Oligodendrocyte dysfunction in human age-related cognitive decline.

Oligodendrocyte dysfunction in human age-related cognitive decline

Cognitive decline in aging impacts quality of life, for which therapeutic interventions are lacking. Why some individuals experience more severe cognitive decline with aging is unclear. Here we uncover a role for the brain’s myelin-forming cells—oligodendrocytes—in explaining this variance. By investigating neuropathological and transcriptomic changes in human white matter associated with individual rates of cognitive decline in aging, we find that worse cognitive trajectories unexpectedly associate with smaller myelinated axon size, thicker myelin and more oligodendrocytes with downregulation of NRF2. Aged oligodendrocyte-specific NRF2-knockout mice showed attenuated cognitive improvement over time and mirrored the white matter pathology of human cognitive decline in aging. These findings place the oligodendrocyte as a contributor to cognitive decline and highlight the NRF2 pathway as a therapeutic target to preserve cognition in human aging.

This study identifies oligodendrocytes as active contributors to cognitive decline in aging, by leveraging a unique human brain bank with more than 70 years of associated cognitive data combined with experimental modeling of oligodendrocyte dysfunction.

Main

Cognitive decline, defined by impaired memory, reasoning and processing speed over time, is a major socioeconomic burden, rising in prevalence due to the aging population. The underlying mechanisms driving cognitive decline in aging remain unclear. Learning in humans in youth and adulthood is associated with increased central nervous system (CNS) myelin1, the insulating membrane produced by oligodendrocytes that surrounds neuronal axons to facilitate rapid electrical impulse conduction and provide metabolic support. Furthermore, it is now well established that, in mice, learning requires an increase in oligodendrocytes through de novo generation2–6. Cognitive impairment in aged mice and non-human primates is associated with reduced oligodendrocytes and myelin6,7; however, the white matter pathologies driving human cognitive decline remain unclear. Indeed, although magnetic resonance imaging studies associate white matter abnormalities with cognitive decline in aged humans8–12, the underpinning oligodendrocyte and myelin changes are unknown. In the present study, we identified the myelin and oligodendrocyte pathology specific to human age-related cognitive decline and unexpectedly discovered that an increase in dysfunctional oligodendrocytes has a functional contribution.

Results

Severity of cognitive decline in aging humans is associated with a shift in myelinated axon profiles and thicker myelin

We first aimed to investigate white matter health in association with cognitive decline in aging. We examined samples from the Lothian Birth Cohort 1936 (LBC1936), the longest-running study of human cognition. Individuals from this cohort were born in 1936 and living in the Lothian regions of Scotland at recruitment, tested for IQ at age 11, then followed up at age 70 and every 3 years thereafter13; this allowed us to associate tissue pathology with individual lifelong trajectories of cognitive decline (Fig. 1a). To comprehensively assess cognitive decline with aging, cognition from 70 years to 82 years was measured with a series of standardized tests across visuospatial, processing speed and memory domains (Weschler Adult Intelligence Scale III/Memory Scale III) (Extended Data Fig. 1a). Cognitive level and decline were modeled using latent growth curve modeling (factor of curves) on cognitive data14–16. Individual rates of cognitive decline were defined as the slope change over time (annual change in cognitive level) for all individuals who had follow-up testing beyond age 70 (n = 866/1,017). Almost all individuals with follow-up testing beyond age 70 exhibited cognitive decline (n = 865/866). The average rate of cognitive decline from age 70 was −0.26; individuals with steeper than average rates of change were classed as ‘severe’, whereas those with less negative than average rates were classed as ‘mild’ (Fig. 1b). Whereas age 11 IQ was predictive of age 70 IQ (Fig. 1c), cognitive decline trajectories beyond age 70 did not relate to age 11 IQ (Fig. 1d), as previously shown15,16.

We investigated the corpus callosum given that it is a large white matter region involved in cognitive processing17–19 and is vulnerable to age-related pathology20,21 (cohort characteristics in Supplementary Table 1). To assess gross changes in myelin, we used spectral confocal reflectance (SCoRe) microscopy to specifically detect compacted myelin22, which showed a significant decrease in individuals with severe cognitive decline (Fig. 1e–g). To assess myelin integrity and myelinated axonal health on an ultra-structural level, corpus callosum samples from a subset of LBC1936 individuals were processed for transmission electron microscopy (Fig. 1h). No differences were observed in the density or percentage of myelinated axons (Fig. 1i,j). However, individuals with more severe cognitive decline had a shift in myelinated axon profiles toward smaller axon diameters, as indicated by probability plots of the frequency distribution and average myelinated axon size (Fig. 1k−m). Myelin thickness was surprisingly increased with worse cognitive decline, measured by a reduced g ratio (ratio of axon diameter: axon + myelin diameter) but only for large-diameter axons (>1 µm) (Fig. 1n,o). Consistent with decreased SCoRe signal, cases with severe cognitive decline had increased myelin decompactions in large-diameter axons (Extended Data Fig. 1c,d); however, these did not artificially impact myelin thickness measurements as there was no significant relationship to g ratio (R = −0.36, P = 0.22).

We also noted several instances where myelinated profiles were present but had no apparent axon (labeled ‘empty’; Fig. 1p), suggestive of the early stages of Wallerian degeneration23, which increased in frequency with worse cognitive decline (Fig. 1q,r). The degenerating and smaller axon profiles alongside thicker myelin could be indicative of a primary axonopathy, such as axon shrinkage. However, we did not observe any myelinated axons with established features known to precede axonopathy, such as axonal spheroids or condensed (dark) axoplasm24,25. In addition, average mitochondrial size, known to increase with axonal stress especially in large-diameter myelinated axons26,27, was not correlated with severity of cognitive decline yet was increased generally with aging, as expected28 (Fig. 1s,t). Furthermore, no difference was observed in the percentage of area positive for the axon pathology marker SMI-32 (ref. 29) between cases with mild and severe cognitive decline (Fig. 1u,v).

We acknowledge that many clinical parameters and technical artifacts from tissue processing are often confounding factors in human tissue analyses. Nonetheless, neither axon diameter, myelin thickness nor the density of degenerating myelinated axon profiles was associated with age at death, sex, age 11 IQ, technical variables (such as postmortem interval, brain weight, brain pH) or neuropathological indices of small vessel disease (Extended Data Fig. 1b). Moreover, we did not see a significant relationship between myelinated axon changes and absolute cognition (either at age 70 or extrapolated at death; Extended Data Fig. 1b). Therefore, it is worsened cognitive decline over time, rather than absolute cognition, that is associated with myelinated axon pathology. Interestingly, there was a significant negative association between Braak stage and axon diameter (Extended Data Fig. 1b), suggesting that smaller axons may be a shared pathological feature between age-associated cognitive decline and Alzheimer’s disease pathology. Together, these findings uncover a selective vulnerability of large-diameter myelinated axons to pathology in human aging, in association with worse cognitive decline.

Oligodendrocytes deficient in NRF2 are increased in severe cognitive decline in humans

To elucidate potential mechanisms underlying the myelin and axonal pathology associated with human cognitive decline, we optimized a single-nucleus RNA sequencing (snRNA-seq) protocol previously applied to adult and aged human white matter30,31 to enhance yield and nuclei quality while preserving relative abundance of cell types and clusters; fluorescence-activated nuclei sorting (FANS) improved yield tenfold without compromising nuclei quality or cell type proportions (Extended Data Fig. 2a−f). Implementing this pipeline on fresh-frozen corpus callosum samples from 14 individuals (Supplementary Table 1), we retained 45,437 high-quality nuclei after quality control (97% of all nuclei), with an average of 2,740 genes per nucleus and 7,706 unique molecular identifiers (UMIs) per nucleus (Extended Data Fig. 2g−i). After unbiased clustering, major cell types were identified through expression of canonical markers for oligodendrocytes (PLP1, BCAS1, ENPP6), immune cells (including microglia, macrophages and infiltrating cells; PTPRC, CSF3R, DOCK8), astrocytes (GFAP, ALDH1A1, AQP1), oligodendrocyte precursor cells (PDGFRA, MMP16, CSPG4), neurons (SNAP25, GRIN1, CHRM2) and endothelial/vascular cells (CLDN5, MYO1B, SLC38A11) (Fig. 2a,b). Mapping by cognitive status showed no emergence of novel clusters with cognitive decline (Extended Data Fig. 2j).

Unexpectedly, we found that oligodendrocyte abundance was increased with more severe cognitive decline (Fig. 2c and Extended Data Fig. 2k). Although snRNA-seq of white matter is biased toward capturing oligodendrocytes, no other cell types showed associations of number or proportion of total cells with cognitive decline severity (Fig. 2c and Extended Data Fig. 2k) and were captured at frequencies expected from previous studies of human white matter31,32. Given that cell abundance from sequencing alone cannot reliably indicate altered numbers, immunofluorescence staining for Olig2 was performed, which validated an increased oligodendrocyte density with more severe cognitive decline (Fig. 2d,e). As shifts in oligodendrocyte heterogeneity have previously been associated with neurodegenerative disease31,33–36 and myelin thickening37, we next subclustered oligodendrocytes into seven subpopulations with distinct transcriptional signatures (Fig. 2f and Extended Data Fig. 3a). To understand how oligodendrocyte subclusters were altered with cognitive decline, pseudobulking and differential gene expression analyses were performed using edgeR and limma packages; genetic variance associated with technical factors (library size) and/or biological factors already known to influence cognition (age, sex, IQ at age 70 and histopathological evidence of Alzheimer’s disease or small vessel disease) were regressed out as covariates. Subclusters Oligo4 and Oligo5 showed the highest number of differentially expressed genes (DEGs) between mild and severe cognitive decline (167 and 113 genes, respectively; Supplementary Data 1), whereas all other oligodendrocyte subclusters showed either zero (Oligo1, Oligo2, Oligo3, Oligo6) or two (Oligo0) DEGs. With more severe cognitive decline, Oligo4 upregulated genes implicated in neurodevelopmental disorders with cognitive impairment (RFX3-AS1) and expressed by oligodendrocytes in the context of demyelination (NRXN3) as well as those involved in responses to DNA damage (MND1) and oxidative stress (OXR1) (Fig. 2g). In Oligo5, genes increased with more severe cognitive decline included those related to mitochondrial function (MT-CO1) and oligodendrocyte differentiation (OPALIN), whereas downregulated genes related to lipid production (CERS6), heat shock responses (HSPA4L, HSPE1) and autophagy (SQSTM1) (Fig. 2h). Moreover, in comparison to other oligodendrocyte subclusters, Oligo4 and Oligo5 were enriched for genes previously detected in oligodendrocytes in Alzheimer’s disease, Parkinson’s disease or multiple sclerosis, including LINC01099, FRY, KCNIP4 and LAMA2 (refs. 33,38–40) (Fig. 2i−l). Of note, subclustering and differential gene expression analysis revealed MT-CO3 to be the only DEG enriched in a non-oligodendroglial cluster with cognitive decline, in an immune subcluster (Extended Data Fig. 3b−g). Ingenuity Pathway Analysis (IPA) of DEGs altered with cognitive decline in Oligo4 and Oligo5 was then performed to identify upstream regulators. Of all the upstream pathway regulators identified as significant by IPA (|Z| > 2), the only one shared across both Oligo4 and Oligo5 was NRF2 (Z = 2.217 and Z = 2.183 for Oligo4 and Oligo5, respectively), which was predicted to be downregulated with more severe cognitive decline in both subpopulations (Supplementary Data 1). This was of interest as global knockout of the gene encoding NRF2 (Nfe2l2) worsens cognitive impairment in mouse models of aging41–43, Alzheimer’s disease44–47, vascular dementia48 and infection49, yet the contributing cell types are unclear. We next assessed NRF2 expression by oligodendrocytes via immunostaining, which validated that the proportion of Olig2+ cells expressing NRF2 protein was decreased with severe cognitive decline (Fig. 2m,n).

We found no statistical relationship between oligodendrocyte densities nor NRF2+ oligodendrocyte proportions and postmortem interval, age, sex, age 11 IQ, cognition at age 70, extrapolated cognition at death or neuropathological indices of Alzheimer’s disease or small vessel disease (Fig. 2o). However, there was a significant correlation between oligodendrocyte densities and the proportion of NRF2+ oligodendrocytes (Fig. 2o). To assess whether these responses are functionally linked, we asked whether oligodendrocyte accumulation alone would be sufficient to induce downregulation of NRF2. We data-mined a transcriptional dataset in a mouse model where oligodendrocytes accumulate in excess (Olig2-Cre;Tfebfl/fl) consequent to blocked apoptosis50. Nfe2l2 was found to be significantly downregulated in Olig2-Cre;Tfebfl/fl oligodendroglia versus floxed control mice (P = 2.6 × 10−6). These data indicate that accumulation of oligodendrocytes is sufficient to downregulate NRF2 gene expression (Fig. 2p). Altogether, these results indicate that aged humans with more severe cognitive decline have an enrichment of oligodendrocytes with reduced NRF2 expression.

