Functional network modules overlap and are linked to interindividual connectome differences during human brain development.

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

White matter and latency of visual evoked potentials during maturation: A miniature pig model of adolescent development.

BACKGROUND: Continuous myelination of cerebral white matter (WM) during adolescence overlaps with the formation of higher cognitive skills and the onset of many neuropsychiatric disorders. We developed a miniature-pig model of adolescent brain development for neuroimaging and neurophysiological assessment during this critical period. Minipigs have gyroencephalic brains with a large cerebral WM compartment and a well-defined adolescence period. METHODS: Eight Sinclair™ minipigs (Sus scrofa domestica) were evaluated four times during weeks 14-28 (40, 28 and 28 days apart) of adolescence using monocular visual stimulation (1 Hz)-evoked potentials and diffusion MRI (dMRI) of WM. The latency for the pre-positive 30 ms (PP30), positive 30 ms (P30) and negative 50 ms (N50) components of the flash visual evoked potentials (fVEPs) and their interhemispheric latency (IL) were recorded in the frontal, central and occipital areas during ten 60-second stimulations for each eye. The dMRI imaging protocol consisted of fifteen b-shells (b = 0-3500 s/mm2) with 32 directions/shell, providing measurements that included fractional anisotropy (FA), radial kurtosis, kurtosis anisotropy (KA), axonal water fraction (AWF), and the permeability-diffusivity index (PDI). RESULTS: Significant reductions (p < 0.05) in the latency and IL of fVEP measurements paralleled significant rises in FA, KA, AWF and PDI over the same period. The longitudinal latency changes in fVEPs were primarily associated with whole-brain changes in diffusion parameters, while fVEP IL changes were related to maturation of the corpus callosum. CONCLUSIONS: Good agreement between reduction in the latency of fVEPs and maturation of cerebral WM was interpreted as evidence for ongoing myelination and confirmation of the minipig as a viable research platform. Adolescent development in minipigs can be studied using human neuroimaging and neurophysiological protocols and followed up with more invasive assays to investigate key neurodevelopmental hypotheses in psychiatry.

Comparative analysis of brain age prediction using structural and diffusion MRIs in neonates.

Using machine learning techniques to predict brain age from multimodal data has become a crucial biomarker for assessing brain development. Among various types of brain imaging data, structural magnetic resonance imaging (sMRI) and diffusion magnetic resonance imaging (dMRI) are the most commonly used modalities. sMRI focuses on depicting macrostructural features of the brain, while dMRI reveals the orientation of major white matter fibers and changes in tissue microstructure. However, their differential capabilities in reflecting newborn age and clinical implications have not been systematically studied. This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA) images, we found their mean absolute errors (MAE) in predicting infant age to be similar. Exploratory analysis revealed for T2w images, areas such as the cerebral cortex and ventricles contribute most significantly to age prediction, whereas FA images highlight the cerebral cortex and regions of the main white matter tracts. Despite both modalities focusing on the cerebral cortex, they exhibit significant region-wise differences, reflecting developmental disparities in macro- and microstructural aspects of the cortex. Additionally, we examined the effects of prematurity, gender, and hemispherical asymmetry of the brain on age prediction for both modalities. Results showed significant differences (p<0.05) in age prediction biases based on FA images across gender and hemispherical asymmetry, whereas no significant differences were observed with T2w images. This study underscores the differences between T2w and FA images in predicting infant brain age, offering new perspectives for studying infant brain development and aiding more effective assessment and tracking of infant development.

Functional and structural maturation of auditory cortex from 2 months to 2 years old.

BACKGROUND: In school-age children, the myelination of the auditory radiation thalamocortical pathway is associated with the latency of auditory evoked responses, with the myelination of thalamocortical axons facilitating the rapid propagation of acoustic information. Little is known regarding this auditory system function-structure association in infants and toddlers. METHODS AND PARTICIPANTS: The present study tested the hypothesis that maturation of auditory radiation white-matter microstructure (e.g., fractional anisotropy (FA); measured using diffusion-weighted MRI) is associated with the latency of the infant auditory response (the P2m response, measured using magnetoencephalography, MEG) in a cross-sectional (N = 47, 2 to 24 months, 19 females) as well as longitudinal cohort (N = 18, 2 to 29 months, 8 females) of typically developing infants and toddlers. Of 18 longitudinal infants, 2 infants had data from 3 timepoints and 16 infants had data from 2 timepoints. RESULTS: In the cross-sectional sample, non-linear maturation of P2m latency and auditory radiation diffusion measures were observed. Auditory radiation diffusion accounted for significant variance in P2m latency, even after removing the variance associated with age in both P2m latency and auditory radiation diffusion measures. In the longitudinal sample, latency and FA associations could be observed at the level of a single child. CONCLUSIONS: Findings provide strong support for the hypothesis that an increase in thalamocortical neural conduction velocity, due to increased axon diameter and/or myelin maturation, contributes to a decrease in the infant P2m auditory evoked response latency. SIGNIFICANCE: Infant multimodal brain imaging identifies brain mechanisms contributing to the rapid changes in neural circuit activity during the first two years of life.

