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Background: Recent studies highlight distinct patterns of cortical atrophy between amnestic (typical) and non‐amnestic (atypical, with subtypes: behavioural, dysexecutive, logopenic and visuospatial) clinical phenotypes of Alzheimer's disease (AD). The current study aimed to assess regional MRI patterns of cortical atrophy across AD phenotypes, and their association with amyloid‐beta (Aβ), phosphorylated tau (pTau), axonal degeneration (NfL) and microvascular deterioration (COLIV). Method: Postmortem In‐situ 3DT1 3T‐MRI data was collected for 33 AD (17 typical, 16 atypical) and 16 control brain donors. Images were segmented and AAL3 atlas regional volumes were obtained using QyScore®. At subsequent autopsy, eight brain regions were selected, immunostained for Aβ (4G8), pTau (AT8), Neurofilament‐light (NFL), and Collagen‐IV (COLIV), and quantified using qupath. Group comparisons and volume‐pathology associations were analyzed using linear models and partial correlations with covariates age, sex, postmortem delay, and intracranial volume. Results: Compared to controls, AD phenotype groups showed overall lower cortical volume, while only minor volume differences were observed between AD phenotype groups, observed primarily in limbic regions (Fig. 1). Across pathological markers, AD phenotype groups showed consistently higher immunoreactivity than controls, while atypical AD showed consistently higher immunoreactivity than typical AD (Fig. 2). Moreover, different patterns of pathology could be observed between atypical subtypes (e.g. distinctly higher pTau load in the occipital gyrus of the visuospatial subtype). In typical AD, global volume loss was associated with lower Aβ and higher pTau, NFL and COLIV immunoreactivity, while in atypical AD, global volume loss was primarily associated with higher NFL immunoreactivity (Fig. 3a). Regionally, AD phenotype differences in atrophy‐pathology association were most pronounced in the (para) hippocampal regions. This distinction was mainly characterized by negative associations for NFL and COLIV, which was only observed in typical AD (Fig. 3b‐c). Conclusion: Atrophy patterns between AD phenotypes showed only minor differences, potentially attributable to the on average later disease stage of the study cohort. The higher immunoreactivity for pathological markers found in atypical AD might suggest a more severe disease burden. (Para) hippocampal volume decline was associated with axonal and microvascular deterioration in typical AD, but not in the higher pathologically burdened atypical AD group, suggesting a differential susceptibility between AD phenotypes. [ABSTRACT FROM AUTHOR]

Background: Quantitative imaging provides valuable information for early detection, disease progression and treatment response monitoring. Manual segmentation is the current gold‐standard, but is prohibitively labour‐intensive for large scale use, such as in clinical routine, and suffers from individual variability. Thus, there is a substantial unmet need for validation of automatic segmentation techniques that perform as accurately as this labour‐intensive manual gold‐standard. Method: The validation cohort consisted of 50 individuals with multiple diagnoses, wide age range (18‐86 yrs), balanced for sex and acquired on multiple scanners for improved generalizability of quantification results (Table 1). Three expert neuroradiologists each manually segmented the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles on 3DT1 images using itk‐SNAP (http://www.itksnap.org/) software. A consensus expert segmentation was then derived using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm (Wakefield et al., (2004)) for each region and compared with the fully automated segmentations produced using QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device. Performance was investigated using the Dice Similarity Coefficient (DSC) and concordance assessed with plotted linear regression. Result: Mean, standard deviation and confidence intervals of the DSC demonstrated a high degree of agreement between the consensus manual gold‐standard and QyScore®'s automated segmentations (Table 2). Importantly, this agreement remained consistent following stratification by field strength, demonstrating generalizability to most clinical imaging centres. For 1.5T scanners the mean DSC was 0.81, 0.89, 0.79, 0.86, 0.94, 0.93 and 0.91 for the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles respectively. For the 3T scanners, this was equivalent or marginally better at 0.84, 0.89, 0.82, 0.86, 0.94, 0.94, and 0.93 respectively. In addition, strong concordance was demonstrated between the volumes, expressed as a percentage of intracranial volume (%ICV), obtained by the automated medical device QyScore® and the consensus of the manual gold‐standard segmentation (Figure 1: Table 2). Conclusion: QyScore® produces fast reliable automated segmentations with comparable accuracy to expert neuroradiologists. These findings support the implementation of QyScore® in clinical trials and in clinical routine to provide quantitative image analysis in support of diagnosis and monitoring of disease progression and treatment response. [ABSTRACT FROM AUTHOR]