Reduced NRF2 expression in aged mouse oligodendrocytes attenuates cognitive improvement over time and recapitulates neuropathology associated with human cognitive decline

To elucidate the functional consequences of NRF2 downregulation in oligodendrocytes on myelin and axonal health in aging, we generated an oligodendrocyte-specific inducible conditional knockout of NRF2 (Plp-CreERT;Nfe2l2fl/fl) (Fig. 3a). Recombination was induced at 6 months of age, by which age the majority of oligodendrogenesis has occurred51,52, and was confirmed by RNAscope in 12−15-month-old aged animals (Fig. 3b,c). Behavioral tests were performed at 12 months of age, when wild-type mice begin to show impairment in learning and memory6.

First, we performed the open field test to screen for any major motor or anxiety effects that may influence cognitive test readouts. Female conditional knockout mice showed confounding altered anxiety levels (Extended Data Fig. 4a,b) and were, thus, excluded from cognitive assay analysis. Next, the Morris water maze (MWM) was used to assess cognitive performance and learning over time, facilitating comparison to the longitudinal cognitive testing in the human cohort. Notably, the MWM has previously been shown to be sensitive to age-related cognitive impairment concomitant with alterations in corpus callosum myelin5,6. Mice underwent an acclimatization period of eight training trials per day for 3 days (four trial patterns, repeated twice), where they swam to a visible platform that changed quadrants between trials (Fig. 3d and Extended Data Fig. 4c,d). To then test development of spatial learning and memory, the mice underwent a testing phase of six trials per day for 4 days (three trial patterns, repeated twice), where visual cues enabled localization of the hidden platform in a fixed location (Fig. 3d). Over the course of the testing phase, all mice showed an increase in the percentage of time spent in the target quadrant containing the platform, indicating spatial learning and memory encoding (Fig. 3e). Although conditional knockout animals initially spent more time in the target quadrant in the first 2 days of the testing phase relative to controls, they spent relatively less time in the target quadrant by days 3 and 4 (Fig. 3e). Accordingly, when percentage time spent in the target quadrant was plotted as a fold change relative to testing day 1, controls showed an average improvement of 57.75%, whereas conditional knockouts improved by an average of only 27.82% (Fig. 3f). Conditional knockouts surprisingly had a shorter latency to reach the target quadrant versus controls over all test days (Extended Data Fig. 4e). As this did not reflect faster swim speed in the testing phase (Extended Data Fig. 4f), we next scored search strategy using an established rubric53, where more direct pathways to the platform are associated with higher scores (Extended Data Fig. 4g). Although conditional knockouts showed a more direct spatial strategy from day 1 (Extended Data Fig. 4h), they did not improve their spatial strategy over time as much as the controls (Extended Data Fig. 4i); notably, this did not represent reaching a ceiling level of improvement, as both conditional knockout and control animals only ever reached the maximum score of 6 in approximsately 10% of test trials (Extended Data Fig. 4g).

Although conditional knockouts showed attenuated cognitive improvement over time, we noted a large degree of individual variation in performance across both genotypes. To correlate individual cognitive performance with white matter histology as was done in humans, we binned animals based on whether performance over time was better or poorer than average (Fig. 3g). No significant difference was observed in oligodendrocyte densities between better versus poorer performers at 12 months (Fig. 3h). However, when tissue was assessed by electron microscopy, there were clear differences in ultra-structural features of myelinated axons between better and poorer performers (Fig. 3i). Both axon diameter and g ratio were significantly reduced in poorer performers (Fig. 3j,k), indicating that smaller axons and thicker myelin are correlates of worse cognitive performance in aging mice. In late aging (15−18 months), oligodendrocyte density and myelin compaction were unchanged (Extended Data Fig. 4j−n); however, myelinated axon diameter and g ratio were significantly reduced in conditional knockouts versus controls (Fig. 3l−o). This suggests that loss of NRF2 in oligodendrocytes exacerbates the neuropathological features associated with age-related cognitive decline. These findings demonstrate that NRF2 downregulation in oligodendrocytes is sufficient to blunt cognitive performance over time in aging and induce thicker myelin and a shift to smaller myelinated axon diameters, mirroring observations in severe cognitive decline in human aging. Altogether, these results indicate a novel role for oligodendroglial NRF2 in contributing to cognitive impairment in aging.

Discussion

Here we identify the oligodendrocyte as a contributor to the neuropathology of cognitive decline with aging. Using a unique human brain bank from a cohort with lifelong individual trajectories of cognitive status complemented by functional modeling in transgenic mice, we reveal that oligodendrocyte dysfunction contributes to myelinated axon pathology and cognitive impairment over time in aging. This study suggests that oligodendrocytes are not always supportive of CNS function and cognition, revealing that they can become actively detrimental in aging.

We found that individuals with severe cognitive decline have a loss of myelin integrity, including abnormally thicker myelin on large-diameter axons and a shift toward smaller myelinated axon profiles. These may directly influence cognitive ability, as 85% of variance in human axon conduction velocity is predicted to be controlled by myelin thickness and axon size54. Nonetheless, our association of more myelin with worse cognitive decline is surprising, given that, in youth/adulthood, learning involves myelin production1,55, and cognitive deficits are typically associated with myelin loss56,57. This could reflect that aging-associated cognitive decline involves surpassing the amount of additional myelin which is beneficial. Indeed, excess myelin thickening is known to precede CNS neurodegeneration in young adults with multiple sclerosis58, in aged non-human primates59 and in mouse models of amyloidopathy37,60–62. Consistent with these findings, myelinated axons are recently recognized to be more vulnerable to degeneration than non-myelinated axons in human24 and mouse white matter63, indicating that pathological myelin is more deleterious for axons than no myelin at all. We build upon this by now identifying a particular vulnerability of myelinated axons of large diameter (>1 µm) in human aging, which preferentially show thicker myelin and decreased frequencies in more severe cognitive decline. This contrasts with previous work documenting decreased small-diameter myelinated axons in human white matter in aging versus youth64–66; we did not detect this phenomenon in association with cognitive decline, suggesting that small axon pathology may occur at an equal rate across aged individuals yet could have less impact on cognitive decline than large axon pathology.

We uncovered that oligodendrocyte dysfunction can be driven by NRF2 deficiency. Decreased expression of NRF2 in oligodendrocytes was observed in individuals with severe cognitive decline; modeling this in mice was functionally sufficient to cause thicker myelin and a shift toward myelination of smaller-diameter axons, and to blunt cognitive improvement over time in aging. NRF2 regulates the activation of hundreds of genes in a cell-type-specific manner, in turn influencing a wide range of cellular functions, including autophagy, antioxidant responses, proliferation and differentiation; here we identify a novel role for NRF2 in oligodendrocytes in regulating myelin thickness and myelinated axon health. Although the exact mechanism by which NRF2 regulates myelin thickness is unknown, the established interplay between NRF2 and autophagy in other cell and tissue systems suggests that this could contribute and would be consistent with autophagy disruption in oligodendrocytes leading to hypermyelination and subsequent neurodegeneration67–69. This complements the well-established role of NRF2 in cognition, where global NRF2 knockout exacerbates age-related cognitive deficits in several mouse models41–49; drug-induced NRF2 activation (using dimethyl fumarate) improves cognitive function in rodents and in multiple sclerosis70–74, yet the contributing cell type was undefined. Together with our findings, this highlights the possibility of repurposing NRF2 activators to mitigate oligodendrocyte dysfunction associated with cognitive decline in human aging.

There are likely several triggers of oligodendrocyte dysfunction and consequent myelin pathology in aging. Our data suggest that an increase in oligodendrocytes may occur upstream of NRF2 downregulation. Whether the increased oligodendrocytes in more severe cognitive decline reflects new generation in aging or enhanced generation earlier in life is unknown, although limited oligodendrogenesis in human aging75 may support the latter possibility. Our finding of a deleterious association of oligodendrocyte densities with cognitive status is unexpected, as new oligodendrocyte generation is required for learning during development and adulthood in mice2–5, and supraphysiological boosting of new oligodendrocytes in aged mice can attenuate age-related memory deficits4,6,76. Nevertheless, increased oligodendrocyte densities have been previously documented in human pathology—in the gray matter in Alzheimer’s disease77 and in the white matter after developmental insults78— although the functional status of these oligodendrocytes and the consequences of this increase remain unexplored in these contexts. Our findings suggest that, at least in the context of human aging, an increase in oligodendrocytes is associated with adoption of a state that is dysfunctional. There are likely also other upstream drivers of oligodendrocyte dysfunction and myelin pathology in aging; changes in the aging brain, such as inflammation and vascular pathology, are known modulators of NRF2 (refs. 79,80) and oligodendrocyte/myelin biology81,82 and may, therefore, contribute to cognitive decline. In addition, an established driver of increased myelin thickness is neuronal activity, which drives experience-dependent adaptive myelin remodeling to support learning83,84. Neuronal activity is suggested to increase in aging in some individuals with detrimental outcomes for lifespan85, and we speculate that this may also support maladaptive (excessive) myelination86 to contribute to cognitive decline. Aging humans often show greater activity in certain brain regions during cognitive tasks compared to younger individuals87–89, which may reflect an increase in neuronal activity in an attempt to compensate for cognitive decline. Investigation of other brain regions beyond the corpus callosum could, therefore, reveal additional cellular and molecular contributors to cognitive decline in aging.

Altogether, our study reveals the oligodendrocyte to be a contributor to the white matter neuropathology associated with cognitive decline in aging. This work, therefore, highlights oligodendrocytes and their NRF2 expression as novel therapeutic targets to help support cognitive health in aging.

Methods

LBC1936

Cognitive data and brain samples were obtained from LBC1936 (Medical Research Council Brain Bank, The University of Edinburgh), with ethics permission from the Multi-Centre Research Ethics Committee for Scotland (MREC/01/0/56), the Lothian Research Ethics Committee (LREC/2003/2/29) and the Scotland A Research Ethics Committee (07/MRE00/58). Participants are Scottish, of majority white European ancestry and include both sexes (gender data not requested). Clinical history and neuropathological examinations were provided by C. Smith (The University of Edinburgh). The use of LBC samples was in accordance with the terms of use for donor tissue and information.

Cognitive testing

In 1947, participants of mean age 11 years underwent a group-administered 45-minute general intelligence test (Moray House Test (MHT) No. 12), which indicated concurrent validity with the standardized Stanford−Binet test of intelligence (R = 0.8)90. Individuals consented to re-testing at age 70 onwards13; of 1,091 participants, 1,017 had viable age 11 and age 70 test scores, enabling correlation between IQ in childhood and aging. In total, 866 participated beyond wave 1, of whom 816 had viable age 11 test scores, enabling correlation between cognitive decline in aging and age 11 IQ. Data from waves 1−5 were used in this study, where standardized cognitive tests were administered at each wave (approximately every 3 years), in addition to the MHT (No. 12) (Fig. 1a and Extended Data Fig. 1a). Of these, 10 tests were input to a latent growth curve model in a structural equation modeling framework to indicate latent general cognitive ability14–16, including matrix reasoning, block design, digit−symbol match and symbol search (Weschler Adult Intelligence Scale III); logical memory, digit span, verbal paired associates and digit backwards test (Weschler Memory Scale III); choice reaction time (UK Health and Lifestyle Survey); and inspection test (constructed using E-Prime software). Raw test results were scaled so that variances were in similar ranges across tests. Growth curve models for individual tests were specified and fit to the data using the lavaan (version 0.6.21), haven (version 2.5.5) and psych (version 2.6.5) packages in R (version 4.4.2), with slope weightings as the mean test interval (years) and using full information maximum likelihood estimation. A hierarchical model was fit in which cognitive test scores were assigned to cognitive domains (Extended Data Fig. 1a), and high correlations between domains were modeled as a hierarchical general cognitive factor (g)14–16, showing excellent fit to the data (Tucker−Lewis index (TLI) of 0.945, comparative fit index (CFI) of 0.946, standardized root mean square residual (SRMR) of 0.066 and root mean square error of approximation (RMSEA) of 0.033). Intercept and slope scores were extracted for each individual using the lavPredict function (lavaan package, version 0.6.21); the intercept represented g at age 70, and the slope represented annual decline from 70 to 82 years. All but one individual showed a negative rate of change in g (Sg < 0) beyond age 70, with the average decline per year being −0.26 s.d. g units. Trajectories declining more steeply than this average (that is, Sg < −0.26) were classed as ‘severe’, whereas those above average were classed as ‘mild’.

