Comparison of Two Methods for Automatic Brain Morphometry Analysis

The methods of computational neuroanatomy are widely used; the data on their individual strengths and limitations from direct comparisons are, however, scarce. The aim of the present study was direct comparison of DBM based on high-resolution spatial transforms with widely used VBM analysis based on segmented high-resolution images. We performed DBM and VBM analyses on simulated volume changes in a set of 20 3-D MR images, compared to 30 MR images, where only random spatial transforms were introduced. The ability of the two methods to detect regions with the simulated volume changes was determined using overlay index together with the ground truth regions of the simulations; the precision of the detection in space was determined using the distance measures between the centers of detected and simulated regions. DBM was able to detect all the regions with simulated local volume changes with high spatial precision. On the other hand, VBM detected only changes in vicinity of the largest simulated change, with a poor overlap of the detected changes and the ground truth. Taken together we suggest that the analysis of high-resolution deformation fields is more convenient, sensitive, and precise than voxel-wise analysis of tissue-segmented images

TIPIT: A randomised controlled trial of thyroxine in preterm infants under 28 weeks gestation: Magnetic Resonance Imaging and Magnetic Resonance Angiography protocol

<p>Abstract </p> <p>Background</p> <p>Infants born at extreme prematurity are at high risk of developmental disability. A major risk factor for disability is having a low level of thyroid hormone described as hypothyroxinaemia, which is recognised to be a frequent phenomenon in these infants. Derangements of critical thyroid function during the sensitive window in prematurity when early development occurs, may have a range of long term effects for brain development. Further research in preterm infants using neuroimaging techniques will increase our understanding of the specificity of the effects of hypothyroxinaemia on the developing foetal brain. This is an explanatory double blinded randomised controlled trial which is aimed to assess the effect of thyroid hormone supplementation on brain size, key brain structures, extent of myelination, white matter integrity and vessel morphology, somatic growth and the hypothalamic-pituitary-adrenal axis.</p> <p>Methods</p> <p>The study is a multi-centred double blinded randomised controlled trial of thyroid hormone supplementation in babies born below 28 weeks' gestation. All infants will receive either levothyroxine or placebo until 32 weeks corrected gestational age. The primary outcomes will be width of the sub-arachnoid space measured using cranial ultrasound and head circumference at 36 weeks corrected gestational age. The secondary outcomes will be thyroid hormone concentrations, the hypothalamic pituitary axis status and auxological data between birth and expected date of delivery; thyroid gland volume, brain size, volumes of key brain structures, extent of myelination and brain vessel morphology at expected date of delivery and markers of morbidity which include duration of mechanical ventilation and/or oxygen requirement and chronic lung disease.</p> <p><b>Trial registration</b></p> <p>Current Controlled Trials ISRCTN89493983</p

Brain Structural Networks Associated with Intelligence and Visuomotor Ability

Increasing evidence indicates that multiple structures in the brain are associated with intelligence and cognitive function at the network level. The association between the grey matter (GM) structural network and intelligence and cognition is not well understood. We applied a multivariate approach to identify the pattern of GM and link the structural network to intelligence and cognitive functions. Structural magnetic resonance imaging was acquired from 92 healthy individuals. Source-based morphometry analysis was applied to the imaging data to extract GM structural covariance. We assessed the intelligence, verbal fluency, processing speed, and executive functioning of the participants and further investigated the correlations of the GM structural networks with intelligence and cognitive functions. Six GM structural networks were identified. The cerebello-parietal component and the frontal component were significantly associated with intelligence. The parietal and frontal regions were each distinctively associated with intelligence by maintaining structural networks with the cerebellum and the temporal region, respectively. The cerebellar component was associated with visuomotor ability. Our results support the parieto-frontal integration theory of intelligence by demonstrating how each core region for intelligence works in concert with other regions. In addition, we revealed how the cerebellum is associated with intelligence and cognitive functions

Brain Structural Networks Associated with Intelligence and Visuomotor Ability

Increasing evidence indicates that multiple structures in the brain are associated with intelligence and cognitive function at the network level. The association between the grey matter (GM) structural network and intelligence and cognition is not well understood. We applied a multivariate approach to identify the pattern of GM and link the structural network to intelligence and cognitive functions. Structural magnetic resonance imaging was acquired from 92 healthy individuals. Source-based morphometry analysis was applied to the imaging data to extract GM structural covariance. We assessed the intelligence, verbal fluency, processing speed, and executive functioning of the participants and further investigated the correlations of the GM structural networks with intelligence and cognitive functions. Six GM structural networks were identified. The cerebello-parietal component and the frontal component were significantly associated with intelligence. The parietal and frontal regions were each distinctively associated with intelligence by maintaining structural networks with the cerebellum and the temporal region, respectively. The cerebellar component was associated with visuomotor ability. Our results support the parieto-frontal integration theory of intelligence by demonstrating how each core region for intelligence works in concert with other regions. In addition, we revealed how the cerebellum is associated with intelligence and cognitive functions

