Increased white matter fibre dispersion and lower IQ scores in adults born preterm.

Preterm birth has been associated with altered microstructural properties of the white matter and lower cognitive ability in childhood and adulthood. Due to methodological limitations of the diffusion tensor model, it is not clear whether alterations in myelination or variation in fibre orientation are driving these differences. Novel models applied to multi-shell diffusion imaging have been used to disentangle these effects, but to date this has not been used to study the preterm brain in adulthood. This study investigated whether novel advanced diffusion MRI metrics such as microscopic anisotropy and orientation dispersion are altered in adults born preterm, and whether this was associated with cognitive performance. Seventy-two preterm born participants (<37 weeks gestational age) were recruited from a 1982-1984 cohort (33 males, mean age 33.5 ± 1.0 years). Seventy-two term born (>37 weeks gestational age) controls (34 males, mean age 30.9 ± 4.0 years) were recruited from the general population. Tensor FA was calculated with FSL, while microscopic FA and orientation dispersion entropy (ODE) were estimated using the Spherical Mean Technique (SMT). Estimated Full Scale IQ (FSIQ), Verbal Comprehension Index (VCI) and Perceptual Reasoning Index (PRI) were obtained from the WASI-II (abbreviated) IQ test. Voxel-wise comparisons using FSL's tract-based spatial statistics were performed to test between-group differences in diffusion MRI metrics as well as within-group associations of diffusion MRI metrics and IQ outcomes. The preterm group had significantly lower FSIQ, VCI and PRI scores. Preterm subjects demonstrated widespread decreases in ODE reflecting increased fibre dispersion, but no differences in microscopic FA. Tensor FA was increased in a small area in the anterior corona radiata. Lower FA values in the preterm population were associated with lower FSIQ and PRI scores. An increase in fibre dispersion in white matter and lower IQ scores after preterm birth exist in adulthood. Advanced diffusion MRI metrics such as the orientation dispersion entropy can be used to monitor white matter alterations across the lifespan in preterm born individuals. Although not significantly different between preterm and term groups, tensor FA values in the preterm group were associated with cognitive outcome.

Fibre orientation atlas guided rapid segmentation of white matter tracts.

Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.

The incidence of movement disorder increases with age and contrasts with subtle and limited neuroimaging abnormalities in argininosuccinic aciduria.

Argininosuccinate lyase (ASL) is integral to the urea cycle detoxifying neurotoxic ammonia and the nitric oxide (NO) biosynthesis cycle. Inherited ASL deficiency causes argininosuccinic aciduria (ASA), a rare disease with hyperammonemia and NO deficiency. Patients present with developmental delay, epilepsy and movement disorder, associated with NO-mediated downregulation of central catecholamine biosynthesis. A neurodegenerative phenotype has been proposed in ASA. To better characterise this neurodegenerative phenotype in ASA, we conducted a retrospective study in six paediatric and adult metabolic centres in the UK in 2022. We identified 60 patients and specifically looked for neurodegeneration-related symptoms: movement disorder such as ataxia, tremor and dystonia, hypotonia/fatigue and abnormal behaviour. We analysed neuroimaging with diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) in an individual with ASA with movement disorders. We assessed conventional and DTI MRI alongside single photon emission computer tomography (SPECT) with dopamine analogue radionuclide 123 I-ioflupane, in Asl-deficient mice treated by hASL mRNA with normalised ureagenesis. Movement disorders in ASA appear in the second and third decades of life, becoming more prevalent with ageing and independent from the age of onset of hyperammonemia. Neuroimaging can show abnormal DTI features affecting both grey and white matter, preferentially basal ganglia. ASA mouse model with normalised ureagenesis did not recapitulate these DTI findings and showed normal 123 I-ioflupane SPECT and cerebral dopamine metabolomics. Altogether these findings support the pathophysiology of a late-onset movement disorder with cell-autonomous functional central catecholamine dysregulation but without or limited neurodegeneration of dopaminergic neurons, making these symptoms amenable to targeted therapy.

SENSE EPI reconstruction with 2D phase error correction and channel-wise noise removal.