Human brain tissue

Brain tissue was collected from a subset of consenting individuals91. Postmortem intervals ranged between 1 day and 4 days, during which cadavers were stored at 4−6 °C92. LBC1936 samples (79−85 years) and adult samples (34−49 years) were acquired from the LBC tissue bank or the Sudden Death Brain Bank under the Medical Research Council Edinburgh Brain and Tissue Bank (21/ES/0097) and approved by local ethical review boards in Edinburgh and at Unity Health Toronto (REB 23-088). For LBC1936, one hemisphere was drop fixed into 10% (w/v) formalin fixative for paraffin embedding and histochemistry, and the remaining hemisphere was sliced into 1-cm-thick coronal sections; 1-cm3 regions of interest were collected as tissue punches91 and processed for electron microscopy. Adjacent samples were snap frozen for transcriptomic analyses.

Electron microscopy of human white matter

Fresh human postmortem brain tissue (1 cm3)91 was placed in 0.1 M phosphate buffer. Tissue was dissected (1 × 1 mm3) and fixed for 48 hours in 4% paraformaldehyde (PFA; Electron Microscopy Sciences, cat. no. 15710) and 2.5% (w/v) glutaraldehyde (Electron Microscopy Sciences, cat. no. 16310) in 0.1 M electron microscopy-grade phosphate buffer (pH 7.4) at 4 °C and then washed and stored in 0.1 M phosphate buffer for a minimum of 24 hours92. Samples were incubated in 1% (w/v) osmium tetroxide (Electron Microscopy Sciences, cat. no. 19152) in 0.1 M phosphate buffer for 30 minutes at room temperature, washed in 0.1 M phosphate buffer and then ddH20 and dehydrated in graded ethanols, finishing in propylene oxide (Sigma-Aldrich, cat. no.56671-5ML-F). Sections were embedded in durcupan resin (Sigma-Aldrich, cat. no.44610-1EA) for 24 hours and polymerized for 48 hours at 56 °C. Ultra-thin sections (50 nm) were cut on an ultra-microtome (Leica, EM UC7) and stained with uranyl acetate and lead citrate, and grids were imaged on a JEOL JEM 1400 Plus transmission electron microscope. A minimum of 100 myelinated axons were traced for axon diameter and myelin thickness using Fiji ImageJ, assuming circularity (axon diameter = 2 × sqrt(axon area / Π)). Myelin thickness (g ratio) was calculated by (inner axon diameter / outer myelinated axon diameter). Large-diameter axons were categorized as being >1 µm, at which myelin is thickest under homeostasis93 and more vulnerable to increased thickness in disease37. Myelinated axons were quantified in an average area of 1,500 µm2. Mitochondrial area was measured from a minimum of 100 myelinated axons per donor and traced using the polygon tracing tool (Fiji ImageJ). Degenerating axons were defined as lacking visible pallor with intensities equivalent to cytoplasmic space. Seventeen lateral corpus callosum samples were available for electron microscopy analysis; due to poor tissue quality, two cases were excluded from myelinated axon density and four from myelin thickness analysis. Investigators were blinded to case history and cognitive trajectories throughout analysis.

snRNA-seq

A pilot study optimized retrieval of high-quality nuclei for sequencing (Extended Data Fig. 2a−f), testing a previously established protocol (1)30,31, including twice the input material (2) or using FANS to isolate nuclei and exclude myelin debris (3). Fresh-frozen human tissue sections (20 µm) were cut at −20 °C and placed into ultra-low binding Eppendorf tubes on ice. Either five sections (protocols 1 and 3) or 10 sections (protocol 2) of 1 × 1 cm3 were utilized. Then, 200 µl of lysis buffer (1,700 µl of PURE buffer (Sigma-Aldrich, NUC-201-1KT), 17 µl of 0.1 M DTT (Thermo Fisher Scientific, cat. no. 15846582), 17 µl of 10% Triton-X (BioShop Canada, cat. no. TRX777.100) and 34 µl of SUPER RNAse inhibitor (Thermo Fisher Scientific, cat. no. AM2696)) was added to each tube and tissue homogenized on ice with needles of ascending gauge until a smooth consistency was achieved. Lysates were mixed with 360 µl of sucrose cushion medium (PURE 5 M sucrose solution (Sigma-Aldrich, NUC-201-1KT)) containing 11% PURE sucrose cushion buffer (Sigma-Aldrich, NUC-201-1KT), 0.11% DTT (Thermo Fisher Scientific, cat. no. 15846582) and 0.02% RNAse inhibitors (Thermo Fisher Scientific, cat. no. AM2696) and then passed through a 30-µm strainer (Wolf Laboratories, 04-004-2326). The filtrate was gently plated on 200 µl of the sucrose cushion medium, and samples were centrifuged for 45 minutes at 16,100g at 4 °C. The pellet was resuspended in 100 µl of elution buffer, which, for protocols 1 and 2, constituted 7 ml of PURE storage buffer (Sigma-Aldrich, NUC-201-1KT) containing 70 µl of RNAse inhibitor (Thermo Fisher Scientific, cat. no. AM2696) and, for protocol 3, constituted sterile PBS (Thermo Fisher Scientific, cat. no. BP399-20) with 1% fatty-acid-free BSA (Sigma-Aldrich, cat. no. A8806-1G), 2 mM EDTA and 1% RNAse inhibitor (Thermo Fisher Scientific, cat. no. AM2696). Resuspended samples were moved into a fresh tube, centrifuged for 5 minutes at 1,000g at 4 °C and pellet resuspended in 200 µl of elution buffer. DAPI (1:1,000) was added in to protocol 3. Samples were centrifuged for 5 minutes at 1,000g at 4 °C and resuspended in 50−150 µl of elution buffer. For protocols 1 and 2, nuclei were counted by mixing with trypan blue (1:1) using the Bio-Rad Cell Counter TC200. Nuclei concentration was standardized to 100,000 nuclei per milliliter using the elution solution and processed for snRNA-seq. Protocol 3 was FAN-sorted using a BD FACSAria II cytometer running BD FACSDiva software (version 8.0.1), gating on DAPI+ nuclei; 60,000 events were captured into elution solution, the sample centrifuged at 750g for 5 minutes, pellets resuspended in 50 µl of elution solution and standardized to 100,000 nuclei per milliliter. As sorting nuclei provided the best results, protocol 3 was utilized when running the LBC samples as singular cases. Eight cases were processed as separate samples per day, with 2 days of processing (total 16 cases), filling a 10x Genomics Chromium Single Cell 3′ chip. Best efforts were taken to balance each batch for sex, age and degree of cognitive decline. In total, 52,000−60,000 nuclei were collected per case during the FANS step, with final 10× loading of 20,000 nuclei per case.

Library preparation and next-generation whole-transcriptome sequencing

Approximately 20,000 nuclei per sample were loaded onto a 10x Genomics Chromium Single Cell 3′ chip. Then, 10x Genomics v3 beads, gel beads-in-emulsion (GEM) kits and library kits were used as per the manufacturer’s instructions to generate Chromium Single Cell 3′ Gene Expression (GEX) libraries. Library concentrations and quality were tested with an Agilent TapeStation (HS D5000). Two of 16 samples had cDNA libraries with a concentration lower than 6 ng µl−1 and were, therefore, of poor quality; all other samples had libraries between 6.37 ng µl−1 and 13.8 ng µl−1. Libraries were assessed for size distribution on the Agilent Bioanalyzer (Agilent Technologies, G2939AA) with the DNA HS Kit (5067-4626) and quantified using the Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Q32866) and the Qubit dsDNA HS assay kit (Q32851). Library molarity for sequencing was calculated using the Qubit dsDNA quantification results and the fragment size information from the Bioanalyzer results. Sequencing was performed on the NextSeq 2000 platform (Illumina, SY-415-1002) using the NextSeq 1000/2000 P3 Reagents (100 cycles) v3 kit (20040559). Libraries were pooled in equimolar quantities, and PhiX Control v3 (FC-110-3001) library was spiked into the three runs at a concentration of approximately 1% to help with cluster resolution and facilitate troubleshooting. All 16 libraries were initially run on one P3 flow cell, and resulting nuclei quality was confirmed. Two of 16 samples with significantly lower nuclei counts were flagged as having poor library quality and removed from further analysis. The remaining 14 libraries were sequenced on an additional two P3 flow cells. The three flow cells generated 180 GB per run (approximately 540 GB total) with approximately 80% of clusters passing filter during sequencing and 93% of the data in each run being >Q30. Base call data (generated by NextSeq 1000/2000 Control Software version 1.5.0.42699) were transferred to a computer running Ubuntu (version 20.04.2 LTS), and Cell Ranger (version 7.1.0) generated FASTQ files via demultiplexing raw base call files. FASTQ files were demultiplexed and aligned to the human reference genome GRCh38 using 10x Genomics’ Cell Ranger pipeline (version 7.1.0) on a high-performance computing cluster. Prior to any quality control, 46,988 nuclei were retrieved with an average of 2,715 genes per nucleus, 7,750 UMIs per nucleus and an average mitochondrial percentage per nucleus of 2.22%. The quality of nuclei retrieved did not significantly differ across samples (Extended Data Fig. 2g−i).

snRNA-seq analysis

Filtered feature matrices resulting from the alignment of each sample with Cell Ranger (version 7.1.0) were read into R (version 4.4.2) using the Read10X function (Seurat package, version 5.2.1), and Seurat objects for each sample were created with the CreateSeuratObject (SeuratObject package, version 5.1.0). Each Seurat object was visually inspected for number of features, counts and mitochondrial gene percentages. Filtering thresholds across Seurat objects were chosen such that only nuclei with a feature number between 200 and 7,500 and mitochondrial gene percentages less than 10% were included, as recommended for human nuclei94. No nucleus had a UMI less than the recommended cutoff of 500, with the average UMI count per nucleus being 7,750 before filtering. After quality control filtering, we retained 45,437 nuclei, with an average of 2,740 genes and 7,705 UMIs per nucleus. Seurat objects were normalized, and the 2,000 most variably expressed features across objects were selected as integration anchors to combine the samples into one Seurat object. The resulting integrated object was scaled, and linear dimensional reduction via principal component analysis (PCA) was performed. Elbow plots and JackStraw plots visualized the contributions of each principal component, and the top 11 were chosen as input to cluster the nuclei in a uniform manifold approximation and projection (UMAP) space. For primary cell type annotation, a resolution value of 0.01 was used.

Cell type annotation and subsetting

Canonical features of expected populations (PLP1, BCAS1 and ENPP6 for oligodendrocytes; GFAP, ALDH1A1 and AQP1 for astrocytes; PTPRC, CSF3R and DOCK8 for immune cells; PDGFRA, MMP16 and CSPG4 for oligodendrocyte precursor cells; SNAP25, GRIN1 and CHRM2 for neurons; and CLDN5, MYO1B and SLC38A11 for endothelial/vascular cells) were visualized in the combined UMAP by feature plots. Cell type identities were confirmed via inspection of top-expressed genes within each cluster. To analyze cell type subpopulations, we subset the combined Seurat object by each cell type cluster, and the resulting objects underwent scaling and linear dimensional reduction via PCA. An appropriate dimensionality of the data was chosen via inspection of the elbow plots. For all subset populations, a dimensionality of 8 was chosen for further processing. UMAPs were run with the input dimension set to 8, and single-nucleus neighbors and clusters were found via k-means clustering. Resolution was determined through trialing progressively higher resolutions for each cell type and inspecting subcluster separation (visualized via heatmaps of the top-expressed genes using Model-based Analytics of Single-cell Transcriptomics (MAST)) and alignment with known biological variation in cell types. Oligodendrocyte subclusters were well separated transcriptionally (Extended Data Fig. 3a), recapitulating the approximate number seen previously30,31,34,95, including in human33. The final resolutions used for each cell type subset were 0.2 for oligodendrocytes (seven clusters), 0.07 for oligodendrocyte precursor cells (three clusters), 0.1 for neurons (four clusters), 0.02 for astrocytes (three clusters), 0.2 for immune cells (seven clusters) and 0.05 for endothelial/vascular cells (four clusters) (Extended Data Fig. 3b).

