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How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods to Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture., (Copyright © 2021. Published by Elsevier Inc.)

When asked to perform the same task, different individuals exhibit markedly different patterns of brain activity. This variability is often attributed to volatile factors, such as task strategy or compliance. We propose that individual differences in brain responses are, to a large degree, inherent to the brain and can be predicted from task-independent measurements collected at rest. Using a large set of task conditions, spanning several behavioral domains, we train a simple model that relates task-independent measurements to task activity and evaluate the model by predicting task activation maps for unseen subjects using magnetic resonance imaging. Our model can accurately predict individual differences in brain activity and highlights a coupling between brain connectivity and function that can be captured at the level of individual subjects., (Copyright © 2016, American Association for the Advancement of Science.)

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Several studies have demonstrated age-related regional differences in the magnitude of the BOLD signal using task-based fMRI. It has been suggested that functional changes reflect either compensatory or de-differentiation mechanisms, both of which assume response to a specific stimulus. Here, we have tested whether ageing affects both task-based and resting brain function, and the extent to which functional changes are mediated by reductions in grey matter (GM) volume. Two groups, of 22 healthy younger and 22 older volunteers, underwent an imaging protocol involving structural and functional MRI, both during a memory task and at rest. The two groups had similar socio-demographical characteristics and cognitive performance. Image analysis revealed both structural and functional differences. Increased BOLD signal in older relative to younger volunteers was mainly observed in the frontal lobes, both during the task and at rest. Functional changes in the frontal lobes were largely located in brain regions spared from GM loss, and adding GM covariates to the fMRI analysis did not significantly alter the group differences. Our results are consistent with the suggestion that, during normal ageing, the brain responds to neuronal loss by fine-tuning connections between spared neurons. Longitudinal studies will be necessary to fully test this hypothesis., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Schippers, Renken and Keysers (NeuroImage, 2011) present a simulation of multi-subject lag-based causality estimation. We fully agree that single-subject evaluations (e.g., Smith et al., 2011) need to be revisited in the context of multi-subject studies, and Schippers' paper is a good example, including detailed multi-level simulation and cross-subject statistical modelling. The authors conclude that "the average chance to find a significant Granger causality effect when no actual influence is present in the data stays well below the p-level imposed on the second level statistics" and that "when the analyses reveal a significant directed influence, this direction was accurate in the vast majority of the cases". Unfortunately, we believe that the general meaning that may be taken from these statements is not supported by the paper's results, as there may in reality be a systematic (group-average) difference in haemodynamic delay between two brain areas. While many statements in the paper (e.g., the final two sentences) do refer to this problem, we fear that the overriding message that many readers may take from the paper could cause misunderstanding., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Increasing age and carrying an APOE ε4 allele are well established risk factors for Alzheimer's disease (AD). The earlier age of onset of AD observed in ε4-carriers may reflect an accelerated aging process. We recently reported that APOE genotype modulates brain function decades before the appearance of any cognitive or clinical symptoms. Here we test the hypothesis that APOE influences brain aging by comparing healthy ε4-carriers and non-carriers, using the same imaging protocol in distinct groups of younger and older healthy volunteers. A cross-sectional factorial design was used to examine the effects of age and APOE genotype, and their interaction, on fMRI activation during an encoding memory task. The younger (N=36; age range 20-35; 18 ε4-carriers) and older (35 middle-age/elderly; age range 50-78 years; 15 ε4-carriers) healthy volunteers taking part in the study were cognitively normal. We found a significant interaction between age and ε4-status in the hippocampi, frontal pole, subcortical nuclei, middle temporal gyri and cerebellum, such that aging was associated with decreased activity in e4-carriers and increased activity in non-carriers. Reduced cerebral blood flow was found in the older ε4-carriers relative to older non-carriers despite preserved grey matter volume. Overactivity of brain function in young ε4-carriers is disproportionately reduced with advancing age even before the onset of measurable memory impairment. The APOE genotype determines age-related changes in brain function that may reflect the increased vulnerability of ε4-carriers to late-life pathology or cognitive decline., (Copyright © 2010 Elsevier Inc. All rights reserved.)