Background: Recent studies highlight distinct patterns of cortical atrophy in amnestic (typical) and non‐amnestic (atypical – behavioural, dysexecutive and visuospatial) clinical phenotypes of Alzheimer's disease (AD). The current study aimed to assess regional phenotypic MRI patterns of cortical atrophy, and their association with Amyloid‐beta (Aβ), phosphorylated Tau (pTau), and axonal degeneration (NfL). Method: Postmortem In‐situ 3DT1 3T‐MRI data were collected for 28 AD (14 typical, 14 atypical) and 10 control brain donors. Images were segmented using QyScore® and regional volumes according to the AAL3 atlas were obtained. At subsequent autopsy, Eight brain regions from the right hemisphere were selected and immunostained for Aβ (4G8), pTau (AT8), and Neurofilament light (NfL), and quantified for area% load. Group comparisons and MRI volume‐pathology associations were analyzed using linear models with covariates age, gender, postmortem delay, and intracranial volume when applicable. Results: When assessing regional cortical volumes, compared to controls, typical AD showed a more parietal‐temporal, while atypical AD showed a more a frontal‐temporal atrophy pattern (fig1). Typical and atypical phenotypes showed different pathological load across all selected regions in both Aβ and NFL (p = 0.032,p = 0.0007, respectively). Subsequent explorative assessment indicates a difference in regional pTau distribution between behavioural, dysexecutive and visuospatial subtypes, specifically in the occipital region (fig2). Addressing MRI‐pathology associations across all selected regions, we observed a positive association between cortical volume and Aβ in typical AD (β = 1.98,p = 0.026), while both pTau and NFL showed a negative association (β = ‐0.26,p = 0.004; β = ‐2.00,p = 0.001, respectively). In atypical AD, only NFL showed a significant negative association (β = ‐2.27,p<0.001) with cortical volume. Regionally (fig3), only NfL associated with MRI volume; with the precuneus in both typical and atypical AD (β = ‐2.81,p = 0.044; β = ‐3.81,p = 0.011, respectively), and with posterior cingulate cortex volume in atypical AD (β = ‐3.24,p = 0.045). Conclusion: This study found varying atrophy patterns in AD phenotypes when compared to controls. Across all selected regions, typical AD showed volume‐pathology associations with Aβ, pTau and axonal damage, while atypical AD only showed associations with axonal damage, indicating a possible difference in volume‐pathology associations between phenotypes. Regionally, cortical volume was predominantly associated with axonal damage, rather than Aβ or pTau protein accumulation, in parietal regions. [ABSTRACT FROM AUTHOR]

Background: Quantitative measures of amyloid‐β (Aβ) pathology using positron emission tomography (PET) imaging are sensitive to identify pathological changes early in Alzheimer's disease (AD). The Centiloid scale aims to standardize these in vivo amyloid quantifications to a 100‐point scale, where an average value of zero signifies high certainty of amyloid negativity and 100 identifies average typical AD Aβ‐pathology load (Klunk et al., 2015). The current study developed and validated a single and fully automated Centiloid quantification pipeline for multiple amyloid PET compounds. Method: QyScore's® fully automated pipeline was validated on 11C‐PiB‐PET and 18F‐PET images from the Centiloid project (https://www.gaain.org/centiloid‐project): 34 young controls [age = 31.5 ± 6.3 years] and 47 AD patients [age = 67.5 ± 10.5 years; CDR = 0.5–1]. 18F tracers included Florbetapir (FBP, N = 46), Forbetaben (FBB, N = 35), Flutemetamol (FTM, N = 74) and NAV4694 (NAV, N = 55). PET/MR image pairs were both co‐registered and normalized in the MNI template space. The fully automated segmentation from QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device, parcellated the masks of the grey matter tissue (target) and of the cerebellum (reference) region (Figure 1). The standardized uptake value ratio (SUVr) was computed as the ratio of the mean signal in both regions. Correlations of 11C‐PiB‐PET and 18F‐PET SUVr values with published SUVr data were computed. Further, correlations between 18F‐PET SUVr and paired 11C‐PiB‐PET SUVr were computed. Correlation coefficients (R2) > 0.7 were required to consider the Centiloid calibration valid. Result: QyScore's® fully automated quantitative pipeline produced SUVr values well within the bounds defined by the Centiloid method (SUVr_AD‐100 = 2.08 +/‐ 0.2; SUVr_YC‐0 = 1.01+/‐ 0.05; R2 = 0.99; slope = 1.00; intercept = ‐0.44). 