Differential gene expression analysis

To analyze differential gene expression, pseudobulk count matrices of the sequencing data were generated using the limma and voom pipeline within the limma (version 3.56.2) and edgeR (version 3.42.4) packages in R (version 4.4.2). For each cell type, pseudobulk groups corresponding to cell type subclusters for each individual were defined. The pseudobulk count matrix was normalized to counts per million per gene and filtered such that only genes with at least one read in at least 80% of samples were retained. The library sizes were reset to reflect the refined dataset, and raw library sizes were converted into normalized effective library sizes via the calcNormFactors function in edgeR (version 3.42.4). Only clusters found in more than half of the sequenced cases were analyzed for DEGs, excluding four subpopulations (Neuron2, Astro2 and Immune 5/Immune6). To analyze DEGs in oligodendrocyte subclusters, contrast matrices were defined to compare each enriched subpopulation against all other oligodendrocyte subpopulations. These design matrices were used as input when readying count data for linear modeling using the voom function of the limma (version 3.56.2) package, and resulting voom plots were inspected to check that gene filtering thresholds were stringent enough to remove poorly predictive data. Voom output on the filtered dataset was fit to a linear model (lmFit function, ‘stats’ package (version 4.3.1)) adhering to the design matrix defined, and estimated coefficients were computed (contrasts.fit function, limma package (version 3.56.2)). Log-odds of differential expression were generated via empirical Bayes moderation of the standard errors toward a global value (eBayes function, limma package (version 3.56.2)). To analyze differential gene expression in cell type subclusters across cognitive decline scores, design matrices were generated that included cognitive decline slopes and the following covariates: absolute cognitive score at age 70, neuropathological indications of Alzheimer’s disease or small vessel disease, age, sex and size of the cluster per donor; this regressed out variability associated with biological processes distinct from the rate of cognitive decline, which drastically reduced the number of DEGs passing the threshold of adjusted P < 0.05.

Pathway analysis

Pathway analysis was implemented using IPA software. For analysis of Oligo4 and Oligo5 across the spectrum of cognitive decline, a filtration cutoff of P < 0.1 was applied in order to include a sufficient number of genes required for statistical confidence. Predicted upstream regulators were only considered for follow-up analysis if they were not included as chemical drugs or toxicants and had an ‘activated z-score’ (indicative of significant pathway activity) greater than absolute 2.

SCoRe imaging

SCoRe imaging was carried out using a Leica Stellaris 5 confocal microscope with an RT85/15 beamsplitter set to capture reflectance. Laser wavelengths were set to 488 nm, 561 nm and 638 nm, and detectors centered around these wavelengths at the minimum physical opening threshold of 5 nm. For the first image in each sample, laser power and gain were increased until the first positive saturated pixel was detected, and then image settings were fixed. For the human tissue, formalin-fixed paraffin-embedded (FFPE) sections were deparaffinized, and eight technical replicate images were taken with a ×63 oil objective (numerical aperture 1.4; HC PL APO CS2) at systematic random sampling intervals across the section. The readout from each channel was merged into one composite image and converted to an RGB 8-bit image, and then the percentage area of signal above threshold background was quantified using a fixed threshold (value 9, based on the average default threshold across the first three samples imaged) in Fiji ImageJ.

Immunofluorescent staining of human white matter

FFPE sections (6 µm) of lateral corpus callosum were dewaxed at 60 °C for a minimum of 2 hours and then rehydrated in xylene (2 × 10 minutes) and 5-minute incubations in ethanol (100%, 100%, 95%, 80%, 70%). Samples were microwaved in hot (approximately 95 °C) 0.1 M sodium citrate buffer (pH 6; Vector Laboratories, cat. no. H-3300-250, diluted in ddH2O) for 5−10 minutes, placed in a 60 °C oven for 30 minutes and then cooled at room temperature. Endogenous peroxidase and alkaline phosphatase blocking solution (Bloxall; Novus Biologicals, cat. no. Sp-6000-NB) was applied for 10 minutes, before a 1-hour block in 10% normal horse serum with 0.3% Tween 20 (Thermo Fisher Scientific, cat. no. BP337100) in 0.1 M TBS (Thermo Fisher Scientific, cat. no. BP24711). Primary antibodies were applied in a humid chamber overnight at room temperature. Tissue was washed (3 × 5 minutes in 0.1 M TBS), incubated for 1 hour with an HRP-conjugated secondary antibody (Vector Laboratories) and then washed again and incubated with a tyramide Opal-conjugated secondary antibody diluted in amplification diluent for 10 minutes (Akoya Biosciences, cat. no. FP1135). Tissue was washed, and Hoechst (1:1,000; Thermo Fisher Scientific, cat. no. H1399, lot 2936197) was added into the penultimate TBS wash. Sections were dried, mounted with Fluoromount G (Thermo Fisher Scientific, cat, no. 00-4958-02) and coverslipped (Thermo Fisher Scientific, cat. no. 12541033CA). For oligodendrocyte detection, a rabbit primary antibody against Olig2 (1:100; Sigma-Aldrich, cat. no. AB9610, RRID: AB_570666, lot 3857662), a secondary horse anti-rabbit HRP-ImmPress antibody (Vector Laboratories, cat. no. MP-7401, RRID: AB_2336529, lot ZK0830; used neat) and a tyramide Opal-570 fluorophore (1:100; Akoya Biosciences, cat. no. FP1488001KT, lot 231016009) were applied. For NRF2 and Olig2 double staining, mouse anti-Olig2 (1:100; Sigma-Aldrich, MABN50; lot 421151), horse anti-mouse HRP-ImmPress secondary antibody (Vector Laboratories, cat. no. MP-7402-15, lot ZL1001) and tyramide Opal-570 fluorophore (1:100; Akoya Biosciences, cat. no. FP1488001KT, lot 231016009) were applied first, followed by incubation with a rabbit primary against NRF2 (1:100; Abcam, cat. no. AB31163, RRID: AB_881705, lot 1030320-17) diluted in 10% normal horse serum and 0.3% Tween 20 (Thermo Fisher Scientific, cat. no. BP337100) in 0.1 M TBS overnight at room temperature and a 1.5-hour incubation in donkey anti-rabbit Alexa647-conjugated secondary antibody (1:200; Thermo Fisher Scientific, cat. no. A-31571, RRID: AB_162542, lot 3219281). For neuronal staining, mouse primary antibody against SMI-32 (1:500; BioLegend, cat. no. 801701, RRID: AB_2564642, lot B448462), horse anti-mouse HRP-ImmPress secondary antibody (Vector Laboratories, cat. no. VECTMP740215, lot ZL1001) and tyramide Opal-520 fluorophore (1:100; Akoya Biosciences, cat. no. FP1487001KT, lot 230919042) were applied, with all washes including 0.3% Tween 20. Sections were washed, and remaining tyramide signal amplification was heat stripped via hot citrate buffer. Slides were cooled, and then chicken primary antibody against neurofilament (1:500; BioLegend, cat. no. 822601) was incubated overnight at room temperature, followed by washing and 1.5-hour incubation with donkey anti-chicken Alexa647 secondary antibody (1:200; Thermo Fisher Scientific, cat. no. 78952, lot 2622383).

Immunofluorescent imaging and quantification of human white matter

Tile-scanned images across human FFPE sections were generated by a Zeiss AxioScan.Z1 slide scanner with a ×20 Plan Apochromat objective (numerical aperture 0.8). For Olig2 density analysis, the entirety of each FFPE section imaged was analyzed, corresponding to approximately 1.5 × 1.5 cm2 of tissue surveyed and a minimum of 62,000 nuclei per individual. Olig2+ cell densities were quantified using the cell detection application in QuPath (version 0.5.1). Thresholds were chosen (between 1,200 and 3,400 for Hoechst; between 2,700 and 4,700 for Olig2), which optimized the cell detection algorithm without losing positive cells or introducing background signal. Fixed thresholds were not applied across samples as these resulted in false-positive and false-negative detections. Autofluorescent blood vessels were manually traced and removed. For NRF2 and SMI-32 analyses, at least 3 × 1 mm2 randomly sampled technical replicate images were exported and analyzed with Fiji/ImageJ (Fiji.sc version 2.1.0/1.53c). For NRF2 analysis, NRF2+Olig2+ cells were manually quantified with the Cell Counter plugin in ImageJ. For SMI-32 analysis, any SMI-32+ cell body staining co-localizing with Hoechst was circled using the oval tool in Fiji and manually excluded to specifically assess axonal SMI-32. Fixed threshold analyses unreliably yielded false-positive and false-negative threshold detection across samples, so the automatic threshold detector in ImageJ was used to detect SMI-32 signal. For all stains, the average of technical replicates was used as one biological replicate. Case cognitive scores, sex, age and IQ values were blinded to the investigator during the analysis. Results were exported from QuPath/ImageJ and graphed using R (version 4.4.2).

Nfe2l2 oligodendrocyte-specific conditional knockout mice

Plp-CreERT mice96 on a C57BL/6J background (Popko laboratory, Northwestern University) were crossed with Nfe2l2fl/fl mice (The Jackson Laboratory, 025433)97 on a C57BL/6J background to obtain a tamoxifen-inducible conditional knockout mouse (Plp-CreERT;Nfe2l2fl/fl). Animals (both sexes) were housed in pathogen-free, temperature-controlled conditions (maintained at an ambient temperature of 20−24 °C and 25−50% humidity) in accordance with the guidelines set forth by the Northwestern University Institutional Animal Care and Use Committee. Animals had ad libitum access to food and water and were on a 12-hour per day light on/off cycle. At 6 months, animals were intraperitoneally injected with 1 mg d−1 4-hydroxy-tamoxifen (4-OHT; Sigma-Aldrich, H6278) diluted in sunflower oil for five consecutive days. At 12 months, animals underwent cognitive testing, first on the large open field (LOF) test and then the MWM. After cognitive testing, a subset of the 12-month-old animals were either intracardially perfused with 4% PFA (Thermo Fisher Scientific, cat. no. T353-500) and snap frozen in optimal cutting temperature (OCT) compound for immunofluorescence or intracardially perfused with 0.1 M cold PBS (Thermo Fisher Scientific, cat. no. BP399-20) followed by 4% PFA (Thermo Fisher Scientific, cat. no. T353-500) with 2.5% glutaraldehyde (Electron Microscopy Sciences, cat. no. 16220) in 0.1 M sodium cacodylate (Electron Microscopy Sciences, cat. no. 11652) for electron microscopy. Separate cohorts of Plp-CreERT;Nfe2l2fl/fl animals (which did not receive behavioral testing) were collected at 15 months and 18.5 months for immunohistochemistry and electron microscopy, respectively. Mice from both sexes were used at each timepoint; however, female mice showed genotype-specific differences in the LOF test (Extended Data Fig. 4a,b) and, thus, were excluded from further analysis.

RNAscope

RNAscope was performed using an RNAscope v2 Multiplex Fluorescence Kit (ACDBio, cat. no. 323136) with a probe against murine Nfe2l2 (ACDBio, cat. no. 475571), following the ACDBio protocol. Coronal sections from 12−15-month-old Plp-CreERT;Nfe2l2fl/fl and age-matched controls were thawed for 30−60 minutes at room temperature and then dehydrated by 5-minute incubations of increasing ethanol concentrations (50%, 70%, 95%, 100%). Hydrophobic barriers were drawn around tissue (ImmEdge barrier pen; ACDBio, cat. no. 323136), and sections were incubated in hydrogen peroxide for 10 minutes. Slides were placed in hot antigen retrieval buffer at 95 °C for 5 minutes and then cooled. Manual Pretreat Pro (RNAscope v2 Multiplex Fluorescence Kit; ACDBio, cat. no. 323136) was incubated at 40 °C for 30 minutes, and the Nfe2l2 probe was hybridized at 40 °C for 2 hours. Separate sections were hybridized for 3-plex positive (Pol2RA; PPIPB; UBC) or negative (DapB) control probes. A series of amplification probes (AMP1,2,3) were applied in 15−30-minute incubations, after which secondary antibodies were added (Akoya Biosciences, FP1496001KT). HRP blocker was added for 15 minutes at 40 °C, and tissue was incubated overnight with primary antibody against Olig2 (goat anti-Olig2 at 1:200; R&D Systems, cat. no. AF2418). Sections were washed, and donkey anti-goat Alexa568 secondary antibody (1:200; Thermo Fisher Scientific, cat. no. 11057) and Hoechst (1:10,000) were applied for 2 hours at room temperature before being washed and mounted.