The default-mode network, a coherent resting-state brain network, is thought to characterize basal neural activity. Aberrant default-mode connectivity has been reported in a host of neurological and psychiatric illnesses and in persons at genetic risk for such illnesses. Whereas the neurophysiologic mechanisms that regulate default-mode connectivity are unclear, there is growing evidence that genetic factors play a role. In this report, we estimate the importance of genetic effects on the default-mode network by examining covariation patterns in functional connectivity among 333 individuals from 29 randomly selected extended pedigrees. Heritability for default-mode functional connectivity was 0.424 +/- 0.17 (P = 0.0046). Although neuroanatomic variation in this network was also heritable, the genetic factors that influence default-mode functional connectivity and gray-matter density seem to be distinct, suggesting that unique genes influence the structure and function of the network. In contrast, significant genetic correlations between regions within the network provide evidence that the same genetic factors contribute to variation in functional connectivity throughout the default mode. Specifically, the left parahippocampal region was genetically correlated with all other network regions. In addition, the posterior cingulate/precuneus region, medial prefrontal cortex, and right cerebellum seem to form a subnetwork. Default-mode functional connectivity is influenced by genetic factors that cannot be attributed to anatomic variation or a single region within the network. By establishing the heritability of default-mode functional connectivity, this experiment provides the obligatory evidence required before these measures can be considered as endophenotypes for psychiatric or neurological illnesses or to identify genes influencing intrinsic brain function.

Brain development continues actively during adolescence. Previous MRI studies have shown complex patterns of apparent loss of grey matter (GM) volume and increases in white matter (WM) volume and fractional anisotropy (FA), an index of WM microstructure. In this longitudinal study (mean follow-up=2.5+/-0.5 years) of 24 adolescents, we used a voxel-based morphometry (VBM)-style analysis with conventional T1-weighted images to test for age-related changes in GM and WM volumes. We also performed tract-based spatial statistics (TBSS) analysis of diffusion tensor imaging (DTI) data to test for age-related WM changes across the whole brain. Probabilistic tractography was used to carry out quantitative comparisons across subjects in measures of WM microstructure in two fiber tracts important for supporting speech and motor functions (arcuate fasciculus [AF] and corticospinal tract [CST]). The whole-brain analyses identified age-related increases in WM volume and FA bilaterally in many fiber tracts, including AF and many parts of the CST. FA changes were mainly driven by increases in parallel diffusivity, probably reflecting increases in the diameter of the axons forming the fiber tracts. FA values of both left and right AF (but not of the CST) were significantly higher at the end of the follow-up than at baseline. Over the same period, widespread reductions in the cortical GM volume were found. These findings provide imaging-based anatomical data suggesting that brain maturation in adolescence is associated with structural changes enhancing long-distance connectivities in different WM tracts, specifically in the AF and CST, at the same time that cortical GM exhibits synaptic "pruning".

This study investigated eyewitness identification using ERPs. Twenty participants completed two eyewitness lineup tasks (standard and deception conditions). For the standard condition, participants tried to accurately identify the culprit, whereas in the deception condition, they were asked to deceptively conceal their recognition of the culprit. Identification rates based on P300 patterns were calculated using two different individual analysis procedures (A and B) that varied in stringency. Correct identification rates for the standard condition were 100% for both procedures A and B. For the deception condition, correct identification rates of the concealed culprit were 90%, and 70% respectively for procedures A and B. Data from a prior study [the culprit-absent condition from Lefebvre, C.D., Marchand, Y., Smith, S.M. & Connolly, J.F., 2007. Determining eyewitness identification accuracy using event-related brain potentials (ERPs). Psychophysiology, 44, 894-904.] was reanalysed to investigate differences in false identification rates based on procedures A and B. False identifications were substantially higher when using procedure A (29%) versus procedure B (0%). Overall, superiority was found for procedure B compared to procedure A based on Grier's A'.

Objectives: To investigate the relationship between markers of vitamin B(12) status and brain volume loss per year over a 5-year period in an elderly population., Methods: A prospective study of 107 community-dwelling volunteers aged 61 to 87 years without cognitive impairment at enrollment. Volunteers were assessed yearly by clinical examination, MRI scans, and cognitive tests. Blood was collected at baseline for measurement of plasma vitamin B(12), transcobalamin (TC), holotranscobalamin (holoTC), methylmalonic acid (MMA), total homocysteine (tHcy), and serum folate., Results: The decrease in brain volume was greater among those with lower vitamin B(12) and holoTC levels and higher plasma tHcy and MMA levels at baseline. Linear regression analysis showed that associations with vitamin B(12) and holoTC remained significant after adjustment for age, sex, creatinine, education, initial brain volume, cognitive test scores, systolic blood pressure, ApoE epsilon4 status, tHcy, and folate. Using the upper (for the vitamins) or lower tertile (for the metabolites) as reference in logistic regression analysis and adjusting for the above covariates, vitamin B(12) in the bottom tertile (<308 pmol/L) was associated with increased rate of brain volume loss (odds ratio 6.17, 95% CI 1.25-30.47). The association was similar for low levels of holoTC (<54 pmol/L) (odds ratio 5.99, 95% CI 1.21-29.81) and for low TC saturation. High levels of MMA or tHcy or low levels of folate were not associated with brain volume loss., Conclusion: Low vitamin B(12) status should be further investigated as a modifiable cause of brain atrophy and of likely subsequent cognitive impairment in the elderly.