11C‐PiB SUVr correlation coefficients with published values were above 0.99 (Figure 2). Correlation coefficients of 18F‐PET SUVr with 11C‐PiB‐PET SUVr were respectively 0.91, 0.95, 0.96, 0.99 (Figure 3). Equations for converting to Centiloid were respectively o Centiloid = 177.79 FBP_SUVr ‐ 183.56 o Centiloid = 153.08 FBB_SUVr – 152.93 o Centiloid = 122.39 FTM_SUVr – 120.97 o Centiloid = 90.20 NAV_SUVr – 91.61 Conclusion: We demonstrate the feasibility and reliability of a fully automated amyloid PET pipeline for multiple amyloid‐PET compounds (11C‐PiB and 18F) suitable for implementation in clinical trials. [ABSTRACT FROM AUTHOR]

Background: Quantitative measures of amyloid‐ß (Aß) pathology using positron emission tomography (PET) imaging are sensitive to identify pathological changes early in Alzheimer's disease (AD). The Centiloid scale aims to standardize these in vivo amyloid quantifications to a 100‐point scale, where an average value of zero signifies high certainty of amyloid negativity and 100 identifies average typical AD Aß‐pathology load (Klunk et al., 2015). The current study developed and validated a single and fully automated Centiloid quantification pipeline for multiple amyloid PET compounds. Method: QyScore's® fully automated pipeline was validated on 11C‐PiB‐PET and 18F‐PET images from the Centiloid project (https://www.gaain.org/centiloid‐project): 34 young controls [age = 31.5 ± 6.3 years] and 47 AD patients [age = 67.5 ± 10.5 years; CDR = 0.5–1]. 18F tracers included Florbetapir (FBP, N = 46), Forbetaben (FBB, N = 35), Flutemetamol (FTM, N = 74) and NAV4694 (NAV, N = 55). PET/MR image pairs were both co‐registered and normalized in the MNI template space. The fully automated segmentation from QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device, parcellated the masks of the grey matter tissue (target) and of the cerebellum (reference) region (Figure 1). The standardized uptake value ratio (SUVr) was computed as the ratio of the mean signal in both regions. Correlations of 11C‐PiB‐PET and 18F‐PET SUVr values with published SUVr data were computed. Further, correlations between 18F‐PET SUVr and paired 11C‐PiB‐PET SUVr were computed. Correlation coefficients (R2) > 0.7 were required to consider the Centiloid calibration valid. Result: QyScore's® fully automated quantitative pipeline produced SUVr values well within the bounds defined by the Centiloid method (SUVr_AD‐100 = 2.08 +/‐ 0.2; SUVr_YC‐0 = 1.01+/‐ 0.05; R2 = 0.99; slope = 1.00; intercept = ‐0.44). 11C‐PiB SUVr correlation coefficients with published values were above 0.99 (Figure 2). Correlation coefficients of 18F‐PET SUVr with 11C‐PiB‐PET SUVr were respectively 0.91, 0.95, 0.96, 0.99 (Figure 3). Equations for converting to Centiloid were respectively: • Centiloid = 177.79 FBP_SUVr ‐ 183.56 • Centiloid = 153.08 FBB_SUVr – 152.93 • Centiloid = 122.39 FTM_SUVr – 120.97 • Centiloid = 90.20 NAV_SUVr – 91.61 Conclusion: We demonstrate the feasibility and reliability of a fully automated amyloid PET pipeline for multiple amyloid‐PET compounds (11C‐PiB and 18F) suitable for implementation in clinical trials. [ABSTRACT FROM AUTHOR]

Background: The suboptimal selection of patients is a key challenge for disease‐modifying trials in mild cognitive impairment (MCI) and Alzheimer's disease. Improved selection strategies are urgently needed to better power trials. Recent advances in AI predictive modeling, such as the QyPredict® algorithm, are promising tools to improve the selection of patients likely to clinically progress during the clinical trial timeframe. Method: QyPredict® was applied to 519 MCI patients from ADNI: age: (71.8(±7.1), 303 Male, MMSE range 24‐30, CDR = 0.5, with available amyloid and APOE‐ε4 + status. QyPredict®, a tunable machine learning model, incorporated baseline QyScore® volumetric MRI results, demographic (age and sex) and clinical (MMSE and CDR) inputs. A QyPredict® score (0‐1) was produced for each individual, representing the probability they will demonstrate a modelled outcome (here an increase in CDR‐SOB of 0.5+ over 24 months). A score close to zero indicating very low probability of change. Predictive performance was evaluated using positive predictive value (PPV), sensitivity and specificity. Mean and standard deviation for change in CDR‐SOB over 24 months were calculated and compared using Mann‐Whitney