High-resolution diffusion magnetic resonance imaging and spatial-transcriptomic in developing mouse brain.

Brain development is a highly complex process regulated by numerous genes at the molecular and cellular levels. Brain tissue exhibits serial microstructural changes during the development process. High-resolution diffusion magnetic resonance imaging (dMRI) affords a unique opportunity to probe these changes in the developing brain non-destructively. In this study, we acquired multi-shell dMRI datasets at 32 µm isotropic resolution to investigate the tissue microstructure alterations, which we believe to be the highest spatial resolution dMRI datasets obtained for postnatal mouse brains. We adapted the Allen Developing Mouse Brain Atlas (ADMBA) to integrate quantitative MRI metrics and spatial transcriptomics. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI) metrics were used to quantify brain development at different postnatal days. We demonstrated that the differential evolutions of fiber orientation distributions contribute to the distinct development patterns in white matter (WM) and gray matter (GM). Furthermore, the genes enriched in the nervous system that regulate brain structure and function were expressed in spatial correlation with age-matched dMRI. This study is the first one providing high-resolution dMRI, including DTI, DKI, and NODDI models, to trace mouse brain microstructural changes in WM and GM during postnatal development. This study also highlighted the genotype-phenotype correlation of spatial transcriptomics and dMRI, which may improve our understanding of brain microstructure changes at the molecular level.

Development of Myelin Growth Charts of the White Matter Using T1 Relaxometry.

BACKGROUND AND PURPOSE: Myelin maturation occurs in late fetal life to early adulthood, with the most rapid changes observed in the first few years of infancy. To quantify the degree of myelination, a specific MR imaging sequence is required to measure the changes in tissue proton relaxivity (R1). R1 positively correlates with the degree of myelination maturation at a given age. Similar to head circumference charts, these data can be used to develop normal growth charts for specific white matter tracts to detect pathologies involving abnormal myelination. MATERIALS AND METHODS: This is a cross-sectional study using normal clinical pediatric brain MR images with the MP2RAGE sequence to generate T1 maps. The T1 maps were segmented to 75 brain regions from a brain atlas (white matter and gyri). Statistical modeling for all subjects across regions and the age range was computed, and estimates of population-level percentile ranking were computed to describe the effective myelination rate as a function of age. Test-retest analysis was performed to assess reproducibility. Logistic trendline and regression were performed for selected white matter regions and plotted for growth charts. RESULTS: After exclusion for abnormal MR imaging or diseases affecting myelination from the electronic medical record, 103 subject MR images were included, ranging from birth to 17 years of age. Test-retest analysis resulted in a high correlation for white matter (r = 0.88) and gyri (r = 0.95). All white matter regions from the atlas had significant P values for logistic regression with R 2 values ranging from 0.41 to 0.99. CONCLUSIONS: These data can serve as a myelination growth chart to permit patient comparisons with normal levels with respect to age and brain regions, thus improving detection of developmental disorders affecting myelin.

Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels.

Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas-based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age-specific 1- and 2-year-old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high-quality DTI and structural MRI data were obtained from 50 healthy children to build up three-dimensional age-specific 1- and 2-year-old brain templates and atlases. Age-specific templates include a single-subject template as well as two population-averaged templates from linear and nonlinear transformation, respectively. Each age-specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age-specific atlases. These established 1- and 2-year-old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.

Affinity of structural white matter tracts between infant and adult pig.

