy. MRI adds value by detecting microvascular pathology, enabling volumetric analysis, and ensuring safe monitoring of amyloid-related imaging abnormalities (ARIA). National Swedish MRI protocols and structured reporting templates support harmonized diagnostics and follow-up. Nuclear medicine methods are useful for complex cases to assess glucose metabolism, amyloid burden or dopamine transport function. With emerging treatment options for Alzheimer's disease, standardized imaging and close collaboration across specialties are essential for upscaling diagnostic routines and for safe and efficient care.

Aging is the strongest risk factor for Alzheimer's disease (AD). Identifying reliable biomarkers of brain aging helps to predict functional decline and dementia onset. Evaluations of aging-related biomarkers in plasma and neuronal-derived extracellular vesicles (nEVs) from cognitively healthy and AD subjects, alongside post-mortem IPL brain samples from control (Ctr), pre-clinical AD (PCAD), mild cognitive impairment (MCI) and AD cases were performed. Cognitive tests, functional assessments, and MRI data were also included. Biomarkers in nEVs more accurately reflected brain pathology than those measured in plasma and showed stronger associations with cognitive and functional decline. Sex-specific patterns also emerged: GDF-15 was higher in nEVs from females with AD, whereas IL-6, IL-18 and Jag-1 were higher in nEVs from males with AD. A minimal nEV-derived panel including lower GDF-11 and higher GDF-15, Jag-1 and Leptin (after correction for age and sex) discriminated AD from Ctr and was associated with MRI-determined cortical atrophy in regions vulnerable to AD. These markers captured aging-related molecular trajectories that were disrupted in AD, and key associations observed in nEVs were confirmed in post-mortem brain tissue. Our results suggests that nEV-derived biomarkers capture early, brain-specific and sex-modulated aging signatures, providing superior sensitivity compared to plasma. Their convergence with post-mortem findings underscores their biological validity and translational potential. These results highlight the value of nEVs for stratifying individuals at higher risk of AD and support their integration into precision medicine approaches for dementia prevention.

BACKGROUND:Lewy body diseases (LBD) collectively share α-synuclein Lewy pathology, yet present wide clinical heterogeneity, with overlapping motor and non-motor features and progression patterns that challenge traditional diagnostic boundaries. METHODS:To resolve this spatiotemporal heterogeneity at the biological level, we applied a data-driven atrophy progression framework to MRI data from 833 individuals across Parkinson's disease, dementia with Lewy bodies, and prodromal isolated REM sleep behaviour disorder using the Subtype and Stage Inference algorithm. FINDINGS:Four transdiagnostic subtypes (A: Early cortico-limbic/late basal ganglia, B: Early basal ganglia/late limbic, C: Early temporo-limbic/late basal ganglia, and D: Early basal ganglia-cingulate/late cortex) emerged, each defined by a distinct spatiotemporal progression of atrophy that explained cognitive, motor, and psychiatric variability. An early cortico-limbic/late basal ganglia subtype represented a dementia-prone subtype across clinical diagnoses, with limbic involvement associating with the emergence of visual hallucinations. INTERPRETATION:These biologically relevant spatiotemporal atrophy subtypes provide an interpretable stratification of patients with LBD, with the potential to refine prognosis, improve clinical trial stratification, and guide precision therapeutic approaches. FUNDING:This work was made possible by an Ignition grant from the University of Sydney and University College London (Global Engagement Fund).