PURPOSE: To develop a robust reconstruction pipeline for EPI data that enables 2D Nyquist phase error correction using sensitivity encoding without incurring major noise artifacts in low SNR data. METHODS: SENSE with 2D phase error correction (PEC-SENSE) was combined with channel-wise noise removal using Marcenko-Pastur principal component analysis (MPPCA) to simultaneously eliminate Nyquist ghost artifacts in EPI data and mitigate the noise amplification associated with phase correction using parallel imaging. The proposed pipeline (coined SPECTRE) was validated in phantom DW-EPI data using the accuracy and precision of diffusion metrics; ground truth values were obtained from data acquired with a spin echo readout. Results from the SPECTRE pipeline were compared against PEC-SENSE reconstructions with three alternate denoising strategies: (i) no denoising; (ii) denoising of magnitude data after image formation; (iii) denoising of complex data after image formation. SPECTRE was then tested using high b $$ b $$ -value (i.e., low SNR) diffusion data (up to b = 3000 $$ b=3000 $$ s/mm 2 $$ {}^2 $$ ) in four healthy subjects. RESULTS: Noise amplification associated with phase error correction incurred a 23% bias in phantom mean diffusivity (MD) measurements. Phantom MD estimates using the SPECTRE pipeline were within 8% of the ground truth value. In healthy volunteers, the SPECTRE pipeline visibly corrected Nyquist ghost artifacts and reduced associated noise amplification in high b $$ b $$ -value data. CONCLUSION: The proposed reconstruction pipeline is effective in correcting low SNR data, and improves the accuracy and precision of derived diffusion metrics.

Human milk feeding and cognitive outcome in preterm infants: the role of infection and NEC reduction.

BACKGROUND: Breast milk has been associated with lower risk of infection and necrotising enterocolitis (NEC) and improved long-term cognitive outcomes in preterm infants but, if unsupplemented, does not meet the nutritional requirements of preterm infants. METHODS: Preterm infants were randomised to receive a high nutrient intervention diet: preterm formula (PTF) or the standard diet: term formula (TF) or banked donor breast milk (BBM), either as their sole diet or as supplement to maternal breast milk (MBM). IQ tests were performed at ages 7, 15, 20, and 30 years. RESULTS: An increase in MBM and BBM intake was associated with a lower chance of neonatal infection/NEC. Neonatal infection/NEC was associated with lower Full Scale IQ (FSIQ) and Performance IQ (PIQ) score at ages 7 and 30 years. The relationship between higher intake of MBM and PIQ at age 7 years was partly mediated by neonatal infection/NEC. The intervention diet was associated with higher Verbal IQ (VIQ) scores compared to the standard diet. There was no evidence that these effects changed from childhood through to adulthood. CONCLUSIONS: Neonatal diet is an important modifiable factor that can affect long-term cognitive outcome through a 'human milk' factor, protecting against infection/NEC, and a 'nutrient content' factor. IMPACT: This is the first study to demonstrate the effects of neonatal infection/necrotising enterocolitis (NEC) on IQ in the same cohort in childhood and adulthood. Diet can be a key factor in long-term cognitive outcome in people born preterm by preventing neonatal infection/NEC and providing adequate nutrients. Human milk, whether MBM or BBM, is associated with a reduced risk of infection/NEC. A higher nutrient diet is associated with better cognitive outcome in childhood. Performance IQ is particularly vulnerable to the effects of infection/NEC and verbal IQ to the quantity of (macro)nutrients in the diet.

Neuroconductor: an R platform for medical imaging analysis.

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.

A multi-shell multi-tissue diffusion study of brain connectivity in early multiple sclerosis.

BACKGROUND: The potential of multi-shell diffusion imaging to produce accurate brain connectivity metrics able to unravel key pathophysiological processes in multiple sclerosis (MS) has scarcely been investigated. OBJECTIVE: To test, in patients with a clinically isolated syndrome (CIS), whether multi-shell imaging-derived connectivity metrics can differentiate patients from controls, correlate with clinical measures, and perform better than metrics obtained with conventional single-shell protocols. METHODS: Nineteen patients within 3 months from the CIS and 12 healthy controls underwent anatomical and 53-direction multi-shell diffusion-weighted 3T images. Patients were cognitively assessed. Voxel-wise fibre orientation distribution functions were estimated and used to obtain network metrics. These were also calculated using a conventional single-shell diffusion protocol. Through linear regression, we obtained effect sizes and standardised regression coefficients. RESULTS: Patients had lower mean nodal strength (p = 0.003) and greater network modularity than controls (p = 0.045). Greater modularity was associated with worse cognitive performance in patients, even after accounting for lesion load (p = 0.002). Multi-shell-derived metrics outperformed single-shell-derived ones. CONCLUSION: Connectivity-based nodal strength and network modularity are abnormal in the CIS. Furthermore, the increased network modularity observed in patients, indicating microstructural damage, is clinically relevant. Connectivity analyses based on multi-shell imaging can detect potentially relevant network changes in early MS.