test at increasing QyPredict® cut‐offs (0.1, 0.2, 0.3, 0.4 and 0.5). Actual change versus predicted change in CDR‐SB scores was further investigates for 'Stable' (QyPredict®<0.5) versus 'Decliners' (QyPredict®>0.5) (Figure 1). Sample sizes to detect a 30% treatment effect (reduction in change in CDR‐SOB) were calculated for the full sample and a cohort enriched with only 'Decliners'. Result: For the full sample without QyPredict® enrichment, change in CDR‐SOB at 24 months was 2.3 (±2.11), which significantly increase (p<0.001) from a QyPredict®>0.2, reaching a change of 3.4 (±2.30) points for MCI patients with QyPredict®>0.5. PPV, sensitivity and specificity were: 0.76, 0.70 and 0.75 (the full cohort); 0.83, 0.77 and 0.70 (Amyloid positive patients) and 0.82, 0.75 and 0.74 (APOE‐ε4 + patients). Sample sizes for a putative trial where 304 (full sample) compared with 110 (enriched cohort) per treatment arm. Conclusion: Using baseline QyScore® metrics, basic demographic, and typical clinical data available at screening visits, QyPredict® accurately modelled the likelihood of future clinical decline, resulting in substantially reduced patient cohorts to detect treatment effects when enriched with 'decliners'. [ABSTRACT FROM AUTHOR]

In medical imaging, multiple sclerosis (MS) lesions can lead to confounding effects in automatic morphometric processing tools such as registration, segmentation and cortical extraction, and subsequently alter individual longitudinal measurements. Multiple magnetic resonance imaging (MRI) inpainting techniques have been proposed to decrease the impact of MS lesions in medical image processing, however, most of these methods make the assumption that lesions only affect white matter. Here, we propose a method to fill lesion regions using the patch-based non-local mean (NLM) strategy. The method consists of a hierarchical concentric filling strategy after identification of the lesion region. The lesion is filled iteratively, based on the surrounding tissue intensity, using an onion peel strategy. This concentric technique presents the advantage of preserving the local information and therefore the continuity of the anatomy and does not require identification of any a priori normal brain tissues. The method is first evaluated on 20 healthy subjects with simulated artificial MS lesions where we assessed our technique by measuring the peak signal-to-noise ratio (PSNR) of the images with inpainted lesion and the original healthy images. Second, in order to assess the impact of lesion filling on longitudinal image analyses, we performed a power analysis with sample size estimation to evaluate brain atrophy and ventricular growth in patients with MS. The method was compared to two different publicly available methods (FSL lesion fill and Lesion LEAP) and a more classic method, which fills the region with intensities similar to that of the surrounding healthy white matter tissue or mask the lesions. The proposed method was shown to exceed the other methods in reproducing the fidelity of healthy subject images where the lesions were inpainted. The method also improved the power to detect brain atrophy or ventricular growth by decreasing the sample size by 25% in the presence of MS lesions. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases such as Alzheimer's disease present subtle anatomical brain changes before the appearance of clinical symptoms. Manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each time-point is analyzed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scanners and other physiological effects may also introduce variability in the measurement. We propose to use 4D non-linear registration with spatio-temporal regularization to correct for potential longitudinal inconsistencies in the context of structure segmentation. The major contribution of this article is the use of individual template creation spatio-temporal regularization of the deformation fields for each subject. We validate our method with different sets of real MRI data, compare it to available longitudinal methods such as FreeSurfer, SPM12, QUARC, TBM, and KNBSI, and demonstrate that spatially local temporal regularization yields more consistent rates of change of global structures resulting in better statistical power to detect significant changes over time and between populations. [ABSTRACT FROM AUTHOR]