Hippocampus size predicts fluid intelligence in musically trained people

Introduction Neurogenesis persists in the human adult hippocampus1 and the survival of new progenitor cells is enhanced by learning activities2. Using the musician's brain as a model for cortical plasticity, musical training induced functional adaptations of the hippocampus have been demonstrated3,4. Furthermore, there is evidence for a positive correlation between hippocampus size and fluid intelligence5, encompassing aspects of attention, working memory and executive functions6. Previous data strongly suggest that musical training impacts on such higher order cognitive functions7,14. Following these findings we hypothesize a linkage between hippocampus size and fluid intelligence in musically trained people. Methods Participants: Three groups - piano experts (E, n=20), piano amateurs (A, n=20) and nonmusicians (N, n=19), matched by age and gender. Task: short version of the Raven's Test, Advanced Progressive Matrices (time limit 15 minutes). Structural MRI: manual segmentation8,9,10,11,12 of left (LH) and right (RH) hippocampi done by a single investigator blinded for group belonging and ID of each subject, software MRIcroN13 (Fig. 1) Statistics: one-way ANOVAs on Raven performance and hippocampus volume; Fisher's r to z transformations; robust multiple regression models for each hemisphere: (i) to predict Raven performance by hippocampus volume and (ii) to test whether this prediction is modulated by the factor of musical training. Robust regression analysis (implemented by statistical software R) represents a valid alternative to least square regression analysis when data is potentially contaminated by single influential observations. Results One way ANOVAs with three levels of expertise: no main effects of Expertise neither in Raven's Test performance nor in hippocampus volumes. No main effect of Lateralization (Fig. 2). Pooling of musicians (M=A+E) justified as no difference in predictive power exists between A and E, neither in the left nor in the right hemisphere. LH: z=0.84, p=0.401, RH: z=-0.45, p=0.623. Robust multiple regression analysis testing the prediction of Raven's performance by hippocampus size, modulated by musicianship (two levels: N, M(A+E)): - Left hemisphere: Significant interaction (t=2.221, p=.030), revealing that prediction of Raven's performance by hippocampus size is modulated by musical training: N (beta =.03) and M (beta =.46). - Right hemisphere: Significant interaction (t=2.003, p=.050), revealing that prediction of Raven's performance by hippocampus size is modulated by musical training: N (beta =.01) and M (beta =.38). Conclusion Hippocampus size significantly predicts fluid intelligence performance in musically experienced subjects but not in musically naïve ones. This result represents a striking additional corroboration of musicians' brain plasticity. It seems highly plausible that a longlasting complex activity like musical instrumental training from childhood into adulthood induced an increase in hippocampus size associated with enhanced logical reasoning. Further research is needed to investigate cognitive functions favored by musical training and possible consequent impact on the development of peculiar brain structures. NB: This research was performed within the framework of an ongoing research project performed by Clara James (principal investigator) and postdoc collaborator Mathias Oechslin entitled "Behavioral, neuro-functional and neuro-anatomical correlates of experience dependant music perception" (FNS 100014_125050). This research project investigates brain adaptations in correlation with changes of behavior in young adults with varying musical experience, anticipating gradual changes in behavior, brain functioning and brain structure with degree of musical aptitude. In this frame, I did the data collection of hippocampus volumes and analyzed the results in correlation with a literature research on the subject

Topography of slow sigma power during sleep is associated with processing speed in preschool children

Cognitive development is influenced by maturational changes in processing speed, a construct reflecting the rapidity of executing cognitive operations. Although cognitive ability and processing speed are linked to spindles and sigma power in the sleep electroencephalogram (EEG), little is known about such associations in early childhood, a time of major neuronal refinement. We calculated EEG power for slow (10-13 Hz) and fast (13.25-17 Hz) sigma power from all-night high-density electroencephalography (EEG) in a cross-sectional sample of healthy preschool children (n = 10, 4.3 ± 1.0 years). Processing speed was assessed as simple reaction time. On average, reaction time was 1409 ± 251 ms; slow sigma power was 4.0 ± 1.5 μV²; and fast sigma power was 0.9 ± 0.2 μV². Both slow and fast sigma power predominated over central areas. Only slow sigma power was correlated with processing speed in a large parietal electrode cluster (p \u3c 0.05, r ranging from -0.6 to -0.8), such that greater power predicted faster reaction time. Our findings indicate regional correlates between sigma power and processing speed that are specific to early childhood and provide novel insights into the neurobiological features of the EEG that may underlie developing cognitive abilities

Brain Activation Time-Locked to Sleep Spindles Associated With Human Cognitive Abilities

Simultaneous electroencephalography and functional magnetic resonance imaging (EEG–fMRI) studies have revealed brain activations time-locked to spindles. Yet, the functional significance of these spindle-related brain activations is not understood. EEG studies have shown that inter-individual differences in the electrophysiological characteristics of spindles (e.g., density, amplitude, duration) are highly correlated with “Reasoning” abilities (i.e., “fluid intelligence”; problem solving skills, the ability to employ logic, identify complex patterns), but not short-term memory (STM) or verbal abilities. Spindle-dependent reactivation of brain areas recruited during new learning suggests night-to-night variations reflect offline memory processing. However, the functional significance of stable, trait-like inter-individual differences in brain activations recruited during spindle events is unknown. Using EEG–fMRI sleep recordings, we found that a subset of brain activations time-locked to spindles were specifically related to Reasoning abilities but were unrelated to STM or verbal abilities. Thus, suggesting that individuals with higher fluid intelligence have greater activation of brain regions recruited during spontaneous spindle events. This may serve as a first step to further understand the function of sleep spindles and the brain activity which supports the capacity for Reasoning