Tractography-based parcellation does not provide strong evidence of anatomical organisation within the thalamus.

Connectivity-based parcellation of subcortical structures using diffusion tractography is now a common paradigm in neuroscience. These analyses often imply voxel-level specificity of connectivity, and the formation of compact, spatially coherent clusters is often taken as strong imaging-based evidence for anatomically distinct subnuclei in an individual. In this study, we demonstrate that internal structure in diffusion anisotropy is not necessary for a plausible parcellation to be obtained, by spatially permuting diffusion parameters within the thalami and repeating the parcellation. Moreover, we show that, in a winner-takes-all paradigm, most voxels receive the same label before and after this shuffling process-a finding that is stable across image acquisitions and tractography algorithms. We therefore suggest that such parcellations should be interpreted with caution.

Structural network disruption markers explain disability in multiple sclerosis.

OBJECTIVE: To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions. METHODS: This cross-sectional study included 51 healthy controls and 122 patients comprising 58 relapsing-remitting, 28 primary progressive and 36 secondary progressive. Structural brain networks were reconstructed from diffusion-weighted MRIs and standard metrics reflecting network density, efficiency and clustering coefficient were derived and compared between subjects' groups. Stepwise linear regression analyses were used to investigate the contribution of network measures that explain clinical disability (Expanded Disability Status Scale (EDSS)) and information processing speed (Symbol Digit Modalities Test (SDMT)) compared with conventional MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT. RESULTS: Compared with controls, network efficiency and clustering coefficient were reduced in MS while these measures were also reduced in secondary progressive relative to relapsing-remitting patients. Structural network metrics increase the variance explained by the statistical models for clinical and information processing dysfunction. The best model for EDSS showed that reduced network density and global efficiency and increased age were associated with increased clinical disability. The best model for SDMT showed that lower deep grey matter volume, reduced efficiency and male gender were associated with worse information processing speed. CONCLUSIONS: Structural topological changes exist between subjects' groups. Network density and global efficiency explained disability above non-network measures, highlighting that network metrics can provide clinically relevant information about MS pathology.

Structural connectivity of the amygdala in young adults with autism spectrum disorder.

Autism spectrum disorder (ASD) is characterized by impairments in social cognition, a function associated with the amygdala. Subdivisions of the amygdala have been identified which show specificity of structure, connectivity, and function. Little is known about amygdala connectivity in ASD. The aim of this study was to investigate the microstructural properties of amygdala-cortical connections and their association with ASD behaviours, and whether connectivity of specific amygdala subregions is associated with particular ASD traits. The brains of 51 high-functioning young adults (25 with ASD; 26 controls) were scanned using MRI. Amygdala volume was measured, and amygdala-cortical connectivity estimated using probabilistic tractography. An iterative 'winner takes all' algorithm was used to parcellate the amygdala based on its primary cortical connections. Measures of amygdala connectivity were correlated with clinical scores. In comparison with controls, amygdala volume was greater in ASD (F(1,94) = 4.19; p = .04). In white matter (WM) tracts connecting the right amygdala to the right cortex, ASD subjects showed increased mean diffusivity (t = 2.35; p = .05), which correlated with the severity of emotion recognition deficits (rho = -0.53; p = .01). Following amygdala parcellation, in ASD subjects reduced fractional anisotropy in WM connecting the left amygdala to the temporal cortex was associated with with greater attention switching impairment (rho = -0.61; p = .02). This study demonstrates that both amygdala volume and the microstructure of connections between the amygdala and the cortex are altered in ASD. Findings indicate that the microstructure of right amygdala WM tracts are associated with overall ASD severity, but that investigation of amygdala subregions can identify more specific associations.

Microstructural parameter estimation in vivo using diffusion MRI and structured prior information.