BACKGROUND: The piglet brain has been increasingly used as an excellent surrogate for investigation of pediatric neurodevelopment, nutrition, and traumatic brain injuries. This study intends to establish a piglet brain's structural connectivity model and compare it with the adult pig, enhancing its application for structurally guided functional analysis. METHODS: In this study, diffusion-weighted (DW)-MRI data from piglets (n=11, 3-week-old) was used to establish piglet model and compare with adult pigs. We employed a data-driven independent component analysis (ICA) method to derive piglet-specific tracts. Pearson correlations and Kullback-Leibler (KL) divergences was employed to identify common tracts and unique tracts for piglet. Common tracts were then used in a blueprint connectome study to highlight differences in regions of interest (ROI). RESULTS: The data-driven approach applied to piglet brains revealed 17 common tracts, showing high similarity with adult pigs' white matter (WM) tracts, and identified 3 tracts unique to piglets and 10 negative marker tracts. Additionally, the study highlighted notable differences in 3 ROIs associated with blueprint connectome. COMPARING WITH EXISTING METHODS: This study marks a significant shift from surface-based to voxel-based methodologies in analyzing pig brain structural connectivity and generating connectome blueprints. Additionally, it sheds light on the use of the piglet model for developmental studies, offering new perspectives in this area. CONCLUSION: This study established a piglet brain tract model and conducts a comparative analysis of adult pig's and piglet's structural connectivity. These findings underscore the potential use of the piglet brain model in employing piglet model for developmental studies.

Differences in educational opportunity predict white matter development.

Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual's environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual's educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come.

Lateralization of dorsal fiber tract targeting Broca's area concurs with language skills during development.

Language is bounded to the left hemisphere in the adult brain and the functional lateralization can already be observed early during development. Here we investigate whether this is paralleled by a lateralization of the white matter structural language network. We analyze the strength and microstructural properties of language-related fiber tracts connecting temporal and frontal cortices with a separation of two dorsal tracts, one targeting the posterior Broca's area (BA44) and one targeting the precentral gyrus (BA6). In a large sample of young children (3-6 years), we demonstrate that, in contrast to the BA6-targeting tract, the microstructural asymmetry of the BA44-targeting fiber tract significantly correlates locally with different aspects of development. While the asymmetry in its anterior segment reflects age, the asymmetry in its posterior segment is associated with the children's language skills. These findings demonstrate a fine-grained structure-to-function mapping in the lateralized network and go beyond our current view of language-related human brain maturation.

Normative volumes and relaxation times at 3T during brain development.

While research has unveiled and quantified brain markers of abnormal neurodevelopment, clinicians still work with qualitative metrics for MRI brain investigation. The purpose of the current article is to bridge the knowledge gap between case-control cohort studies and individual patient care. Here, we provide a unique dataset of seventy-three 3-to-17 years-old healthy subjects acquired with a 6-minute MRI protocol encompassing T1 and T2 relaxation quantitative sequence that can be readily implemented in the clinical setting; MP2RAGE for T1 mapping and the prototype sequence GRAPPATINI for T2 mapping. White matter and grey matter volumes were automatically quantified. We further provide normative developmental curves based on these two imaging sequences; T1, T2 and volume normative ranges with respect to age were computed, for each ROI of a pediatric brain atlas. This open-source dataset provides normative values allowing to position individual patients acquired with the same protocol on the brain maturation curve and as such provides potentially useful quantitative biomarkers facilitating precise and personalized care.

Longitudinal brain atlases of early developing cynomolgus macaques from birth to 48 months of age.

Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development.

Mapping white matter maturational processes and degrees on neonates by diffusion kurtosis imaging with multiparametric analysis.

White matter maturation has been characterized by diffusion tensor (DT) metrics. However, maturational processes and degrees are not fully investigated due to limitations of univariate approaches and limited specificity/sensitivity. Diffusion kurtosis imaging (DKI) provides kurtosis tensor (KT) and white matter tract integrity (WMTI) metrics, besides DT metrics. Therefore, we tried to investigate performances of DKI with the multiparametric analysis in characterizing white matter maturation. Developmental changes in metrics were investigated by using tract-based spatial statistics and the region of interest analysis on 50 neonates with postmenstrual age (PMA) from 37.43 to 43.57 weeks. Changes in metrics were combined into various patterns to reveal different maturational processes. Mahalanobis distance based on DT metrics (DM,DT ) and that combing DT and KT metrics (DM,DT-KT ) were computed, separately. Performances of DM,DT-KT and DM,DT were compared in revealing correlations with PMA and the neurobehavioral score. Compared with DT metrics, WMTI metrics demonstrated additional changing patterns. Furthermore, variations of DM,DT-KT across regions were in agreement with the maturational sequence. Additionally, DM,DT-KT demonstrated stronger negative correlations with PMA and the neurobehavioral score in more regions than DM,DT . Results suggest that DKI with the multiparametric analysis benefits the understanding of white matter maturational processes and degrees on neonates.