Although neuroscientific research has revealed experience-dependent brain changes across the life span in sensory, motor, and cognitive domains, plasticity relating to social capacities remains largely unknown. To investigate whether the targeted mental training of different cognitive and social skills can induce specific changes in brain morphology, we collected longitudinal magnetic resonance imaging (MRI) data throughout a 9-month mental training intervention from a large sample of adults between 20 and 55 years of age. By means of various daily mental exercises and weekly instructed group sessions, training protocols specifically addressed three functional domains: (i) mindfulness-based attention and interoception, (ii) socio-affective skills (compassion, dealing with difficult emotions, and prosocial motivation), and (iii) socio-cognitive skills (cognitive perspective-taking on self and others and metacognition). MRI-based cortical thickness analyses, contrasting the different training modules against each other, indicated spatially diverging changes in cortical morphology. Training of present-moment focused attention mostly led to increases in cortical thickness in prefrontal regions, socio-affective training induced plasticity in frontoinsular regions, and socio-cognitive training included change in inferior frontal and lateral temporal cortices. Module-specific structural brain changes correlated with training-induced behavioral improvements in the same individuals in domain-specific measures of attention, compassion, and cognitive perspective-taking, respectively, and overlapped with task-relevant functional networks. Our longitudinal findings indicate structural plasticity in well-known socio-affective and socio-cognitive brain networks in healthy adults based on targeted short daily mental practices. These findings could promote the development of evidence-based mental training interventions in clinical, educational, and corporate settings aimed at cultivating social intelligence, prosocial motivation, and cooperation. [ABSTRACT FROM AUTHOR]

Background: There are no approved oral disease-modifying treatments for Alzheimer's disease (AD)., Objectives: The objective of this study was to assess efficacy and safety of blarcamesine (ANAVEX®2-73), an orally available small-molecule activator of the sigma-1 receptor (SIGMAR1) in early AD through restoration of cellular homeostasis including autophagy enhancement., Design: ANAVEX2-73-AD-004 was a randomized, double-blind, placebo-controlled, 48-week Phase IIb/III trial., Setting: Multicenter - 52 medical research centers/hospitals in 5 countries., Intervention: 508 participants with early AD (Stage 3) were randomized to receive either blarcamesine (n = 338) in medium dose group 30 mg or in high dose group 50 mg or placebo (n = 170) oral capsules once daily for 48 weeks. Participants in these groups were offered to enroll into the open-label-extension study ATTENTION-AD, which completed June 2024, ClinicalTrials.gov Identifier NCT04314934., Measurements: The co-primary cognitive and functional outcomes were assessed as change in ADAS-Cog13 and ADCS-ADL from baseline to 48 weeks. The outcomes include the secondary outcome CDR-SB and biomarkers from the A/T/N spectrum, plasma Aβ42/40-ratio and global brain volume changes measured by MRI. All clinical endpoints were analyzed using mixed model for repeated measures (MMRM), plasma biomarker measurements were analyzed by Welch's t-test, and volumetric MRI scans were analyzed by general linear model., Results: Among 462 randomized participants in the intent-to-treat population (mean age, 73.7 years; 225 [48.7%] women), 338 (73.2%) completed the trial. The co-primary outcome was met under the multiplicity control rule, since the differences in the least-squares mean (LSM) change from baseline to 48 weeks between the prespecified blarcamesine and placebo groups for ADAS-Cog13 was significant at a level of P < 0.025 and for CDR-SB was significant at a level of P < 0.025, while ADCS-ADL did not reach significance at Week 48 (ADAS-Cog13 difference of -2.027 [95% CI -3.522 to -0.533]; P = 0.008; CDR-SB difference of -0.483 [95% CI -0.853 to -0.114]; P = 0.010; ADCS-ADL difference of 0.775 [95%CI -0.874 to 2.423]; P = 0.357). Plasma Aβ42/40-ratio increased significantly with blarcamesine group vs. placebo, (P = 0.048) and whole brain volume loss was significantly decreased (P = 0.002). Participants in the full safety population with ≥1 serious treatment-emergent adverse events (TEAEs) occurred in 56 participants (16.7%) in the blarcamesine and 17 (10.1%) in the placebo group. Common TEAEs included dizziness, which was transient and mostly mild to moderate in severity. One death in the blarcamesine group and 1 in the placebo group were both not considered treatment related., Conclusions: Blarcamesine, demonstrating a safety profile with no associated neuroimaging adverse events, significantly slowed clinical progression by 36.3% at 48 weeks with blarcamesine group as well as the individual 30 mg (by 34.6%) and 50 mg (by 38.5%) blarcamesine