PURPOSE: Diffusion MRI has recently been used with detailed models to probe tissue microstructure. Much of this work has been performed ex vivo with powerful scanner hardware, to gain sensitivity to parameters such as axon radius. By contrast, performing microstructure imaging on clinical scanners is extremely challenging. METHODS: We use an optimized dual spin-echo diffusion protocol, and a Bayesian fitting approach, to obtain reproducible contrast (histogram overlap of up to 92%) in estimated maps of axon radius index in healthy adults at a modest, widely-available gradient strength (35 mT m(-1)). A key innovation is the use of influential priors. RESULTS: We demonstrate that our priors can improve precision in axon radius estimates--a 7-fold reduction in voxelwise coefficient of variation in vivo--without significant bias. Our results may reflect true axon radius differences between white matter regions, but this interpretation should be treated with caution due to the complexity of the tissue relative to our model. CONCLUSIONS: Some sensitivity to relatively large axons (3-15 µm) may be available at clinical field and gradient strengths. Future applications at higher gradient strength will benefit from the favorable eddy current properties of the dual spin-echo sequence, and greater precision available with suitable priors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.

Intelligence quotient in paediatric sickle cell disease: a systematic review and meta-analysis.

AIM: Sickle cell disease (SCD) is the commonest cause of childhood stroke worldwide. Magnetic resonance imaging (MRI) is routinely used to detect additional silent cerebral infarction (SCI), as IQ is lower in SCI as well as stroke. This review assesses the effect of infarction on IQ, and specifically whether, compared to healthy controls, IQ differences are seen in children with SCI with no apparent MRI abnormality. METHOD: A systematic review was conducted to include articles with an SCD paediatric population, MRI information, and Wechsler IQ. A meta-analysis of 19 articles was performed to compare IQ in three groups: stroke vs SCI; SCI vs no SCI; and no SCI vs healthy controls. RESULTS: Mean differences in IQ between all three groups were significant: stroke patients had lower IQ than patients with SCI by 10 points (six studies); patients with SCI had lower IQ than no patients with SCI by 6 points (17 studies); and no patients with SCI had lower IQ than healthy controls by 7 points (seven studies). INTERPRETATION: Children with SCD and no apparent MRI abnormality have significantly lower IQ than healthy controls. In this chronic condition, other biological, socioeconomic, and environmental factors must play a significant role in cognition.

White Matter Damage Relates to Oxygen Saturation in Children With Sickle Cell Anemia Without Silent Cerebral Infarcts.

BACKGROUND AND PURPOSE: Sickle cell anemia is associated with compromised oxygen-carrying capability of hemoglobin and a high incidence of overt and silent stroke. However, in children with no evidence of cerebral infarction, there are changes in brain morphometry relative to healthy controls, which may be related to chronic anemia and oxygen desaturation. METHODS: A whole-brain tract-based spatial statistics analysis was carried out in 25 children with sickle cell anemia with no evidence of abnormality on T2-weighted magnetic resonance imaging (13 male, age range: 8-18 years) and 14 age- and race-matched controls (7 male, age range: 10-19 years) to determine the extent of white matter injury. The hypotheses that white matter damage is related to daytime peripheral oxygen saturation and steady-state hemoglobin were tested. RESULTS: Fractional anisotropy was found to be significantly lower in patients in the subcortical white matter (corticospinal tract and cerebellum), whereas mean diffusivity and radial diffusivity were higher in patients in widespread areas. There was a significant negative relationship between radial diffusivity and oxygen saturation (P<0.05) in the anterior corpus callosum and a trend-level negative relationship between radial diffusivity and hemoglobin (P<0.1) in the midbody of the corpus callosum. CONCLUSIONS: These data show widespread white matter abnormalities in a sample of asymptomatic children with sickle cell anemia, and provides for the first time direct evidence of a relationship between brain microstructure and markers of disease severity (eg, peripheral oxygen saturation and steady-state hemoglobin). This study suggests that diffusion tensor imaging metrics may serve as a biomarker for future trials of reducing hypoxic exposure.

Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain.