Normal aging is associated with cognitive decline. Functions such as attention, information processing, and working memory are compromised. It has been hypothesized that not only regional changes, but also alterations in the integration of regional brain activity (functional brain connectivity) underlie the observed age-related deficits. Here, we examined the functional properties of brain networks based on spontaneous fluctuations within brain systems using functional magnetic resonance imaging. We hypothesized that functional connectivity of intrinsic brain activity in the "default-mode" network (DMN) is affected by normal aging and that this relates to cognitive function. Ten younger and 22 older subjects were scanned at "rest," that is, lying awake with eyes closed. Our results show decreased activity in older versus younger subjects in 2 resting-state networks (RSNs) resembling the previously described DMN, containing the superior and middle frontal gyrus, posterior cingulate, middle temporal gyrus, and the superior parietal region. These results remain significant after correction for RSN-specific gray matter volume. The relevance of these findings is illustrated by the correlation between reduced activity of one of these RSNs and less effective executive functioning/processing speed in the older group.

Postmortem histological studies have demonstrated that myelination in human brain white matter (WM) continues throughout adolescence and well into adulthood. We used in vivo diffusion-weighted magnetic resonance imaging to test for age-related WM changes in 42 adolescents and 20 young adults. Tract-Based Spatial Statistics (TBSS) analysis of the adolescent data identified widespread age-related increases in fractional anisotropy (FA) that were most significant in clusters including the body of the corpus callosum and right superior corona radiata. These changes were driven by changes in perpendicular, rather than parallel, diffusivity. These WM clusters were used as seeds for probabilistic tractography, allowing us to identify the regions as belonging to callosal, corticospinal, and prefrontal tracts. We also performed voxel-based morphometry-style analysis of conventional T1-weighted images to test for age-related changes in grey matter (GM). We identified a cluster including right middle frontal and precentral gyri that showed an age-related decrease in GM density through adolescence and connected with the tracts showing age-related WM FA increases. The GM density decrease was highly significantly correlated with the WM FA increase in the connected cluster. Age-related changes in FA were much less prominent in the young adult group, but we did find a significant age-related increase in FA in the right superior longitudinal fascicle, suggesting that structural development of this pathway continues into adulthood. Our results suggest that significant microstructural changes in WM continue throughout adolescence and are associated with corresponding age-related changes in cortical GM regions.

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.

Functional magnetic resonance imaging (fMRI) studies of the human brain have suggested that low-frequency fluctuations in resting fMRI data collected using blood oxygen level dependent (BOLD) contrast correspond to functionally relevant resting state networks (RSNs). Whether the fluctuations of resting fMRI signal in RSNs are a direct consequence of neocortical neuronal activity or are low-frequency artifacts due to other physiological processes (e.g., autonomically driven fluctuations in cerebral blood flow) is uncertain. In order to investigate further these fluctuations, we have characterized their spatial and temporal properties using probabilistic independent component analysis (PICA), a robust approach to RSN identification. Here, we provide evidence that: i. RSNs are not caused by signal artifacts due to low sampling rate (aliasing); ii. they are localized primarily to the cerebral cortex; iii. similar RSNs also can be identified in perfusion fMRI data; and iv. at least 5 distinct RSN patterns are reproducible across different subjects. The RSNs appear to reflect "default" interactions related to functional networks related to those recruited by specific types of cognitive processes. RSNs are a major source of non-modeled signal in BOLD fMRI data, so a full understanding of their dynamics will improve the interpretation of functional brain imaging studies more generally. Because RSNs reflect interactions in cognitively relevant functional networks, they offer a new approach to the characterization of state changes with pathology and the effects of drugs.

We discuss model-free analysis of multisubject or multisession FMRI data by extending the single-session probabilistic independent component analysis model (PICA; Beckmann and Smith, 2004. IEEE Trans. on Medical Imaging, 23 (2) 137-152) to higher dimensions. This results in a three-way decomposition that represents the different signals and artefacts present in the data in terms of their temporal, spatial, and subject-dependent variations. The technique is derived from and compared with parallel factor analysis (PARAFAC; Harshman and Lundy, 1984. In Research methods for multimode data analysis, chapter 5, pages 122-215. Praeger, New York). Using simulated data as well as data from multisession and multisubject FMRI studies we demonstrate that the tensor PICA approach is able to efficiently and accurately extract signals of interest in the spatial, temporal, and subject/session domain. The final decompositions improve upon PARAFAC results in terms of greater accuracy, reduced interference between the different estimated sources (reduced cross-talk), robustness (against deviations of the data from modeling assumptions and against overfitting), and computational speed. On real FMRI 'activation' data, the tensor PICA approach is able to extract plausible activation maps, time courses, and session/subject modes as well as provide a rich description of additional processes of interest such as image artefacts or secondary activation patterns. The resulting data decomposition gives simple and useful representations of multisubject/multisession FMRI data that can aid the interpretation and optimization of group FMRI studies beyond what can be achieved using model-based analysis techniques.