groups vs. placebo on the prespecified primary cognitive endpoint ADAS-Cog13. The prespecified secondary endpoint CDR-SB, which is used as the sole primary endpoint in recent successful AD drug submissions, is significantly improved at Week 48 with blarcamesine relative to placebo. The findings are supported by biomarkers from the A/T/N spectrum, including plasma Aβ42/40-ratio and reduction of whole brain atrophy. Additionally, the prespecified SIGMAR1 gene variant subgroup analysis confirmed beneficial clinical effect of blarcamesine group through upstream SIGMAR1 activation - subjects with the common SIGMAR1 wild-type gene (excluding carriers of the mutated SIGMAR1 rs1800866 variant) experienced an even greater significant clinical benefit with slowed clinical progression by 49.8% at 48 weeks on the prespecified primary cognitive endpoint ADAS-Cog13. Oral once daily blarcamesine could represent a novel treatment in early AD and be complementary or alternative to anti-beta amyloid drugs., Competing Interests: Declaration of competing interest DISCLOSURES: Dr. Sabbagh discloses ownership interest (stock or stock options) in NeuroTau, Inc., uMETHOD, Athira Pharma, Inc., and CervoMed and Lighthouse Pharmaceuticals; consulting for Alzheon, Inc, Genentech (Roche Group), Prothena, Novo Nordisk, Anavex Life Sciences, T3D Therapeutics, Inc., Eisai Co., Ltd., Eli Lilly and Co., and KeifeRx. Dr. Macfarlane has received paid honoraria from the following pharmaceutical companies for various speaking engagements and advisory board services: Eisai, Eli Lilly, Janssen-Cilag, Lundbeck, Novo Nordisk. Dr. Macfarlane is contracted by Anavex Life Sciences to provide medical monitoring services for Anavex's Rett syndrome studies. Dr. Grimmer received consulting fees from AbbVie, Alector, Anavex Life Sciences, Biogen, Cogthera, Eli Lilly, Functional Neuromodulation, Grifols, Iqvia, Janssen, Noselab, Novo Nordisk, NuiCare, Orphanzyme, Roche Diagnostics, Roche Pharma, UCB, and Vivoryon; lecture fees from Biogen, Eisai, Grifols, Medical Tribune, Novo Nordisk, Roche Pharma, Schwabe, and Synlab; and has received grants to his institution from Biogen, Eisai, and Roche Diagnostics. Dr. O'Brien's institution has received consultancy and/research funding from Anavex Life Sciences, Eisai, UCB Pharma, ES Therapeutics, Kinoxis Pharmaceuticals, Supernus, Autobahn, Shanghai Zhimeng, Epidarex, and government grant funding from NHMRC (APP1176426), MRFF, DoD and NINDS. Dr. Woodward has received honoraria for speaking and expert advice from Actinogen, Biogen, Roche, MSD/Merck, Glaxo Smith Kline, Cognition Therapies, Eisai, Novo Nordisk and Pfizer. He was previously paid for his role as Chief National Investigator for Anavex Life Sciences. He owns no shares and has no direct employment with any pharmaceutical company or Biotech. Dr. Tartaglia is SAB member of Brain Injury Canada, PSP Awareness, and Women's Brain Project. Advisory to Eisai, Eli Lilly and QurAlis and received Grant funding from NIH, Weston Brain Institute, Tanenbaum Institute for Science in Sport and participated in clinical trials: Biogen, Novo Nordisk, Janssen, Roche, Anavex Life Sciences, Passage Bio, Green Valley. Dr. Frank received paid honoraria from the following pharmaceutical companies for advisory board services: Eisai, Eli Lilly, Roche Pharma, Novo Nordisk. Dr. Lai has received a paid honorarium for speaking engagements with INmune Bio. Dr. Lewis is supported by a National Health and Medical Research Council Leadership Fellowship (1195830) and has received research funding from The Michael J. Fox Foundation and the Australian Research Council, as well as consulting for Pharmaxis Ltd. Dr. Kurrle has received honoraria for educational activities from Roche Diagnostics and Novartis. Dr. Cohen discloses consulting work (no personal fees received) for: Alnylam, Biogen, Biohaven, Cassava, Cogstate, Cognivue, Eisai, Eli Lilly, INmune Bio, Novo Nordisk, ProMIS Neuroscience, Roche, RetiSpec, SciNeuro; and research grants (paid to institution only) from: AbbVie, AgeneBio, Alector, Alnylam, Alzheon, Anavex Life Sciences, Biogen, Cassava, Eisai, Eli Lilly, Janssen, Novo Nordisk, Roche, RetiSpec, UCB Biopharma. Dr. Grunfeld has received paid honoraria from the Janssen-Cilag for advisory board services. Dr. Morris has no financial conflicts of interest to declare. Dr. Connell does not have any professional conflicts of interest. Dr. Thompson does not have any conflicts of interests to declare. Dr. Tacik does not have any conflicts of interests to declare. Dr. Perry has received paid honoraria from the following pharmaceutical companies for various speaking engagements and advisory board services: Eisai, Eli Lilly, MSDF, Biogen, and Roche. Dr. Sharif