The purpose of this work was to assess the reproducibility of diffusion imaging, and in particular the apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM) parameters and diffusion tensor imaging (DTI) parameters, across multiple centres using clinically available protocols with limited harmonization between sequences. An ice-water phantom and nine healthy volunteers were scanned across fives centres on eight scanners (four Siemens 1.5T, four Philips 3T). The mean ADC, IVIM parameters (diffusion coefficient D and perfusion fraction f) and DTI parameters (mean diffusivity MD and fractional anisotropy FA), were measured in grey matter, white matter and specific brain sub-regions. A mixed effect model was used to measure the intra- and inter-scanner coefficient of variation (CV) for each of the five parameters. ADC, D, MD and FA had a good intra- and inter-scanner reproducibility in both grey and white matter, with a CV ranging between 1% and 7.4%; mean 2.6%. Other brain regions also showed high levels of reproducibility except for small structures such as the choroid plexus. The IVIM parameter f had a higher intra-scanner CV of 8.4% and inter-scanner CV of 24.8%. No major difference in the inter-scanner CV for ADC, D, MD and FA was observed when analysing the 1.5T and 3T scanners separately. ADC, D, MD and FA all showed good intra-scanner reproducibility, with the inter-scanner reproducibility being comparable or faring slightly worse, suggesting that using data from multiple scanners does not have an adverse effect compared with using data from the same scanner. The IVIM parameter f had a poorer inter-scanner CV when scanners of different field strengths were combined, and the parameter was also affected by the scan acquisition resolution. This study shows that the majority of diffusion MRI derived parameters are robust across 1.5T and 3T scanners and suitable for use in multi-centre clinical studies and trials.

Functional brain connectivity in children with focal epilepsy: A systematic review of functional MRI studies.

Epilepsy is increasingly recognised as a brain network disorder and many studies have investigated functional connectivity (FC) in children with epilepsy using functional MRI (fMRI). This systematic review of fMRI studies, published up to November 2023, investigated profiles of FC changes and their clinical relevance in children with focal epilepsy compared to healthy controls. A literature search in PubMed and Web of Science yielded 62 articles. We categorised the results into three groups: 1) differences in correlation-based FC between patients and controls; 2) differences in other FC measures between patients and controls; and 3) associations between FC and disease variables (for example, age of onset), cognitive and seizure outcomes. Studies revealed either increased or decreased FC across multiple brain regions in children with focal epilepsy. However, findings lacked consistency: conflicting FC alterations (decreased and increased FC) co-existed within or between brain regions across all focal epilepsy groups. The studies demonstrated overall that 1) interhemispheric connections often displayed abnormal connectivity and 2) connectivity within and between canonical functional networks was decreased, particularly for the default mode network. Focal epilepsy disrupted FC in children both locally (e.g., seizure-onset zones, or within-brain subnetworks) and globally (e.g., whole-brain network architecture). The wide variety of FC study methodologies limits clinical application of the results. Future research should employ longitudinal designs to understand the evolution of brain networks during the disease course and explore the potential of FC biomarkers for predicting cognitive and postsurgical seizure outcomes.

Functional brain network organisation of children between 2 and 5 years derived from reconstructed activity of cortical sources of high-density EEG recordings.

There is increasing interest in applying connectivity analysis to brain measures (Rubinov and Sporns, 2010), but most studies have relied on fMRI, which substantially limits the participant groups and numbers that can be studied. High-density EEG recordings offer a comparatively inexpensive easy-to-use alternative, but require channel-level connectivity analysis which currently lacks a common analytic framework and is very limited in spatial resolution. To address this problem, we have developed a new technique for studies of network development that overcomes the spatial constraint and obtains functional networks of cortical areas by using EEG source reconstruction with age-matched average MRI templates (He et al., 1999). In contrast to previously reported channel-level analysis, this approach provides information about the cortical areas most likely to be involved in the network as well as their functional relationship (Babiloni et al., 2005; De Vico Fallani et al., 2007). In this study, we applied source reconstruction with age-matched templates to task-free high-density EEG recordings in typically-developing children between 2 and 6 years of age (O'Reilly, 2012). Graph theory was then applied to the association strengths of 68 cortical regions of interest based on the Desikan-Killiany atlas. We found linear increases of mean node degree, mean clustering coefficient and maximum betweenness centrality between 2 years and 6 years of age. Characteristic path length was negatively correlated with age. The correlation of the network measures with age indicates network development towards more closely integrated networks similar to reports from other imaging modalities (Fair et al., 2008; Power et al., 2010). We also applied eigenvalue decomposition to obtain functional modules (Clayden et al., 2013). Connection strength within these modules did not change with age, and the modules resembled hub networks previously described for MRI (Hagmann et al., 2010; Power et al., 2010). The high temporal resolution of EEG additionally allowed us to distinguish between frequency bands potentially reflecting dynamic coupling between different neural oscillators. Generally, network parameters were similar for networks based on different frequency bands, but frequency band did emerge as a significant factor for clustering coefficient and characteristic path length. In conclusion, the current analysis shows that source reconstruction of high-density EEG recordings with appropriate head models offers a valuable tool for estimating network parameters in studies of brain development. The findings replicate the pattern of closer functional integration over development described for other imaging modalities (Fair et al., 2008; Power et al., 2010).