Early life factors and white matter microstructure in children with overweight and obesity: The ActiveBrains project.

BACKGROUND & AIMS: Exposure to a suboptimal environment during the fetal and early infancy period's results in long-term consequences for brain morphology and function. We investigated the associations of early life factors such as anthropometric neonatal data (i.e., birth length, birth weight and birth head circumference) and breastfeeding practices (i.e., exclusive and any breastfeeding) with white matter (WM) microstructure, and ii) we tested whether WM tracts related to early life factors are associated with academic performance in children with overweight/obesity. METHODS: 96 overweight/obese children (10.03 ± 1.16 years; 38.7% girls) were included from the ActiveBrains Project. WM microstructure indicators used were fractional anisotropy (FA) and mean diffusivity (MD), derived from Diffusion Tensor Imaging. Academic performance was evaluated with the Battery III Woodcock-Muñoz Tests of Achievement. Regression models were used to examine the associations of the early life factors with tract-specific FA and MD, as well as its association with academic performance. RESULTS: Head circumference at birth was positively associated with FA of the inferior fronto-occipital fasciculus tract (0.441; p = 0.005), as well as negatively associated with MD of the cingulate gyrus part of cingulum (-0.470; p = 0.006), corticospinal (-0.457; p = 0.005) and superior thalamic radiation tract (-0.476; p = 0.001). Association of birth weight, birth length and exclusive breastfeeding with WM microstructure did not remain significant after false discovery rate correction. None tract related to birth head circumference was associated with academic performance (all p > 0.05). CONCLUSIONS: Our results highlighted the importance of the perinatal growth in WM microstructure later in life, although its possible academic implications remain inconclusive.

Early motor outcomes in infants with critical congenital heart disease are related to neonatal brain development and brain injury.

AIM: To assess the relationship between neonatal brain development and injury with early motor outcomes in infants with critical congenital heart disease (CCHD). METHOD: Neonatal brain magnetic resonance imaging was performed after open-heart surgery with cardiopulmonary bypass. Cortical grey matter (CGM), unmyelinated white matter, and cerebellar volumes, as well as white matter motor tract fractional anisotropy and mean diffusivity were assessed. White matter injury (WMI) and arterial ischaemic stroke (AIS) with corticospinal tract (CST) involvement were scored. Associations with motor outcomes at 3, 9, and 18 months were corrected for repeated cardiac surgery. RESULTS: Fifty-one infants (31 males, 20 females) were included prospectively. Median age at neonatal surgery and postoperative brain magnetic resonance imaging was 7 days (interquartile range [IQR] 5-11d) and 15 days (IQR 12-21d) respectively. Smaller CGM and cerebellar volumes were associated with lower fine motor scores at 9 months (CGM regression coefficient=0.51, 95% confidence interval [CI]=0.15-0.86; cerebellum regression coefficient=3.08, 95% CI=1.07-5.09) and 18 months (cerebellum regression coefficient=2.08, 95% CI=0.47-5.12). The fractional anisotropy and mean diffusivity of white matter motor tracts were not related with motor scores. WMI was related to lower gross motor scores at 9 months (mean difference -0.8SD, 95% CI=-1.5 to -0.2). AIS with CST involvement increased the risk of gross motor problems and muscle tone abnormalities. Cerebral palsy (n=3) was preceded by severe ischaemic brain injury. INTERPRETATION: Neonatal brain development and injury are associated with fewer favourable early motor outcomes in infants with CCHD.

Nutritional Intake, White Matter Integrity, and Neurodevelopment in Extremely Preterm Born Infants.