does not have any conflicts of interest to disclose. Dr. Kalafatis does not have any conflicts of interests to declare. Dr. Munisamy does not have any conflicts of interests to declare. Dr. Pearson has received paid honoraria for speaking and advice from Biogen, Eli Lilly and Boehringer-Ingelheim. Dr. Sturm does not have any conflicts of interests to declare. Dr. Oschmann received research support as well as speaking fees and travel fees from Alexion, Bayer Health Care, Biogen, Janssen, Merck Serono, Novartis, Pfizer, Roche, Sanofi Genzyme, TEVA. Dr. Hsiung discloses that he has received grants or contracts from CIHR, NIA/NIH and has participated in expert advisory committee supported by Biogen, Roche, and NovoNordisk. Dr. Hsiung is the current president of C5R (Consortium of Canadian Centres for Clinical Cognitive Research). Dr. Lynch does not have any conflicts of interests to declare. Dr. Brew does not have any conflicts of interests to declare. Dr. Tucker is employed by Anavex Life Sciences as an independent consultant to provide medical monitoring services for the Alzheimer's disease program. Dr. Ingram discloses no financial ownership interest in any pharmaceutical company but has been paid honoraria by Eisai, Merck, Biogen, Roche, Janssen, Eli Lilly to participate in health care planning and messaging regarding their products’ impact on dementia. Anavex research responsibilities were contractually held by Kawartha Centre ∼ Redefining Healthy Aging, previously owned by Dr. Ingram. This company has changed ownership as of January 5, 2023. Dr. Pasternak has received grant support to his institution and hold shares in Zywie Bio LLC. He has received speakers fees from Eli Lilly. Dr. MacSweeney does not have any conflicts of interests to declare. Dr. Short has received paid honoraria from Roche and Eisai for Advisory Board services and speaking engagements. Dr. Bhatt does not have any conflicts of interests to declare. Dr. Drysdale discloses that he has been paid for conducted research by the following companies, Eli Lilly, Cassava Sciences, Roche, Anavex Life Sciences, Lundbeck and Biogen. Dr. Mannering does not have any conflicts of interests to declare. Dr. Henri-Bhargava has received paid honoraria for Advisory boards / speaking engagements for Roche, Lilly, Eisai, Boehringer Ingelheim; Clinical trial payments from: Lilly, Roche, Boheringer Ingelhiem, Anavex Life Sciences, Cerevel, Green Valley Shanghai, Intelgenx; Grants from Canadian Consortium on Neurodegeneration in Aging, Centre for Aging and Brain Health Innovation, Manning Cognitive Health Initiative. Dr. Froelich has received honoraria for consulting or presentations from Biogen, BioVie, Eisai, Grifols, Janssen Cilag, Neurimmune, Noselab, NovoNordisk, Roche, TauRX, Schwabe; Honoraria for Clinical study committees from Avanir/Otsuka, PharmatrophiX, Charité Berlin, Neuroscios, Vivoryon; Clinical trials (honoraria to his institution) from Axon Neuroscience, Anavex Life Sciences, Alector, Boehringer Ingelheim, Eisai, Hummingbird, NovoNordisk, Noselab. Dr. Chertkow has been supported by a Foundation Grant from the CIHR (Canadian Institutes for Health Research), along with funding from the National Institute of Health (US), the Weston Foundation and the Baycrest Health Sciences Foundation. He has participated as a site PI in pharmaceutical trial activities sponsored by Hoffmann-La Roche, TauRx, Lilly, Anavex Life Sciences, Alector, Biogen, Esai, and Immunocal (site investigator for trials). He has participated as an unpaid advisor in 2020 for establishment of an international database by Biogen. He has participated in advisory boards for Esai and Lilly Co., with honoraria going to the Rotman Research Institute. He is Scientific Director for the CCNA, which receives partner support from partners including Pfizer, Lilly, Sanofi. Dr. Mander does not have any conflicts of interests to declare. Dr. Wiltfang does not have any conflicts of interests to declare. Dr. Prins performed consultancy work for Aribio, Amylyx, Eli-Lilly and Janssen and received a speaker fee from Biogen. He is co‐PI of of a current trial with Fuji Film Toyama Chemical. He is CEO and co‐owner of Brain Research Center, the Netherlands. Dr. Peters received consulting or lecture fees from Biogen, Eisai, Eli Lilly, Grifols, Medical Tribune, Noselab, Novo Nordisk, Prinnovation, Priavoid, Roche Diagnostics and Roche Pharma; and has received grants to his institution from Biogen, Eisai, Eli Lilly, Noslab, Predemtec, Roche Pharma, Roche Diagnostics and Vivoryon. Dr. Smith has received personal consulting fees from Alnylam Pharmaceuticals and Eli Lilly. Dr. Dautzenberg has participated as PI in pharmaceutical trials activities sponsored by