Brain computed tomography (CT) is an accessible and commonly utilized technique for assessing brain structure. In cases of idiopathic normal pressure hydrocephalus (iNPH), the presence of ventriculomegaly is often neuroradiologically evaluated by visual rating and manual measurement of each image. Previously, we have developed a deep-learning-model that utilizes transfer learning from magnetic resonance imaging (MRI) for CT-based intracranial tissue segmentation. Accordingly, herein we aimed to enhance the segmentation of ventricular cerebrospinal fluid (VCSF) in brain CT scans and assess the performance of automated brain CT volumetrics in iNPH patient diagnostics. This retrospective study employed a two-stage approach in developing the model. Initially, a 2D U-Net model was trained to predict VCSF segmentations from CT scans, using paired MR-VCSF labels from healthy controls. This model was subsequently refined by incorporating manually segmented lateral CT-VCSF labels from iNPH patients, building on the features learned from the initial U-Net model. The training dataset included 734 CT datasets from healthy controls paired with T1-weighted MRI scans from the Gothenburg H70 Birth Cohort Studies and 62 CT scans from iNPH patients at Uppsala University Hospital. To validate the model's performance across diverse patient populations, external clinical images including scans of 11 iNPH patients from the Universitätsmedizin Rostock, Germany, and 30 iNPH patients from the University of Alabama at Birmingham, United States were used. Further, we obtained three CT-based volumetric measures (CTVMs) related to iNPH. Our analyses demonstrated strong volumetric correlations (ρ = 0.91, P < 0.001) between automatically and manually derived CT-VCSF measurements in iNPH patients. Based on the ventricular volume, the CTVMs exhibited high accuracy in differentiating iNPH patients from controls in external clinical datasets with an AUC of 0.97 (95% CI: 0.94-1.00) and in the Uppsala University Hospital datasets with an AUC of 0.99 (95% CI: 0.98-1.00). CTVMs derived through deep learning show potential for assessing and quantifying morphological features in hydrocephalus. Critically, these measures performed comparably to gold-standard neuroradiology assessments in iNPH patients and healthy controls, even in the presence of intraventricular shunt catheters. Accordingly, such an approach may serve to improve the radiological evaluations of the diagnostic work-up and treatment response monitoring in patients with hydrocephalus. Since CT is much more widely available than MRI, our results have considerable clinical impact.

INTRODUCTION:Brain magnetic resonance imaging (MRI) biomarkers for dementia exist, but little is known about their association with future frailty. We investigated whether baseline brain MRI findings associate with pre-frailty/frailty over 11 years. METHODS:One hundred twenty participants, aged 60 to 77 years, in the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) had baseline MRI data. Frailty status (Fried phenotype) was measured at baseline, and at 2, 7, and 11 years. Risk of future pre-frailty/frailty per one standard deviation or one class greater volume/thickness/Fazekas score in baseline MRI was evaluated. RESULTS:Pre-frailty/frailty was not associated with MRI biomarkers at baseline. Smaller left hippocampal volume was associated with pre-frailty/frailty at 2 (p = 0.042) and 7 years (p = 0.017), and higher load of periventricular white matter hyperintensities (WMHs) at 2 years (p = 0.048), independently of baseline cognition. DISCUSSION:Smaller left hippocampal volume and higher periventricular WMH score in brain MRI may indicate future frailty risk. CLINICAL TRIAL REGISTRATION NUMBER:The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) is registered at ClinicalTrials.gov (no. NCT01041989).

Brain atrophy subtypes are increasingly recognized in Alzheimer’s disease (AD) dementia. However, their relevance across the real-world memory clinic spectrum, from subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) to AD and non-AD dementias, remains unclear. This cross-sectional study aimed to identify MRI-based atrophy subtypes in a relatively young memory clinic and examine associations with demographic, cerebrospinal fluid (CSF) biomarkers, and cerebrovascular burden to inform precision medicine approaches. We included all consecutive patients (SCI to dementia), evaluated at the Karolinska University-Hospital Memory Clinic (Stockholm, Sweden) between 2018 and 2023 with available clinical and 3T MRI data. Subtypes were defined using FreeSurfer-derived volumetric measures and a validated algorithm combining categorical classification (typical, limbic predominant, cortical predominant, minimal atrophy) with continuous indices of typicality (cortical predominant–limbic predominant) and severity (minimal atrophy–typical). Demographics, cognitive profiles, APOE ε4 status, CSF biomarkers (Aβ42, Aβ42/40, phosphorylated [p]-tau181, total tau, neurofilament light chain [NFL]), and cerebrovascular burden were compared across subtypes. Analyses were replicated in Aβ-positive individuals and those eligible for anti-Aβ therapy. Among 809 patients (median age 60.0 years [interquartile-range 56.0–63.0], 56.1

Regional brain atrophy has been observed in dementia with Lewy bodies (DLB), yet determinants of regional vulnerability remain unclear. Using imaging transcriptomics, we examined whether normative gene expression patterns relate to regional atrophy in DLB. We included 164 DLB patients (49 women) and 164 age- and sex-matched healthy controls from three European centres and the Mayo Clinic, USA. Volumetric atrophy was quantified from T1-weighted MRI across 58 left-hemispheric regions using w-scores. Normative expression of twelve genes implicated in alpha-synuclein, beta-amyloid, and tau pathology was extracted from the Allen Human Brain Atlas. DLB patients showed diffuse atrophy across most regions. In the full cohort, normative expression of MAPT, PINK1, and PSEN2 predicted regional atrophy after correction for spatial autocorrelation, although none survived multiple-testing correction. In the Mayo Clinic sub-cohort, expression of APP, BIN1, GBA, MAPT, PINK1, SNCA, and TMEM175 significantly predicted atrophy and survived multiple-testing correction. Random forest models did not outperform spatial null models in the full cohort, but PARK7, PINK1, and PSEN2 consistently emerged as important predictors. A significant global model was observed in the Mayo Clinic sub-cohort, driven by GBA, LRP1, and PINK1. These findings suggest that normative gene expression partially contributes to regional brain atrophy in DLB.