Behavioral assays

Twelve-month-old (±10 days) male and female Plp-CreERT;Nfe2l2fl/fl and age-matched control mice were tested at the Northwestern University Behavioral Phenotyping Core. Mice were moved to the behavioral testing facilities 30 minutes prior to testing for habituation. Between active testing, animals had ab libitum access to water and food. A maximum of 25 animals per day were tested. Animals underwent testing on the LOF (54.6-cm2 arena) in a soundproof room under white light, with four animals tested simultaneously in four separate closets. Arenas of the LOF were defined as 18-cm2 squares at the center or corners of the arena. Animals were allowed to explore freely for 10 minutes. LimeLight5 software from Actimetrics recorded time spent in the center or corners. LOF arenas were cleaned with 70% ethanol between each animal. Female Plp-CreERT;Nfe2l2fl/fl animals showed significantly altered anxiety-like behavior and were, thus, excluded from further analysis. The MWM consisted of a circular water bath (diameter 1.5 m) filled with water and a non-toxic white dye that discolored the water, and was divided into four quadrants corresponding to ordinal directions (NE/SE/SW/NW) with visible cues (91.4 cm wide) on the wall at each direction of the maze (N/E/S/W). MWM testing took place over 9 days, consisting of a 3-day acclimatization phase, a 2-day rest period and then a 4-day testing phase. During acclimatization, a visible platform (20 × 3 × 25.4 cm) was submerged but identifiable via projecting pillars. On each day, animals were tested in two sessions, with four trials per session (total of eight trials per day per animal). In each trial, mice were allowed to navigate to the platform for up to 1 minute and removed upon successful navigation to the platform. If a mouse ceased swimming and floated on its back, the mouse was removed and the trial was ended. At the end of the trial, the platform changed location; the order of which quadrant the mouse was placed in, to where the visible platform was located, was NE to NW; SE to SW; SW to NE; and NE to SE. Next, during testing, a hidden platform (19.05 cm in diameter) was submerged without visible pillars and remained in the same location in the NE quadrant. On each day, animals were tested in two sessions, with three trials per session (total of six trials per day per animal), and were placed in different starting quadrants corresponding to the following order: SE, NW, SW. As above, in each trial, animals were allowed to navigate to the platform for up to 1 minute, removed upon successful navigation to the platform and removed/trial ended if an animal ceased swimming and floated on its back. If an animal failed to reach the platform after 1 minute (in either phase), they were placed on the platform for another minute. During all trials, investigators remained visibly hidden to the animals by stepping behind a curtain. WaterMaze6 software (Actimetrics) recorded mouse location, and experimenters measured time to reach the platform with a manual timer. The daily average of trials in either acclimatization or testing phases was averaged to compute the time spent in the target quadrant, average swimming speed (cm s−1) and time taken to reach the platform (latency). Total path traces for each trial in the test phase were manually categorized by an experimenter blinded to genotype into search strategies53: direct (6), directed search (5), focal search (4), indirect search (4), chaining (3), scanning (2), random search (1) and thigmotaxis (0). The percentage of trials adopting the various search strategies on each testing day, the composite score across trials per day and the fold change in composite score (relative to the day 1 within-genotype average) were calculated per animal. To correlate performance in the MWM with neuropathological features, animals were split into two groups based on the average fold change in the percentage of time spent in the target quadrant on day 4, with ‘better performers’ having an above-average fold change and ‘poorer performers’ having a below-average fold change.

Immunofluorescent staining, imaging and analysis of murine white matter

Coronal sections (25 µm) from 15-month-old Plp-CreERT;Nfe2l2fl/fl animals were thawed for 10 minutes and then blocked for 1 hour with 5% normal horse serum and 0.5% Triton-X (BioShop Canada, cat. no. TRX777.100) in 0.1 M PBS (Thermo Fisher Scientific, cat. no. BP399-20). Primary antibodies against Olig2 (1:100; Sigma-Aldrich, cat. no. AB9610, RRID: AB_570666), CC1 (1:200; Abcam, cat. no. AB16794, RRID: AB_443473) and PDGFRα (1:200; R&D Systems, cat. no. AF1062, RRID: AB_2236897) were incubated overnight at room temperature in a humid chamber. Tissue was washed in 0.1 M PBS three times for 5 minutes. Secondary antibodies donkey anti-goat Alexa488 (1:100; Thermo Fisher Scientific, cat. no. A-11055, RRID: AB_2534102), donkey anti-rabbit Alexa568 (1:100; Thermo Fisher Scientific, cat. no. A10042, RRID: AB_2534017) and donkey anti-mouse Alexa647 (1:100; Thermo Fisher Scientific cat. no. A-31571, RRID: AB_162542) were applied for 2 hours, alongside Hoechst (1:10,000; Thermo Fisher Scientific, cat. no.H1399). Slides were washed in 0.1 M PBS three times for 5 minutes and coverslipped with Fluoromount G (Thermo Fisher Scientific, cat. no. 00-4958-02; Thermo Fisher Scientific, cat. no. 12541033CA). For each animal, at least three 10-µm z-stack tiled images through the corpus callosum were taken on a Zeiss LSM 900 with a ×20 magnification lens (numerical aperture 0.8). Maximum projections were manually counted for density of oligodendrocyte lineage cells using ImageJ (version 2.1.0/1.53c), and the average of the three technical replicates was used as one biological replicate.

Electron microscopic sample processing, imaging and analysis of murine white matter

Twelve-month-old and 18-month-old mice were perfused with 4% PFA (Thermo Fisher Scientific, cat. no. T353-500) and 2.5% glutaraldehyde (Electron Microscopy Sciences, cat. no. 16220) in a 0.1 M sodium cacodylate solution (Electron Microscopy Sciences, cat. no. 11652). Corpus callosa were dissected and post-fixed in the above fixative for 2 weeks at 4 °C and then embedded in epoxy resin (Epon; Electron Microscopy Sciences, cat. no. 14900). Resin blocks were cut to ultra-thins (approximately 80 nm) at the Cellular and Molecular Electron Microscopy (CMEM) facility at SickKids Research Institute/Lunenfeld-Tanenbaum Research Institute and stained with uranyl acetate and lead citrate, and grids were imaged on a Hitachi HT7800 transmission electron microscope. For each animal, at least 200 myelinated axons were traced for axon diameter, myelin thickness (g ratio) and inner tongue thickness using Fiji ImageJ and assuming circularity, as above.

Statistical analysis

Statistical analyses were computed using R (version 4.4.2) or GraphPad Prism (version 10.2.3). Pearson’s correlation tests were performed for correlation analyses. For testing the difference between means of two groups, two-sided unpaired Student’s t-tests were performed when there was equal variance between groups, and Wilcoxon rank-sum tests were performed when variance was unequal. For analysis over two variables, two-way ANOVAs were used with Tukey’s post hoc multiple comparisons tests or Sidak’s/Fisher’s least significant difference (LSD) multiple comparisons correction. Grubbs testing on group means identified a statistically significant outlier to exclude from g ratio analysis at 12 months. Two animals were excluded from behavioral data analysis due to corrupted video files. For correlational graphs, the 95% confidence interval was calculated using the ggplot2 package (version 4.0.3) in R (version 4.4.2). As per General Data Protection Regulation (GDPR) requirements for the European Union, individual-level data correlating human neuropathological characteristics to identifiable demographic or clinical information could not be displayed; however, correlations among cohort-level clinical, technical and neuropathological variables were displayed via correlation matrices (Pearson’s correlations). For all correlational analyses, categorical variables of sex (male/female) and neuropathological evidence of small vessel disease (yes/no) were classed as 1 or 0, respectively, and severity of Alzheimer’s disease pathology was classified on a numeric scale of Braak pathology ranging from 0 to 6. Power calculations were performed using OpenEpi software (https://openepi.com) and reached greater than 80% power for all experiments.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Online content

Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41591-026-04608-y.

Supplementary information

References

1.Scholz J Klein MC Behrens TEJ Johansen-Berg H Training induces changes in white matter architecture Nat. Neurosci. 2009 12 1370 1371 10.1038/nn.2412 19820707 PMC2770457 Scholz, J., Klein, M. C., Behrens, T. E. J. & Johansen-Berg, H. Training induces changes in white matter architecture. Nat. Neurosci.12, 1370–1371 (2009).10.1038/nn.2412 PMC2770457 19820707

2.McKenzie IA Motor skill learning requires active central myelination Science 2014 346 318 322 10.1126/science.1254960 25324381 PMC6324726 McKenzie, I. A. et al. Motor skill learning requires active central myelination. Science346, 318–322 (2014).10.1126/science.1254960 PMC6324726 25324381

3.Xiao L Rapid production of new oligodendrocytes is required in the earliest stages of motor skill learning Nat. Neurosci. 2016 19 1210 1217 10.1038/nn.4351 27455109 PMC5008443 Xiao, L. et al. Rapid production of new oligodendrocytes is required in the earliest stages of motor skill learning. Nat. Neurosci.19, 1210–1217 (2016).10.1038/nn.4351 PMC5008443 27455109

4.Pan S Mayoral SR Choi HS Chan JR Kheirbek MA Preservation of a remote fear memory requires new myelin formation Nat. Neurosci. 2020 23 487 499 10.1038/s41593-019-0582-1 32042175 PMC7213814 Pan, S., Mayoral, S. R., Choi, H. S., Chan, J. R. & Kheirbek, M. A. Preservation of a remote fear memory requires new myelin formation. Nat. Neurosci.23, 487–499 (2020).10.1038/s41593-019-0582-1 PMC7213814 32042175

5.Steadman PE Disruption of oligodendrogenesis impairs memory consolidation in adult mice Neuron 2020 105 150 164 10.1016/j.neuron.2019.10.013 31753579 PMC7579726 Steadman, P. E. et al. Disruption of oligodendrogenesis impairs memory consolidation in adult mice. Neuron105, 150–164 (2020).10.1016/j.neuron.2019.10.013 PMC7579726 31753579

6.Wang F Myelin degeneration and diminished myelin renewal contribute to age-related deficits in memory Nat. Neurosci. 2020 23 481 486 10.1038/s41593-020-0588-8 32042174 PMC7306053 Wang, F. et al. Myelin degeneration and diminished myelin renewal contribute to age-related deficits in memory. Nat. Neurosci.23, 481–486 (2020).10.1038/s41593-020-0588-8 PMC7306053 32042174

7.Phillips KA Age-related changes in myelin of axons of the corpus callosum and cognitive decline in common marmosets Am. J. Primatol. 2019 81 e22949 10.1002/ajp.22949 30620098 PMC6685070 Phillips, K. A. et al. Age-related changes in myelin of axons of the corpus callosum and cognitive decline in common marmosets. Am. J. Primatol.81, e22949 (2019).10.1002/ajp.22949 PMC6685070 30620098

8.Lu PH Age-related slowing in cognitive processing speed is associated with myelin integrity in a very healthy elderly sample J. Clin. Exp. Neuropsychol. 2011 33 1059 1068 10.1080/13803395.2011.595397 22133139 PMC3269444 Lu, P. H. et al. Age-related slowing in cognitive processing speed is associated with myelin integrity in a very healthy elderly sample. J. Clin. Exp. Neuropsychol.33, 1059–1068 (2011).10.1080/13803395.2011.595397 PMC3269444 22133139

9.Lu PH Myelin breakdown mediates age-related slowing in cognitive processing speed in healthy elderly men Brain Cogn. 2013 81 131 138 10.1016/j.bandc.2012.09.006 23195704 Lu, P. H. et al. Myelin breakdown mediates age-related slowing in cognitive processing speed in healthy elderly men. Brain Cogn.81, 131–138 (2013).10.1016/j.bandc.2012.09.006 23195704

10.Gong Z Lower myelin content is associated with more rapid cognitive decline among cognitively unimpaired individuals Alzheimers Dement. 2023 19 3098 3107 10.1002/alz.12968 36720000 PMC10387505 Gong, Z. et al. Lower myelin content is associated with more rapid cognitive decline among cognitively unimpaired individuals. Alzheimers Dement.19, 3098–3107 (2023).10.1002/alz.12968 PMC10387505 36720000

11.Cox SR Ritchie SJ Fawns-Ritchie C Tucker-Drob EM Deary IJ Structural brain imaging correlates of general intelligence in UK Biobank Intelligence 2019 76 101376 10.1016/j.intell.2019.101376 31787788 PMC6876667 Cox, S. R., Ritchie, S. J., Fawns-Ritchie, C., Tucker-Drob, E. M. & Deary, I. J. Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence76, 101376 (2019).10.1016/j.intell.2019.101376 PMC6876667 31787788

12.Ritchie SJ Coupled changes in brain white matter microstructure and fluid intelligence in later life J. Neurosci. 2015 35 8672 8682 10.1523/JNEUROSCI.0862-15.2015 26041932 PMC4452562 Ritchie, S. J. et al. Coupled changes in brain white matter microstructure and fluid intelligence in later life. J. Neurosci.35, 8672–8682 (2015).10.1523/JNEUROSCI.0862-15.2015 PMC4452562 26041932

13.Taylor AM Pattie A Deary IJ Cohort profile update: the Lothian Birth Cohorts of 1921 and 1936 Int. J. Epidemiol. 2018 47 1042 1042r 10.1093/ije/dyy022 29546429 PMC6124629 Taylor, A. M., Pattie, A. & Deary, I. J. Cohort profile update: the Lothian Birth Cohorts of 1921 and 1936. Int. J. Epidemiol.47, 1042–1042r (2018).10.1093/ije/dyy022 PMC6124629 29546429