TauRx, Lilly, Anavex Life Sciences, Alector, Biogen Boehringer Ingelheim, Eisai, NovoNordisk, Green Valley Shanghai, Roche and received a speaker fee from NovoNordisk as National PI. Dr. Evans does not have any conflicts of interests to declare. Dr. Villa does not have any conflicts of interests to declare. Dr. Gordon does not have any conflicts of interests to declare. Dr. Jubault does not have any conflicts of interests to declare. Dr. Guizard does not have any conflicts of interests to declare. Dr. Kaufmann discloses being an employee of and ownership interest (stock or stock options) in Anavex Life Sciences. Dr. Kun Jin discloses being an employee of and ownership interest (stock or stock options) in Anavex Life Sciences. Dr. Chezem discloses being an employee of and ownership interest (stock or stock options) in Anavex Life Sciences. Dr. Missling discloses being an employee of and ownership interest (stock or stock options) in Anavex Life Sciences. Dr. Babajide does not have any conflicts of interest to declare. Dr. Brodtmann has received paid honoraria from the following pharmaceutical companies for advisory board services: Biogen, Roche and Eisai. Dr. Asher does not have any conflicts of interests to declare., (Copyright © 2024 Anavex Life Sciences Corp. Published by Elsevier Masson SAS.. All rights reserved.)

We have shown that cerebellar injury in the premature infant is followed by significant growth impairment of the contralateral cerebral hemisphere evident as early as term adjusted age. In this study, we hypothesize that this remote growth restriction is region specific in the cerebrum. In a prospectively enrolled cohort of 38 expreterm infants with isolated cerebellar injury by neonatal MRI, we performed follow-up volumetric MRI studies at a mean postnatal age of 35.5 +/- 13.8 mo. We measured volumes of cortical and subcortical gray matter, and cerebral white matter within eight parcellated regions for each cerebral hemisphere. Unilateral cerebellar injury (n = 24) was associated with significantly smaller volumes of cortical gray and cerebral white matter in the following regions of the contralateral (versus ipsilateral) cerebral hemisphere: dorsolateral prefrontal, premotor (PM), sensorimotor, and midtemporal regions (p < 0.001 for all except midtemporal cortical gray, p = 0.01), as well as subcortical gray matter in the PM region (p < 0.001). Conversely, in cases of bilateral cerebellar injury (n = 14), there was no significant interhemispheric difference in tissue volumes for any of the cerebral regions studied. These findings suggest that regional cerebral growth impairment results from interruption of cerebellocerebral connectivity and loss of neuronal activation critical for development.

Background: Adverse neurodevelopmental outcome is an important source of morbidity in children with congenital heart disease (CHD). A significant proportion of newborns with complex CHD have abnormalities of brain size, structure, or function, which suggests that antenatal factors may contribute to childhood neurodevelopmental morbidity., Methods and Results: Brain volume and metabolism were compared prospectively between 55 fetuses with CHD and 50 normal fetuses with the use of 3-dimensinal volumetric magnetic resonance imaging and proton magnetic resonance spectroscopy. Fetal intracranial cavity volume, cerebrospinal fluid volume, and total brain volume were measured by manual segmentation. Proton magnetic resonance spectroscopy was used to measure the cerebral N-acetyl aspartate: choline ratio (NAA:choline) and identify cerebral lactate. Complete fetal echocardiograms were performed. Gestational age at magnetic resonance imaging ranged from 25 1/7 to 37 1/7 weeks (median, 30 weeks). During the third trimester, there were progressive and significant declines in gestational age-adjusted total brain volume and intracranial cavity volume in CHD fetuses relative to controls. NAA:choline increased progressively over the third trimester in normal fetuses, but the rate of rise was significantly slower (P<0.001) in CHD fetuses. On multivariable analysis adjusted for gestational age and weight percentile, cardiac diagnosis and percentage of combined ventricular output through the aortic valve were independently associated with total brain volume. Independent predictors of lower NAA:choline included diagnosis, absence of antegrade aortic arch flow, and evidence of cerebral lactate (P<0.001)., Conclusions: Third-trimester fetuses with some forms of CHD have smaller gestational age- and weight-adjusted total brain volumes than normal fetuses and evidence of impaired neuroaxonal development and metabolism. Hemodynamic factors may play an important role in this abnormal development.