BACKGROUND: Cognitive processes are essential for efficient daily functioning. Demographic factors such as age and education influence cognitive performance. However, the impact of sex on cognition is less understood and previous research has reported inconsistent findings. We investigated sex differences in cognitively unimpaired adults in three cohorts, using two complimentary approaches: a univariate approach to compare direct performance across cognitive domains and the multivariate approach of graph theory to compare global and nodal features as well as the modular organization of cognitive connectomes. METHODS: We included 4,259 cognitively unimpaired participants (334 from the GENIC cohort, 3,703 from the National Alzheimer’s Coordinating Center [NACC], and 222 from the Alzheimer’s Disease Neuroimaging Initiative [ADNI]). Cognitive variables were corrected for age and education, and cognitive connectomes were constructed using Spearman correlation coefficients. Sex differences in cognitive performance were examined through ANCOVAs as well as global and nodal network measures. RESULTS: Univariate analyses showed significant sex differences in three out of five cognitive domains across cohorts, mainly of small effect sizes. Graph theory analyses revealed minimal sex differences in cognitive module organization and no significant differences on global network measures, except for a higher modularity observed in women compared to men in the NACC. In contrast, nodal analyses revealed sex differences in several network measures. CONCLUSIONS: Sex differences in cognition seem to be of small effect size and limited to specific cognitive domains or cognitive variables, while the overall organization and global features of cognitive connectomes were largely comparable between men and women. Future studies should clarify whether men and women may rely on slightly different cognitive strategies to approach cognitive tasks without overt differences in cognitive ability.

Biological heterogeneity in cognitively impaired individuals has been described by distinct hypometabolic (FDG-PET) and atrophy (MRI) corticolimbic neurodegeneration patterns. However, the neuroimaging modalities can show different patterns at the individual-level. This study investigated whether postmortem neuropathologies may explain these differences. The study includes 245 individuals, 69 with cognitive impairment who underwent in vivo neuroimaging and neuropathological assessment and 176 cognitively unimpaired individuals as a reference group. Neurodegeneration patterns were identified in both in vivo FDG-PET and MRI, and their link to postmortem AD (amyloid-beta, tau) and non-AD (CAA, alpha-synuclein, TDP-43, and hippocampal sclerosis) pathologies was examined. In vivo individual-level differences of hypometabolic and atrophy neurodegeneration patterns could be associated to different AD and non-AD pathologies postmortem. The patterns significantly differed by neuropathology and neuronal loss at the regional-level but not global-level. Specifically, limbic predominant atrophy was related to distant (neocortical) amyloid, tau, and arteriolosclerosis while limbic predominant hypometabolism with local (mediotemporal) tau and arteriolosclerosis. Both limbic atrophy and hypometabolism reflected local (mediotemporal) TDP-43 and neuronal loss, with limbic hypometabolism additionally reflecting neocortical neuronal loss. Cortical predominant atrophy and hypometabolism were correlated with local (neocortical) alpha synuclein. Neurodegeneration patterns differentially reflect underlying pathologies. Specifically, limbic predominant patterns were more frequently associated with TDP-43 pathology and hippocampal sclerosis, whereas cortical predominant patterns more often reflected cerebrovascular disease and alpha-synuclein pathology. Differences between corticolimbic hypometabolic and atrophy patterns were observed only in cases with postmortem-confirmed AD pathology, suggesting that non-AD pathologies (TDP-43, hippocampal sclerosis, cerebrovascular disease, and alpha-synuclein) may differentially modify AD-related neurodegeneration.