14.Ritchie SJ Predictors of ageing-related decline across multiple cognitive functions Intelligence 2016 59 115 126 10.1016/j.intell.2016.08.007 27932854 PMC5127886 Ritchie, S. J. et al. Predictors of ageing-related decline across multiple cognitive functions. Intelligence59, 115–126 (2016).10.1016/j.intell.2016.08.007 PMC5127886 27932854

15.Conte FP Cognitive change before old age (11 to 70) predicts cognitive change during old age (70 to 82) Psychol. Sci. 2022 33 1803 1817 10.1177/09567976221100264 36113037 PMC9660354 Conte, F. P. et al. Cognitive change before old age (11 to 70) predicts cognitive change during old age (70 to 82). Psychol. Sci.33, 1803–1817 (2022).10.1177/09567976221100264 PMC9660354 36113037

16.Corley J Predictors of longitudinal cognitive ageing from age 70 to 82 including APOE e4 status, early-life and lifestyle factors: the Lothian Birth Cohort 1936 Mol. Psychiatry 2023 28 1256 1271 10.1038/s41380-022-01900-4 36481934 PMC10005946 Corley, J. et al. Predictors of longitudinal cognitive ageing from age 70 to 82 including APOE e4 status, early-life and lifestyle factors: the Lothian Birth Cohort 1936. Mol. Psychiatry28, 1256–1271 (2023).10.1038/s41380-022-01900-4 PMC10005946 36481934

17.Hinkley LBN The role of corpus callosum development in functional connectivity and cognitive processing PLoS ONE 2012 7 e39804 10.1371/journal.pone.0039804 22870191 PMC3411722 Hinkley, L. B. N. et al. The role of corpus callosum development in functional connectivity and cognitive processing. PLoS ONE7, e39804 (2012).10.1371/journal.pone.0039804 PMC3411722 22870191

18.Mesaros S Corpus callosum damage and cognitive dysfunction in benign MS Hum. Brain Mapp. 2009 30 2656 2666 10.1002/hbm.20692 19067325 PMC6870794 Mesaros, S. et al. Corpus callosum damage and cognitive dysfunction in benign MS. Hum. Brain Mapp.30, 2656–2666 (2009).10.1002/hbm.20692 PMC6870794 19067325

19.Huang, X. et al. Cognitive impairments associated with corpus callosum infarction: a ten cases study. Int. J. Clin. Exp. Med.8, 21991−21998 (2015).PMC4724017 26885171

20.Ota M Age-related degeneration of corpus callosum measured with diffusion tensor imaging Neuroimage 2006 31 1445 1452 10.1016/j.neuroimage.2006.02.008 16563802 Ota, M. et al. Age-related degeneration of corpus callosum measured with diffusion tensor imaging. Neuroimage31, 1445–1452 (2006).10.1016/j.neuroimage.2006.02.008 16563802

21.Biegon A Human corpus callosum in aging and Alzheimer’s disease: a magnetic resonance imaging study Neurobiol. Aging 1994 15 393 397 10.1016/0197-4580(94)90070-1 7969715 Biegon, A. et al. Human corpus callosum in aging and Alzheimer’s disease: a magnetic resonance imaging study. Neurobiol. Aging15, 393–397 (1994).10.1016/0197-4580(94)90070-1 7969715

22.Craig GA Reflective imaging of myelin integrity in the human and mouse central nervous systems Front. Cell. Neurosci. 2024 18 1408182 10.3389/fncel.2024.1408182 39049821 PMC11266064 Craig, G. A. et al. Reflective imaging of myelin integrity in the human and mouse central nervous systems. Front. Cell. Neurosci.18, 1408182 (2024).10.3389/fncel.2024.1408182 PMC11266064 39049821

23.Jang SY Autophagic myelin destruction by Schwann cells during Wallerian degeneration and segmental demyelination Glia 2016 64 730 742 10.1002/glia.22957 26712109 Jang, S. Y. et al. Autophagic myelin destruction by Schwann cells during Wallerian degeneration and segmental demyelination. Glia64, 730–742 (2016).10.1002/glia.22957 26712109

24.Schäffner E Myelin insulation as a risk factor for axonal degeneration in autoimmune demyelinating disease Nat. Neurosci. 2023 26 1218 1228 10.1038/s41593-023-01366-9 37386131 PMC10322724 Schäffner, E. et al. Myelin insulation as a risk factor for axonal degeneration in autoimmune demyelinating disease. Nat. Neurosci.26, 1218–1228 (2023).10.1038/s41593-023-01366-9 PMC10322724 37386131

25.Yong Y Hunter-Chang S Stepanova E Deppmann C Axonal spheroids in neurodegeneration Mol. Cell. Neurosci. 2021 117 103679 10.1016/j.mcn.2021.103679 34678457 PMC8742877 Yong, Y., Hunter-Chang, S., Stepanova, E. & Deppmann, C. Axonal spheroids in neurodegeneration. Mol. Cell. Neurosci.117, 103679 (2021).10.1016/j.mcn.2021.103679 PMC8742877 34678457

26.Webster H Transient, focal accumulation of axonal mitochondria during the early stages of Wallerian degeneration J. Cell Biol. 1962 12 361 383 10.1083/jcb.12.2.361 14005486 PMC2106025 Webster, H. Transient, focal accumulation of axonal mitochondria during the early stages of Wallerian degeneration. J. Cell Biol.12, 361–383 (1962).10.1083/jcb.12.2.361 PMC2106025 14005486

27.Barrientos SA Axonal degeneration is mediated by the mitochondrial permeability transition pore J. Neurosci. 2011 31 966 978 10.1523/JNEUROSCI.4065-10.2011 21248121 PMC3245862 Barrientos, S. A. et al. Axonal degeneration is mediated by the mitochondrial permeability transition pore. J. Neurosci.31, 966–978 (2011).10.1523/JNEUROSCI.4065-10.2011 PMC3245862 21248121

28.Stahon KE Age-related changes in axonal and mitochondrial ultrastructure and function in white matter J. Neurosci. 2016 36 9990 10001 10.1523/JNEUROSCI.1316-16.2016 27683897 PMC5039264 Stahon, K. E. et al. Age-related changes in axonal and mitochondrial ultrastructure and function in white matter. J. Neurosci.36, 9990–10001 (2016).10.1523/JNEUROSCI.1316-16.2016 PMC5039264 27683897

29.Schultz V Acutely damaged axons are remyelinated in multiple sclerosis and experimental models of demyelination Glia 2017 65 1350 1360 10.1002/glia.23167 28560740 PMC5518437 Schultz, V. et al. Acutely damaged axons are remyelinated in multiple sclerosis and experimental models of demyelination. Glia65, 1350–1360 (2017).10.1002/glia.23167 PMC5518437 28560740

30.Seeker LA Brain matters: unveiling the distinct contributions of region, age, and sex to glia diversity and CNS function Acta Neuropathol. Commun. 2023 11 84 10.1186/s40478-023-01568-z 37217978 PMC10204264 Seeker, L. A. et al. Brain matters: unveiling the distinct contributions of region, age, and sex to glia diversity and CNS function. Acta Neuropathol. Commun.11, 84 (2023).10.1186/s40478-023-01568-z PMC10204264 37217978

31.Jäkel S Altered human oligodendrocyte heterogeneity in multiple sclerosis Nature 2019 566 543 547 10.1038/s41586-019-0903-2 30747918 PMC6544546 Jäkel, S. et al. Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature566, 543–547 (2019).10.1038/s41586-019-0903-2 PMC6544546 30747918

32.Tran MN Single-nucleus transcriptome analysis reveals cell-type-specific molecular signatures across reward circuitry in the human brain Neuron 2021 109 3088 3103 10.1016/j.neuron.2021.09.001 34582785 PMC8564763 Tran, M. N. et al. Single-nucleus transcriptome analysis reveals cell-type-specific molecular signatures across reward circuitry in the human brain. Neuron109, 3088–3103 (2021).10.1016/j.neuron.2021.09.001 PMC8564763 34582785

33.Pandey S Disease-associated oligodendrocyte responses across neurodegenerative diseases Cell Rep. 2022 40 111189 10.1016/j.celrep.2022.111189 36001972 Pandey, S. et al. Disease-associated oligodendrocyte responses across neurodegenerative diseases. Cell Rep.40, 111189 (2022).10.1016/j.celrep.2022.111189 36001972

34.Falcão AM Disease-specific oligodendrocyte lineage cells arise in multiple sclerosis Nat. Med. 2019 24 1837 1844 10.1038/s41591-018-0236-y PMC6544508 30420755 Falcão, A. M. et al. Disease-specific oligodendrocyte lineage cells arise in multiple sclerosis. Nat. Med.24, 1837–1844 (2019).10.1038/s41591-018-0236-y PMC6544508 30420755

35.Kenigsbuch M A shared disease-associated oligodendrocyte signature among multiple CNS pathologies Nat. Neurosci. 2022 25 876 886 10.1038/s41593-022-01104-7 35760863 PMC9724210 Kenigsbuch, M. et al. A shared disease-associated oligodendrocyte signature among multiple CNS pathologies. Nat. Neurosci.25, 876–886 (2022).10.1038/s41593-022-01104-7 PMC9724210 35760863

36.Park H Single-cell RNA-sequencing identifies disease-associated oligodendrocytes in male APP NL-GF and 5XFAD mice Nat. Commun. 2023 14 802 10.1038/s41467-023-36519-8 36781874 PMC9925742 Park, H. et al. Single-cell RNA-sequencing identifies disease-associated oligodendrocytes in male APP NL-GF and 5XFAD mice. Nat. Commun.14, 802 (2023).10.1038/s41467-023-36519-8 PMC9925742 36781874

37.McNamara NB Microglia regulate central nervous system myelin growth and integrity Nature 2023 613 120 129 10.1038/s41586-022-05534-y 36517604 PMC9812791 McNamara, N. B. et al. Microglia regulate central nervous system myelin growth and integrity. Nature613, 120–129 (2023).10.1038/s41586-022-05534-y PMC9812791 36517604

38.Sadick JS Astrocytes and oligodendrocytes undergo subtype-specific transcriptional changes in Alzheimer’s disease Neuron 2022 110 1788 1805 10.1016/j.neuron.2022.03.008 35381189 PMC9167747 Sadick, J. S. et al. Astrocytes and oligodendrocytes undergo subtype-specific transcriptional changes in Alzheimer’s disease. Neuron110, 1788–1805 (2022).10.1016/j.neuron.2022.03.008 PMC9167747 35381189

39.Barba-Reyes JM Oligodendroglia vulnerability in the human dorsal striatum in Parkinson’s disease Acta Neuropathol. 2025 149 46 10.1007/s00401-025-02884-5 40323467 PMC12053221 Barba-Reyes, J. M. et al. Oligodendroglia vulnerability in the human dorsal striatum in Parkinson’s disease. Acta Neuropathol.149, 46 (2025).10.1007/s00401-025-02884-5 PMC12053221 40323467

40.Niu R-Z Feng W-Q Chen L Bao T-H Single-cell transcriptomic profiling reveals regional differences in the prefrontal and entorhinal cortex of Alzheimer’s disease brain Int. J. Mol. Sci. 2025 26 4841 10.3390/ijms26104841 40429980 PMC12112128 Niu, R.-Z., Feng, W.-Q., Chen, L. & Bao, T.-H. Single-cell transcriptomic profiling reveals regional differences in the prefrontal and entorhinal cortex of Alzheimer’s disease brain. Int. J. Mol. Sci.26, 4841 (2025).10.3390/ijms26104841 PMC12112128 40429980

41.Gergues MM Moiseyenko A Saad SZ Kong A-N Wagner GC Nrf2 deletion results in impaired performance in memory tasks and hyperactivity in mature and aged mice Brain Res. 2018 1701 103 111 10.1016/j.brainres.2018.08.033 30194014 PMC8111504 Gergues, M. M., Moiseyenko, A., Saad, S. Z., Kong, A.-N. & Wagner, G. C. Nrf2 deletion results in impaired performance in memory tasks and hyperactivity in mature and aged mice. Brain Res.1701, 103–111 (2018).10.1016/j.brainres.2018.08.033 PMC8111504 30194014

42.Zweig JA Caruso M Brandes MS Gray NE Loss of NRF2 leads to impaired mitochondrial function, decreased synaptic density and exacerbated age-related cognitive deficits Exp. Gerontol. 2020 131 110767 10.1016/j.exger.2019.110767 31843395 PMC6953739 Zweig, J. A., Caruso, M., Brandes, M. S. & Gray, N. E. Loss of NRF2 leads to impaired mitochondrial function, decreased synaptic density and exacerbated age-related cognitive deficits. Exp. Gerontol.131, 110767 (2020).10.1016/j.exger.2019.110767 PMC6953739 31843395