In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping, or "coloring," attempts to negate the effects of not accurately knowing the intrinsic autocorrelations by imposing known autocorrelation via temporal filtering. Removing the autocorrelation, or "prewhitening," gives the best linear unbiased estimator, assuming that the autocorrelation is accurately known. For single-event designs, the efficiency of the estimator is considerably higher for prewhitening compared with coloring. However, it has been suggested that sufficiently accurate estimates of the autocorrelation are currently not available to give prewhitening acceptable bias. To overcome this, we consider different ways to estimate the autocorrelation for use in prewhitening. After high-pass filtering is performed, a Tukey taper (set to smooth the spectral density more than would normally be used in spectral density estimation) performs best. Importantly, estimation is further improved by using nonlinear spatial filtering to smooth the estimated autocorrelation, but only within tissue type. Using this approach when prewhitening reduced bias to close to zero at probability levels as low as 1 x 10(-5)., (Copyright 2001 Academic Press.)

Purpose: Quantitative measurement of change in brain size and shape (e.g., to estimate atrophy) is an important current area of research. New methods of change analysis attempt to improve robustness, accuracy, and extent of automation. A fully automated method has been developed that achieves high estimation accuracy., Method: A fully automated method of longitudinal change analysis is presented here, which automatically segments brain from nonbrain in each image, registers the two brain images while using estimated skull images to constrain scaling and skew, and finally estimates brain surface motion by tracking surface points to subvoxel accuracy., Results and Conclusion: The method described has been shown to be accurate ( approximately 0.2% brain volume change error) and to achieve high robustness (no failures in several hundred analyses over a range of different data sets).

A conserved hormone response element, CNTFR-DR1 (5'-AGGTCAGAGGTCAGG-3'), has been identified in the 5th intron of the alpha component of the ciliary neurotrophic factor receptor (CNTFRalpha) gene for the human TR4 orphan receptor (TR4). Electrophoretic mobility shift assay showed a specific binding with high affinity (Kd = 0.066 nM) between TR4 and the CNTFR-DR1. A reporter gene assay using chloramphenicol acetyltransferase demonstrated that the 5th intron of CNTFRalpha has an enhancer activity which could be induced by TR4 in a dose-dependent manner. Furthermore, our in situ hybridization data showed that abundant TR4 transcripts were detected in adult brain, in regions of cortical and hippocampal neurons, as well as in many developing neural structures, including brain, spinal cord, ganglia (sympathetic and sensory), and neuronal epithelia (retinal, otic, olfactory, and gustatory). The striking similarities in the expression patterns of TR4 and CNTFRalpha in the developing and postnatal nervous systems further support the potential role of TR4 in neurogenesis. Collectively, these data suggest that the human CNTFRalpha gene could represent the first identified neural-specific gene induced by TR4.

The regional distribution of inositol 1,4,5-trisphosphate (InsP3), inositol 1,3,4,5-tetrakisphosphate (InsP4), and ryanodine binding sites has been characterised and compared in the rat brain using radioligand binding assays. Cortical [3H]InsP3 binding indicated similar positional and stereospecificity as observed in other tissues, with 100-fold selectivity for InsP3 over InsP4. Similarly, high-affinity [32P]InsP4 binding also showed a high degree of positional specificity, with a 1,000-fold selectivity for InsP4 over InsP3. Initial characterisation of [3H]ryanodine binding to cortical membranes demonstrated that specific binding was highly dependent on high salt and micromolar Ca2+ concentrations and inhibited by Ca2+ levels of > 1 mM. [3H]-Ryanodine binding was also enhanced by beta, gamma-methylene-adenosine 5'-trisphosphate and caffeine and inhibited by magnesium and ruthenium red (Ki = 0.81 microM). However, dantrolene (300 microM) was ineffective on the binding. Therefore, although the results indicate a greater similarity to the binding properties of the Ca(2+)-induced Ca2+ release channel isoform present in skeletal, rather than cardiac, muscle, it does not appear to be identical. Detailed binding analysis of ryanodine and polyphosphate sites, with the exception of ruthenium red, indicated no interaction between binding sites. Ruthenium red markedly enhanced the binding of both [3H]InsP3 and [32P]InsP4, an effect most probably due to nonspecific complex formation. Regional binding of InP3, InsP4, and ryanodine in the rat brain was of similar affinity for each ligand in each area, but the density profile for each ligand was clearly different. The highest density of InsP3 sites was in the cerebellum, whereas the highest density of ryanodine sites was in the hippocampus.(ABSTRACT TRUNCATED AT 250 WORDS)