BACKGROUND: Determining optimal nutritional regimens in extremely preterm infants remains challenging. This study aimed to evaluate the effect of a new nutritional regimen and individual macronutrient intake on white matter integrity and neurodevelopmental outcome. METHODS: Two retrospective cohorts of extremely preterm infants (gestational age < 28 weeks) were included. Cohort B (n = 79) received a new nutritional regimen, with more rapidly increased, higher protein intake compared to cohort A (n = 99). Individual protein, lipid, and caloric intakes were calculated for the first 28 postnatal days. Diffusion tensor imaging was performed at term-equivalent age, and cognitive and motor development were evaluated at 2 years corrected age (CA) (Bayley-III-NL) and 5.9 years chronological age (WPPSI-III-NL, MABC-2-NL). RESULTS: Compared to cohort A, infants in cohort B had significantly higher protein intake (3.4 g/kg/day vs. 2.7 g/kg/day) and higher fractional anisotropy (FA) in several white matter tracts but lower motor scores at 2 years CA (mean (SD) 103 (12) vs. 109 (12)). Higher protein intake was associated with higher FA and lower motor scores at 2 years CA (B = -6.7, p = 0.001). However, motor scores at 2 years CA were still within the normal range and differences were not sustained at 5.9 years. There were no significant associations with lipid or caloric intake. CONCLUSION: In extremely preterm born infants, postnatal protein intake seems important for white matter development but does not necessarily improve long-term cognitive and motor development.

Corticospinal neuron subpopulation-specific developmental genes prospectively indicate mature segmentally specific axon projection targeting.

For precise motor control, distinct subpopulations of corticospinal neurons (CSN) must extend axons to distinct spinal segments, from proximal targets in the brainstem and cervical cord to distal targets in thoracic and lumbar spinal segments. We find that developing CSN subpopulations exhibit striking axon targeting specificity in spinal white matter, which establishes the foundation for durable specificity of adult corticospinal circuitry. Employing developmental retrograde and anterograde labeling, and their distinct neocortical locations, we purified developing CSN subpopulations using fluorescence-activated cell sorting to identify genes differentially expressed between bulbar-cervical and thoracolumbar-projecting CSN subpopulations at critical developmental times. These segmentally distinct CSN subpopulations are molecularly distinct from the earliest stages of axon extension, enabling prospective identification even before eventual axon targeting decisions are evident in the spinal cord. This molecular delineation extends beyond simple spatial separation of these subpopulations in the cortex. Together, these results identify candidate molecular controls over segmentally specific corticospinal axon projection targeting.

Development of the visual white matter pathways mediates development of electrophysiological responses in visual cortex.

The latency of neural responses in the visual cortex changes systematically across the lifespan. Here, we test the hypothesis that development of visual white matter pathways mediates maturational changes in the latency of visual signals. Thirty-eight children participated in a cross-sectional study including diffusion magnetic resonance imaging (MRI) and magnetoencephalography (MEG) sessions. During the MEG acquisition, participants performed a lexical decision and a fixation task on words presented at varying levels of contrast and noise. For all stimuli and tasks, early evoked fields were observed around 100 ms after stimulus onset (M100), with slower and lower amplitude responses for low as compared to high contrast stimuli. The optic radiations and optic tracts were identified in each individual's brain based on diffusion MRI tractography. The diffusion properties of the optic radiations predicted M100 responses, especially for high contrast stimuli. Higher optic radiation fractional anisotropy (FA) values were associated with faster and larger M100 responses. Over this developmental window, the M100 responses to high contrast stimuli became faster with age and the optic radiation FA mediated this effect. These findings suggest that the maturation of the optic radiations over childhood accounts for individual variations observed in the developmental trajectory of visual cortex responses.

Brain Atrophy and White Matter Damage Linked to Peripheral Bioenergetic Deficits in the Neurodegenerative Disease FXTAS.

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a neurodegenerative disorder affecting subjects (premutation carriers) with a 55-200 CGG-trinucleotide expansion in the 5'UTR of the fragile X mental retardation 1 gene (FMR1) typically after age 50. As both the presence of white matter hyperintensities (WMHs) and atrophied gray matter on magnetic resonance imaging (MRI) are linked to age-dependent decline in cognition, here we tested whether MRI outcomes (WMH volume (WMHV) and brain volume) were correlated with mitochondrial bioenergetics from peripheral blood monocytic cells in 87 carriers with and without FXTAS. As a parameter assessing cumulative damage, WMHV was correlated to both FXTAS stages and age, and brain volume discriminated between carriers and non-carriers. Similarly, mitochondrial mass and ATP production showed an age-dependent decline across all participants, but in contrast to WMHV, only FADH2-linked ATP production was significantly reduced in carriers vs. non-carriers. In carriers, WMHV negatively correlated with ATP production sustained by glucose-glutamine and FADH2-linked substrates, whereas brain volume was positively associated with the latter and mitochondrial mass. The observed correlations between peripheral mitochondrial bioenergetics and MRI findings-and the lack of correlations with FXTAS diagnosis/stages-may stem from early brain bioenergetic deficits even before overt FXTAS symptoms and/or imaging findings.