43.Zweig JA Loss of NRF2 accelerates cognitive decline, exacerbates mitochondrial dysfunction, and is required for the cognitive enhancing effects of Centella asiatica during aging Neurobiol. Aging 2021 100 48 58 10.1016/j.neurobiolaging.2020.11.019 33486357 PMC7920997 Zweig, J. A. et al. Loss of NRF2 accelerates cognitive decline, exacerbates mitochondrial dysfunction, and is required for the cognitive enhancing effects of Centella asiatica during aging. Neurobiol. Aging100, 48–58 (2021).10.1016/j.neurobiolaging.2020.11.019 PMC7920997 33486357

44.Bahn G NRF2/ARE pathway negatively regulates BACE1 expression and ameliorates cognitive deficits in mouse Alzheimer’s models Proc. Natl Acad. Sci. USA 2019 116 12516 12523 10.1073/pnas.1819541116 31164420 PMC6589670 Bahn, G. et al. NRF2/ARE pathway negatively regulates BACE1 expression and ameliorates cognitive deficits in mouse Alzheimer’s models. Proc. Natl Acad. Sci. USA116, 12516–12523 (2019).10.1073/pnas.1819541116 PMC6589670 31164420

45.Branca C Genetic reduction of Nrf2 exacerbates cognitive deficits in a mouse model of Alzheimer’s disease Hum. Mol. Genet. 2017 26 4823 4835 10.1093/hmg/ddx361 29036636 PMC5886066 Branca, C. et al. Genetic reduction of Nrf2 exacerbates cognitive deficits in a mouse model of Alzheimer’s disease. Hum. Mol. Genet.26, 4823–4835 (2017).10.1093/hmg/ddx361 PMC5886066 29036636

46.Ren P Nrf2 ablation promotes Alzheimer’s disease-like pathology in APP/PS1 transgenic mice: the role of neuroinflammation and oxidative stress Oxid. Med. Cell. Longev. 2020 2020 3050971 10.1155/2020/3050971 32454936 PMC7238335 Ren, P. et al. Nrf2 ablation promotes Alzheimer’s disease-like pathology in APP/PS1 transgenic mice: the role of neuroinflammation and oxidative stress. Oxid. Med. Cell. Longev.2020, 3050971 (2020).10.1155/2020/3050971 PMC7238335 32454936

47.Rojo AI Deficiency in the transcription factor NRF2 worsens inflammatory parameters in a mouse model with combined tauopathy and amyloidopathy Redox Biol. 2018 18 173 180 10.1016/j.redox.2018.07.006 30029164 PMC6052199 Rojo, A. I. et al. Deficiency in the transcription factor NRF2 worsens inflammatory parameters in a mouse model with combined tauopathy and amyloidopathy. Redox Biol.18, 173–180 (2018).10.1016/j.redox.2018.07.006 PMC6052199 30029164

48.Sigfridsson E Marangoni M Hardingham GE Horsburgh K Fowler JH Deficiency of Nrf2 exacerbates white matter damage and microglia/macrophage levels in a mouse model of vascular cognitive impairment J. Neuroinflammation 2020 17 367 10.1186/s12974-020-02038-2 33261626 PMC7709339 Sigfridsson, E., Marangoni, M., Hardingham, G. E., Horsburgh, K. & Fowler, J. H. Deficiency of Nrf2 exacerbates white matter damage and microglia/macrophage levels in a mouse model of vascular cognitive impairment. J. Neuroinflammation17, 367 (2020).10.1186/s12974-020-02038-2 PMC7709339 33261626

49.Liu L Nrf2 deficiency exacerbates cognitive impairment and reactive microgliosis in a lipopolysaccharide-induced neuroinflammatory mouse model Cell. Mol. Neurobiol. 2020 40 1185 1197 10.1007/s10571-020-00807-4 32170572 PMC11448839 Liu, L. et al. Nrf2 deficiency exacerbates cognitive impairment and reactive microgliosis in a lipopolysaccharide-induced neuroinflammatory mouse model. Cell. Mol. Neurobiol.40, 1185–1197 (2020).10.1007/s10571-020-00807-4 PMC11448839 32170572

50.Sun LO Spatiotemporal control of CNS myelination by oligodendrocyte programmed cell death through the TFEB-PUMA axis Cell 2018 175 1811 1826 10.1016/j.cell.2018.10.044 30503207 PMC6295215 Sun, L. O. et al. Spatiotemporal control of CNS myelination by oligodendrocyte programmed cell death through the TFEB-PUMA axis. Cell175, 1811–1826 (2018).10.1016/j.cell.2018.10.044 PMC6295215 30503207

51.Young KM Oligodendrocyte dynamics in the healthy adult CNS: evidence for myelin remodeling Neuron 2013 77 873 885 10.1016/j.neuron.2013.01.006 23473318 PMC3842597 Young, K. M. et al. Oligodendrocyte dynamics in the healthy adult CNS: evidence for myelin remodeling. Neuron77, 873–885 (2013).10.1016/j.neuron.2013.01.006 PMC3842597 23473318

52.Tripathi RB Remarkable stability of myelinating oligodendrocytes in mice Cell Rep. 2017 21 316 323 10.1016/j.celrep.2017.09.050 29020619 PMC5643547 Tripathi, R. B. et al. Remarkable stability of myelinating oligodendrocytes in mice. Cell Rep.21, 316–323 (2017).10.1016/j.celrep.2017.09.050 PMC5643547 29020619

53.Sánchez CQ Search strategy analysis of 5xFAD Alzheimer mice in the Morris water maze reveals sex-and age-specific spatial navigation deficits Biomedicines 2023 11 599 10.3390/biomedicines11020599 36831135 PMC9953202 Sánchez, C. Q. et al. Search strategy analysis of 5xFAD Alzheimer mice in the Morris water maze reveals sex-and age-specific spatial navigation deficits. Biomedicines11, 599 (2023).10.3390/biomedicines11020599 PMC9953202 36831135

54.Drakesmith M Estimating axon conduction velocity in vivo from microstructural MRI Neuroimage 2019 203 116186 10.1016/j.neuroimage.2019.116186 31542512 PMC6854468 Drakesmith, M. et al. Estimating axon conduction velocity in vivo from microstructural MRI. Neuroimage203, 116186 (2019).10.1016/j.neuroimage.2019.116186 PMC6854468 31542512

55.Sampaio-Baptista C Motor skill learning induces changes in white matter microstructure and myelination J. Neurosci. 2013 33 19499 19503 10.1523/JNEUROSCI.3048-13.2013 24336716 PMC3858622 Sampaio-Baptista, C. et al. Motor skill learning induces changes in white matter microstructure and myelination. J. Neurosci.33, 19499–19503 (2013).10.1523/JNEUROSCI.3048-13.2013 PMC3858622 24336716

56.Geraghty AC Loss of adaptive myelination contributes to methotrexate chemotherapy-related cognitive impairment Neuron 2019 103 250 265 10.1016/j.neuron.2019.04.032 31122677 PMC6697075 Geraghty, A. C. et al. Loss of adaptive myelination contributes to methotrexate chemotherapy-related cognitive impairment. Neuron103, 250–265 (2019).10.1016/j.neuron.2019.04.032 PMC6697075 31122677

57.Fernández-Castañeda A Mild respiratory COVID can cause multi-lineage neural cell and myelin dysregulation Cell 2022 185 2452 2468 10.1016/j.cell.2022.06.008 35768006 PMC9189143 Fernández-Castañeda, A. et al. Mild respiratory COVID can cause multi-lineage neural cell and myelin dysregulation. Cell185, 2452–2468 (2022).10.1016/j.cell.2022.06.008 PMC9189143 35768006

58.van den Bosch AM Ultrastructural axon–myelin unit alterations in multiple sclerosis correlate with inflammation Ann. Neurol. 2023 93 856 870 10.1002/ana.26585 36565265 van den Bosch, A. M. et al. Ultrastructural axon–myelin unit alterations in multiple sclerosis correlate with inflammation. Ann. Neurol.93, 856–870 (2023).10.1002/ana.26585 36565265

59.Peters A The effects of normal aging on myelinated nerve fibers in monkey central nervous system Front. Neuroanat. 2009 3 657 10.3389/neuro.05.011.2009 PMC2713738 19636385 Peters, A. The effects of normal aging on myelinated nerve fibers in monkey central nervous system. Front. Neuroanat.3, 657 (2009).10.3389/neuro.05.011.2009 PMC2713738 19636385

60.Ferreira S Oligodendrogenesis increases in hippocampal grey and white matter prior to locomotor or memory impairment in an adult mouse model of tauopathy Eur. J. Neurosci. 2021 54 5762 5784 10.1111/ejn.14726 32181929 PMC8451881 Ferreira, S. et al. Oligodendrogenesis increases in hippocampal grey and white matter prior to locomotor or memory impairment in an adult mouse model of tauopathy. Eur. J. Neurosci.54, 5762–5784 (2021).10.1111/ejn.14726 PMC8451881 32181929

61.Wu Y Alterations of myelin morphology and oligodendrocyte development in early stage of Alzheimer’s disease mouse model Neurosci. Lett. 2017 642 102 106 10.1016/j.neulet.2017.02.007 28174059 Wu, Y. et al. Alterations of myelin morphology and oligodendrocyte development in early stage of Alzheimer’s disease mouse model. Neurosci. Lett.642, 102–106 (2017).10.1016/j.neulet.2017.02.007 28174059

62.Hu X Bace1 modulates myelination in the central and peripheral nervous system Nat. Neurosci. 2006 9 1520 1525 10.1038/nn1797 17099708 Hu, X. et al. Bace1 modulates myelination in the central and peripheral nervous system. Nat. Neurosci.9, 1520–1525 (2006).10.1038/nn1797 17099708

63.Groh J Microglia-mediated demyelination protects against CD8+ T cell-driven axon degeneration in mice carrying PLP defects Nat. Commun. 2023 14 6911 10.1038/s41467-023-42570-2 37903797 PMC10616105 Groh, J. et al. Microglia-mediated demyelination protects against CD8+ T cell-driven axon degeneration in mice carrying PLP defects. Nat. Commun.14, 6911 (2023).10.1038/s41467-023-42570-2 PMC10616105 37903797

64.Marner L Nyengaard JR Tang Y Pakkenberg B Marked loss of myelinated nerve fibers in the human brain with age J. Comp. Neurol. 2003 462 144 152 10.1002/cne.10714 12794739 Marner, L., Nyengaard, J. R., Tang, Y. & Pakkenberg, B. Marked loss of myelinated nerve fibers in the human brain with age. J. Comp. Neurol.462, 144–152 (2003).10.1002/cne.10714 12794739

65.Hou J Pakkenberg B Age-related degeneration of corpus callosum in the 90+ years measured with stereology Neurobiol. Aging 2012 33 1009.e1 9 10.1016/j.neurobiolaging.2011.10.017 22118947 Hou, J. & Pakkenberg, B. Age-related degeneration of corpus callosum in the 90+ years measured with stereology. Neurobiol. Aging33, 1009.e1–9 (2012).10.1016/j.neurobiolaging.2011.10.017 22118947

66.Tang Y Nyengaard JR Pakkenberg B Gundersen HJG Age-induced white matter changes in the human brain: a stereological investigation Neurobiol. Aging 1997 18 609 615 10.1016/S0197-4580(97)00155-3 9461058 Tang, Y., Nyengaard, J. R., Pakkenberg, B. & Gundersen, H. J. G. Age-induced white matter changes in the human brain: a stereological investigation. Neurobiol. Aging18, 609–615 (1997).10.1016/s0197-4580(97)00155-3 9461058

67.Aber ER Oligodendroglial macroautophagy is essential for myelin sheath turnover to prevent neurodegeneration and death Cell Rep. 2022 41 111480 10.1016/j.celrep.2022.111480 36261002 PMC9639605 Aber, E. R. et al. Oligodendroglial macroautophagy is essential for myelin sheath turnover to prevent neurodegeneration and death. Cell Rep.41, 111480 (2022).10.1016/j.celrep.2022.111480 PMC9639605 36261002

68.Ktena N Autophagic degradation of CNS myelin maintains axon integrity Cell Stress 2022 6 93 107 10.15698/cst2022.12.274 36478958 PMC9707329 Ktena, N. et al. Autophagic degradation of CNS myelin maintains axon integrity. Cell Stress6, 93–107 (2022).10.15698/cst2022.12.274 PMC9707329 36478958

69.Ktena, N. et al. Disruption of oligodendroglial autophagy leads to myelin morphological deficits, neuronal apoptosis, and cognitive decline in aged mice. Glia73, 1383−1397 (2025).10.1002/glia.70012 PMC12121467 40105013