Subcortical and cerebellar volumetric deficits in paediatric sickle cell anaemia.

Sickle cell anaemia (SCA) is associated with silent cerebral infarction (SCI), affecting white and cortical grey matter, but there are few data on subcortical volumes. We analysed retrospective magnetic resonance imaging (MRI) data in 26 SCA patients and 20 controls, comparing mean subcortical volumes between three groups: controls, SCA with SCI (n = 13) and SCA without visible abnormality (n = 13). Specific volumetric differences were found in the hippocampus, amygdala, pallidum, caudate, putamen, thalamus, and cerebellum. This is the first study to demonstrate subcortical volume change in SCA, with the most severe volumetric deficits occurring in children with SCI seen on MRI.

Effect of growth hormone deficiency on brain structure, motor function and cognition.

The growth hormone-insulin-like growth factor-1 axis plays a role in normal brain growth but little is known of the effect of growth hormone deficiency on brain structure. Children with isolated growth hormone deficiency (peak growth hormone <6.7 µg/l) and idiopathic short stature (peak growth hormone >10 µg/l) underwent cognitive assessment, diffusion tensor imaging and volumetric magnetic resonance imaging prior to commencing growth hormone treatment. Total brain, corpus callosal, hippocampal, thalamic and basal ganglia volumes were determined using Freesurfer. Fractional anisotropy (a marker of white matter structural integrity) images were aligned and tract-based spatial statistics performed. Fifteen children (mean 8.8 years of age) with isolated growth hormone deficiency [peak growth hormone <6.7 µg/l (mean 3.5 µg/l)] and 14 controls (mean 8.4 years of age) with idiopathic short stature [peak growth hormone >10 µg/l (mean 15 µg/l) and normal growth rate] were recruited. Compared with controls, children with isolated growth hormone deficiency had lower Full-Scale IQ (P < 0.01), Verbal Comprehension Index (P < 0.01), Processing Speed Index (P < 0.05) and Movement-Assessment Battery for Children (P < 0.008) scores. Verbal Comprehension Index scores correlated significantly with insulin-like growth factor-1 (P < 0.03) and insulin-like growth factor binding protein-3 (P < 0.02) standard deviation scores in isolated growth hormone deficiency. The splenium of the corpus callosum, left globus pallidum, thalamus and hippocampus (P < 0.01) were significantly smaller; and corticospinal tract (bilaterally; P < 0.045, P < 0.05) and corpus callosum (P < 0.05) fractional anisotropy were significantly lower in the isolated growth hormone deficiency group. Basal ganglia volumes and bilateral corticospinal tract fractional anisotropy correlated significantly with Movement-Assessment Battery for Children scores, and corpus callosum fractional anisotropy with Full-Scale IQ and Processing Speed Index. In patients with isolated growth hormone deficiency, white matter abnormalities in the corpus callosum and corticospinal tract, and reduced thalamic and globus pallidum volumes relate to deficits in cognitive function and motor performance. Follow-up studies that investigate the course of the structural and cognitive deficits on growth hormone treatment are now required to confirm that growth hormone deficiency impacts significantly on brain structure, cognitive function and motor performance.

Normative development of white matter tracts: similarities and differences in relation to age, gender, and intelligence.

The white matter of the brain undergoes a range of structural changes throughout development; from conception to birth, in infancy, and onwards through childhood and adolescence. Several studies have used diffusion magnetic resonance imaging (dMRI) to investigate these changes, but a consensus has not yet emerged on which white matter tracts undergo changes in the later stages of development or what the most important driving factors are behind these changes. In this study of typically developing 8- to 16-year-old children, we use a comprehensive data-driven approach based on principal components analysis to identify effects of age, gender, and brain volume on dMRI parameters, as well as their relative importance. We also show that secondary components of these parameters predict full-scale IQ, independently of the age- and gender-related effects. This overarching assessment of the common factors and gender differences in normal white matter tract development will help to advance understanding of this process in late childhood and adolescence.