BACKGROUND:Grey matter volume loss (GM-VL) is an accurate marker of multiple sclerosis (MS)-related progression. However, long-term comparisons of GM-VL between MS and healthy controls (HC) are rare, and the brain regions with most significant GM-VL and their clinical importance still need robust and longitudinal validation. METHODS:This multi-cohort longitudinal observational study used two relapsing remitting MS (RRMS) cohorts (N = 386, T1w-scans=940) sampled for up-to-12 years to localise grey matter volume loss (GM-VL) and disease progression (Expanded Disability Status Scale (EDSS), Paced Auditory Serial Addition Test (PASAT), Fatigue Severity Scale (FSS)). The identified region-specific significant GM-VL was compared with 2163 HCs (T1w-scans=4326). RESULTS:The strongest, replicable, significant patterns of brain GM-VL in RRMS were found in the frontal lobes, specifically, in the superior frontal cortex (SFC, βage≤-0.27), pars orbitalis (βage≤-0.25), and thalami (βage≤-0.20). Compared with healthy controls (HCs) >20 years older, MS showed greater GM-VL in the right SFC, caudal-middle frontal cortex, caudate, and left frontal pole (all Z > 2.08, p < 0.019). The overlap of associations between volumetric and clinical outcome changes was limited to EDSS, which was significantly related to left hippocampal volumes βEDSS≤-0.19 in both cohorts. CONCLUSIONS:Our findings indicate GM-VL in people with RRMS comparable to 20-year older HC, and stronger in the SFC and thalamus, and cohort-specific relationships with disability-progression.

BackgroundDifferentiating individuals with mild cognitive impairment who convert to dementia due to Alzheimer's disease (MCI-C) from those who do not convert (MCI-NC) is increasingly important. Functional connectivity (FC) derived from resting state functional MRI (rs-fMRI) has been investigated as a potential biomarker. However, improved data analysis strategies are needed. One underexplored approach is pairwise voxel-to-voxel analysis.ObjectiveTo describe differences in FC between amyloid positive MCI-C and MCI-NC using a whole-cortex voxel-to-voxel pairwise approach.MethodsBaseline rs-fMRI from the Alzheimer's Disease Neuroimaging Initiative was retrieved for amyloid positive MCI participants. Voxel-to-voxel, pairwise, cortical FC was computed. Multivariate distance matrix regression was used to identify voxels presenting FC patterns that were significantly different between MCI-C and MCI-NC.Results21 MCI-C and 28 MCI-NC were included. The primary analysis with voxel-level threshold at p < 0.001 combined with cluster-level p < 0.05 yielded no significant results. At voxel-level p < 0.01 and the same cluster-level threshold, three significant clusters on the right visual cortex were found. These clusters, however, were not robust to head motion, fMRI protocol and additionally clinical or biological severity.ConclusionsProgression from Alzheimer-related MCI to dementia was not significantly associated with FC in the primary analysis. However, a less stringent threshold yielded FC differences in the occipital lobe in alignment with previous studies but were not robust to methodological and biological covariates between groups. Our findings highlight the need for larger samples and careful control of covariates to identify robust FC alterations related to dementia conversion.

BACKGROUND:Up to half of patients with acute ischemic stroke (AIS) achieving successful reperfusion after endovascular thrombectomy (EVT) remain dependent. Although global atrophy may influence recovery, the prognostic value of regional atrophy patterns and white matter (WM) lesions remains uncertain. METHODS:We retrospectively included consecutive AIS patients achieving successful reperfusion (modified Thrombolysis in Cerebral Infarction (mTICI) 2b-3) after EVT at a comprehensive stroke center (2015-2023). Baseline CT was used to rate medial temporal atrophy (MTA), parietal atrophy (Koedam; antero-posterior index (API)), global cortical atrophy-frontal (GCA-F), and white matter lesions (Fazekas scale). The primary outcome was a composite measure of futile recanalization: functional dependence (modified Rankin Scale (mRS) >2), mRS worsening if premorbid mRS>2, or death. The secondary outcome was the ordinal shift across the mRS distribution. Associations between visual rating scales and outcomes were assessed using multivariable logistic and proportional-odds models, with sex-stratified and interaction analyses. RESULTS:A total of 450 AIS patients (mean age 73.6±14.0 years; 53.8% females; n=406 anterior circulation) were included. In anterior circulation stroke, parietal and frontal atrophy scales were independently associated with poor outcome: Koedam (adjusted OR (aOR) 2.19, 95% CI 1.32 to 3.63), GCA-F (aOR 1.90, 95% CI 1.11 to 3.26), and API-positivity (aOR 2.27, 95% CI 1.36 to 3.81). Periventricular and basal ganglia Fazekas scores were associated with poor outcome in univariate analyses but lost significance after multivariable adjustment. CONCLUSIONS:Parietal and frontal atrophy are independently associated with the risk of futile recanalization after EVT. CT-based regional atrophy ratings could enhance individualized risk stratification and support treatment selection.