70.Dhaliwal N Dhaliwal J Singh A Chopra K Dimethyl fumarate attenuates 2-VO-induced vascular dementia via activating the Nrf2 signaling pathway in rats Inflammopharmacology 2021 29 537 547 10.1007/s10787-020-00785-5 33459879 Dhaliwal, N., Dhaliwal, J., Singh, A. & Chopra, K. Dimethyl fumarate attenuates 2-VO-induced vascular dementia via activating the Nrf2 signaling pathway in rats. Inflammopharmacology29, 537–547 (2021).10.1007/s10787-020-00785-5 33459879

71.Liu Y Dimethylfumarate alleviates early brain injury and secondary cognitive deficits after experimental subarachnoid hemorrhage via activation of Keap1-Nrf2-ARE system J. Neurosurg. 2015 123 915 923 10.3171/2014.11.JNS132348 25614941 Liu, Y. et al. Dimethylfumarate alleviates early brain injury and secondary cognitive deficits after experimental subarachnoid hemorrhage via activation of Keap1-Nrf2-ARE system. J. Neurosurg.123, 915–923 (2015).10.3171/2014.11.JNS132348 25614941

72.Wang T Dimethyl fumarate improves cognitive impairment and neuroinflammation in mice with Alzheimer’s disease J. Neuroinflammation 2024 21 55 10.1186/s12974-024-03046-2 38383481 PMC10882778 Wang, T. et al. Dimethyl fumarate improves cognitive impairment and neuroinflammation in mice with Alzheimer’s disease. J. Neuroinflammation21, 55 (2024).10.1186/s12974-024-03046-2 PMC10882778 38383481

73.Yan N Xu Z Qu C Zhang J Dimethyl fumarate improves cognitive deficits in chronic cerebral hypoperfusion rats by alleviating inflammation, oxidative stress, and ferroptosis via NRF2/ARE/NF-κB signal pathway Int. Immunopharmacol. 2021 98 107844 10.1016/j.intimp.2021.107844 34153667 Yan, N., Xu, Z., Qu, C. & Zhang, J. Dimethyl fumarate improves cognitive deficits in chronic cerebral hypoperfusion rats by alleviating inflammation, oxidative stress, and ferroptosis via NRF2/ARE/NF-κB signal pathway. Int. Immunopharmacol.98, 107844 (2021).10.1016/j.intimp.2021.107844 34153667

74.Amato MP Effects of 2-year treatment with dimethyl fumarate on cognition and functional impairment in patients with relapsing remitting multiple sclerosis Neurol. Sci. 2020 41 3185 3193 10.1007/s10072-020-04320-w 32358701 Amato, M. P. et al. Effects of 2-year treatment with dimethyl fumarate on cognition and functional impairment in patients with relapsing remitting multiple sclerosis. Neurol. Sci.41, 3185–3193 (2020).10.1007/s10072-020-04320-w 32358701

75.Yeung MSY Dynamics of oligodendrocyte generation and myelination in the human brain Cell 2014 159 766 774 10.1016/j.cell.2014.10.011 25417154 Yeung, M. S. Y. et al. Dynamics of oligodendrocyte generation and myelination in the human brain. Cell159, 766–774 (2014).10.1016/j.cell.2014.10.011 25417154

76.Chen J-F Enhancing myelin renewal reverses cognitive dysfunction in a murine model of Alzheimer’s disease Neuron 2021 109 2292 2307 10.1016/j.neuron.2021.05.012 34102111 PMC8298291 Chen, J.-F. et al. Enhancing myelin renewal reverses cognitive dysfunction in a murine model of Alzheimer’s disease. Neuron109, 2292–2307 (2021).10.1016/j.neuron.2021.05.012 PMC8298291 34102111

77.Rajani RM Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease PLoS Biol. 2024 22 e3002727 10.1371/journal.pbio.3002727 39042667 PMC11265669 Rajani, R. M. et al. Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease. PLoS Biol.22, e3002727 (2024).10.1371/journal.pbio.3002727 PMC11265669 39042667

78.Holloway RK Localized microglia dysregulation impairs central nervous system myelination in development Acta Neuropathol. Commun. 2023 11 49 10.1186/s40478-023-01543-8 36949514 PMC10035254 Holloway, R. K. et al. Localized microglia dysregulation impairs central nervous system myelination in development. Acta Neuropathol. Commun.11, 49 (2023).10.1186/s40478-023-01543-8 PMC10035254 36949514

79.Fan W NRF2 activation ameliorates blood–brain barrier injury after cerebral ischemic stroke by regulating ferroptosis and inflammation Sci. Rep. 2024 14 5300 10.1038/s41598-024-53836-0 38438409 PMC10912757 Fan, W. et al. NRF2 activation ameliorates blood–brain barrier injury after cerebral ischemic stroke by regulating ferroptosis and inflammation. Sci. Rep.14, 5300 (2024).10.1038/s41598-024-53836-0 PMC10912757 38438409

80.van Horssen J Nrf2 and DJ1 are consistently upregulated in inflammatory multiple sclerosis lesions Free Radic. Biol. Med. 2010 49 1283 1289 10.1016/j.freeradbiomed.2010.07.013 20673799 van Horssen, J. et al. Nrf2 and DJ1 are consistently upregulated in inflammatory multiple sclerosis lesions. Free Radic. Biol. Med.49, 1283–1289 (2010).10.1016/j.freeradbiomed.2010.07.013 20673799

81.Rajani RM Reversal of endothelial dysfunction reduces white matter vulnerability in cerebral small vessel disease in rats Sci. Transl. Med. 2018 10 eaam9507 10.1126/scitranslmed.aam9507 29973407 Rajani, R. M. et al. Reversal of endothelial dysfunction reduces white matter vulnerability in cerebral small vessel disease in rats. Sci. Transl. Med.10, eaam9507 (2018).10.1126/scitranslmed.aam9507 29973407

82.Han B Integrating spatial and single-cell transcriptomics to characterize the molecular and cellular architecture of the ischemic mouse brain Sci. Transl. Med. 2024 16 eadg1323 10.1126/scitranslmed.adg1323 38324639 Han, B. et al. Integrating spatial and single-cell transcriptomics to characterize the molecular and cellular architecture of the ischemic mouse brain. Sci. Transl. Med.16, eadg1323 (2024).10.1126/scitranslmed.adg1323 38324639

83.Gibson EM Neuronal activity promotes oligodendrogenesis and adaptive myelination in the mammalian brain Science 2014 344 1252304 10.1126/science.1252304 24727982 PMC4096908 Gibson, E. M. et al. Neuronal activity promotes oligodendrogenesis and adaptive myelination in the mammalian brain. Science344, 1252304 (2014).10.1126/science.1252304 PMC4096908 24727982

84.Bacmeister CM Motor learning drives dynamic patterns of intermittent myelination on learning-activated axons Nat. Neurosci. 2022 25 1300 1313 10.1038/s41593-022-01169-4 36180791 PMC9651929 Bacmeister, C. M. et al. Motor learning drives dynamic patterns of intermittent myelination on learning-activated axons. Nat. Neurosci.25, 1300–1313 (2022).10.1038/s41593-022-01169-4 PMC9651929 36180791

85.Zullo JM Regulation of lifespan by neural excitation and REST Nature 2019 574 359 364 10.1038/s41586-019-1647-8 31619788 PMC6893853 Zullo, J. M. et al. Regulation of lifespan by neural excitation and REST. Nature574, 359–364 (2019).10.1038/s41586-019-1647-8 PMC6893853 31619788

86.Knowles JK Batra A Xu H Monje M Adaptive and maladaptive myelination in health and disease Nat. Rev. Neurol. 2022 18 735 746 10.1038/s41582-022-00737-3 36376595 Knowles, J. K., Batra, A., Xu, H. & Monje, M. Adaptive and maladaptive myelination in health and disease. Nat. Rev. Neurol.18, 735–746 (2022).10.1038/s41582-022-00737-3 36376595

87.Gutchess AH Aging and the neural correlates of successful picture encoding: frontal activations compensate for decreased medial-temporal activity J. Cogn. Neurosci. 2005 17 84 96 10.1162/0898929052880048 15701241 Gutchess, A. H. et al. Aging and the neural correlates of successful picture encoding: frontal activations compensate for decreased medial-temporal activity. J. Cogn. Neurosci.17, 84–96 (2005).10.1162/0898929052880048 15701241

88.Davis SW Dennis NA Daselaar SM Fleck MS Cabeza R Que PASA? The posterior–anterior shift in aging Cereb. Cortex 2008 18 1201 1209 10.1093/cercor/bhm155 17925295 PMC2760260 Davis, S. W., Dennis, N. A., Daselaar, S. M., Fleck, M. S. & Cabeza, R. Que PASA? The posterior–anterior shift in aging. Cereb. Cortex18, 1201–1209 (2008).10.1093/cercor/bhm155 PMC2760260 17925295

89.Angel L Fay S Bouazzaoui B Isingrini M Two hemispheres for better memory in old age: role of executive functioning J. Cogn. Neurosci. 2011 23 3767 3777 10.1162/jocn_a_00104 21812559 Angel, L., Fay, S., Bouazzaoui, B. & Isingrini, M. Two hemispheres for better memory in old age: role of executive functioning. J. Cogn. Neurosci.23, 3767–3777 (2011).10.1162/jocn_a_00104 21812559

90.Deary IJ Pattie A Starr JM The stability of intelligence from age 11 to age 90 years: the Lothian Birth Cohort of 1921 Psychol. Sci. 2013 24 2361 2368 10.1177/0956797613486487 24084038 Deary, I. J., Pattie, A. & Starr, J. M. The stability of intelligence from age 11 to age 90 years: the Lothian Birth Cohort of 1921. Psychol. Sci.24, 2361–2368 (2013).10.1177/0956797613486487 24084038

91.Henstridge CM Post-mortem brain analyses of the Lothian Birth Cohort 1936: extending lifetime cognitive and brain phenotyping to the level of the synapse Acta Neuropathol. Commun. 2015 3 53 10.1186/s40478-015-0232-0 26335101 PMC4559320 Henstridge, C. M. et al. Post-mortem brain analyses of the Lothian Birth Cohort 1936: extending lifetime cognitive and brain phenotyping to the level of the synapse. Acta Neuropathol. Commun.3, 53 (2015).10.1186/s40478-015-0232-0 PMC4559320 26335101

92.Kay KR Studying synapses in human brain with array tomography and electron microscopy Nat. Protoc. 2013 8 1366 1380 10.1038/nprot.2013.078 23787894 PMC3712649 Kay, K. R. et al. Studying synapses in human brain with array tomography and electron microscopy. Nat. Protoc.8, 1366–1380 (2013).10.1038/nprot.2013.078 PMC3712649 23787894

93.Liewald D Miller R Logothetis N Wagner H-J Schüz A Distribution of axon diameters in cortical white matter: an electron-microscopic study on three human brains and a macaque Biol. Cybern. 2014 108 541 557 10.1007/s00422-014-0626-2 25142940 PMC4228120 Liewald, D., Miller, R., Logothetis, N., Wagner, H.-J. & Schüz, A. Distribution of axon diameters in cortical white matter: an electron-microscopic study on three human brains and a macaque. Biol. Cybern.108, 541–557 (2014).10.1007/s00422-014-0626-2 PMC4228120 25142940

94.Osorio D Cai JJ Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control Bioinformatics 2021 37 963 967 10.1093/bioinformatics/btaa751 32840568 PMC8599307 Osorio, D. & Cai, J. J. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control. Bioinformatics37, 963–967 (2021).10.1093/bioinformatics/btaa751 PMC8599307 32840568

95.Marques S Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system Science 2016 352 1326 1329 10.1126/science.aaf6463 27284195 PMC5221728 Marques, S. et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science352, 1326–1329 (2016).10.1126/science.aaf6463 PMC5221728 27284195

96.Doerflinger NH Macklin WB Popko B Inducible site-specific recombination in myelinating cells Genesis 2003 35 63 72 10.1002/gene.10154 12481300 Doerflinger, N. H., Macklin, W. B. & Popko, B. Inducible site-specific recombination in myelinating cells. Genesis35, 63–72 (2003).10.1002/gene.10154 12481300

97.Reddy NM Potteti HR Mariani TJ Biswal S Reddy SP Conditional deletion of Nrf2 in airway epithelium exacerbates acute lung injury and impairs the resolution of inflammation Am. J. Respir. Cell Mol. Biol. 2011 45 1161 1168 10.1165/rcmb.2011-0144OC 21659655 PMC3262666 Reddy, N. M., Potteti, H. R., Mariani, T. J., Biswal, S. & Reddy, S. P. Conditional deletion of Nrf2 in airway epithelium exacerbates acute lung injury and impairs the resolution of inflammation. Am. J. Respir. Cell Mol. Biol.45, 1161–1168 (2011).10.1165/rcmb.2011-0144OC PMC3262666 21659655