Changes in resting-state functional connectivity (rs-FC) under general anesthesia have been widely studied with the goal of identifying neural signatures of consciousness. This work has commonly revealed an apparent fragmentation of whole-brain network structure during unconsciousness, which has been interpreted as reflecting a break-down in connectivity and a disruption of the brain's ability to integrate information. Here we show, by studying rs-FC under varying depths of isoflurane-induced anesthesia in nonhuman primates, that this apparent fragmentation, rather than reflecting an actual change in network structure, can be simply explained as the result of a global reduction in FC. Specifically, by comparing the actual FC data to surrogate data sets that we derived to test competing hypotheses of how FC changes as a function of dose, we found that increases in whole-brain modularity and the number of network communities - considered hallmarks of fragmentation - are artifacts of constructing FC networks by thresholding based on correlation magnitude. Taken together, our findings suggest that deepening levels of unconsciousness are instead associated with the increasingly muted expression of functional networks, an observation that constrains current interpretations as to how anesthesia-induced FC changes map onto existing neurobiological theories of consciousness.

Functional neuroimaging has been used to show that the developing auditory cortex of very young human infants responds, in some way, to sound. However, impoverished stimuli and uncontrolled designs have made it difficult to attribute brain responses to specific auditory features, and thus made it difficult to assess the maturity of feature tuning in auditory cortex. To address this, we used functional magnetic resonance imaging (fMRI) to measure the brain activity evoked by naturalistic sounds (a series of sung lullabies) in two groups of infants (3 and 9 months) and adults. We developed a novel analysis method - inter-subject regression (ISR) - to quantify the similarity of cortical responses between infants and adults, and to decompose components of the response due to different auditory features. We found that the temporal pattern of activity in infant auditory cortex shared similarity with adults. Some of this shared response could be attributed to simple acoustic features, such as frequency, pitch, envelope, but other parts were not, suggesting that even more complex adult-like features are represented in auditory cortex in early infancy.

There is a considerable body of neuropsychological and neuroimaging evidence supporting the distinction between the brain correlates of noun and verb processing. It is however still not clear whether the observed differences are imputable to grammatical or semantic factors. Beyond the basic difference that verbs typically refer to actions and nouns typically refer to objects, other semantic distinctions might play a role as organizing principles within and across word classes. One possible candidate is the notion of manipulation and manipulability, which may modulate the word class dissociation. We used functional magnetic resonance imaging (fMRI) to study the impact of semantic reference and word class on brain activity during a picture naming task. Participants named pictures of objects and actions that did or did not involve manipulation. We observed extensive differences in activation associated with the manipulation dimension. In the case of manipulable items, for both nouns and verbs, there were significant activations within a fronto-parietal system subserving hand action representation. However, we found no significant effect of word class when all verbs were compared to all nouns. These results highlight the impact of the biologically crucial sensorimotor dimension of manipulability on the pattern of brain activity associated to picture naming.

Anecdotal evidence suggests that functional magnetic resonance imaging (fMRI) studies rarely consider statistical power when setting a sample size. This raises concerns since undersized studies may fail to detect effects of interest and encourage data dredging. Although sample size methodology in this field exists, implementation requires specifications of estimated effect size and variance components. We therefore systematically evaluated how often estimates of effect size and variance components were reported in observational fMRI studies involving clinical human participants published in six leading journals between January 2010 and December 2011. A random sample of 100 eligible articles was included in data extraction and analyses. Two independent reviewers assessed the reporting of sample size calculations and the data components required to perform the calculations in the fMRI literature. One article (1%) reported sample size calculations. The reporting of parameter estimates for effect size (8%), between-subject variance (4%), within-subject variance (1%) and temporal autocorrelation matrix (0%) was uncommon. Three articles (3%) reported Cohen's d or F effect sizes. The majority (83%) reported peak or average t, z or F statistics. The inter-rater agreement was very good, with a prevalence-adjusted bias-adjusted kappa (PABAK) value greater than 0.88. We concluded that sample size calculations were seldom reported in fMRI studies. Moreover, omission of parameter estimates for effect size, between- and within-subject variances, and temporal autocorrelation matrix could limit investigators' ability to perform power analyses for new studies. We suggest routine reporting of these quantities, and recommend strategies for reducing bias in their reported values.

Recently, much attention has been focused on the definition and structure of the hippocampus and its subfields, while the projections from the hippocampus have been relatively understudied. Here, we derive a reliable protocol for manual segmentation of hippocampal white matter regions (alveus, fimbria, and fornix) using high-resolution magnetic resonance images that are complementary to our previous definitions of the hippocampal subfields, both of which are freely available at https://github.com/cobralab/atlases. Our segmentation methods demonstrated high inter- and intra-rater reliability, were validated as inputs in automated segmentation, and were used to analyze the trajectory of these regions in both healthy aging (OASIS), and Alzheimer's disease (AD) and mild cognitive impairment (MCI; using ADNI). We observed significant bilateral decreases in the fornix in healthy aging while the alveus and cornu ammonis (CA) 1 were well preserved (all p's<0.006). MCI and AD demonstrated significant decreases in fimbriae and fornices. Many hippocampal subfields exhibited decreased volume in both MCI and AD, yet no significant differences were found between MCI and AD cohorts themselves. Our results suggest a neuroprotective or compensatory role for the alveus and CA1 in healthy aging and suggest that an improved understanding of the volumetric trajectories of these structures is required.

The preschool period is a time of significant physical and behavioral growth, including the improvement of gross and fine motor skills. Although motor development has been comprehensively mapped from infancy to adulthood, the neural correlates associated with motor advancements during early childhood remain unclear. The current study used diffusion tensor imaging (DTI) to delineate key motor pathways and characterize their relationships with motor performance in 80 typically developing preschool children, aged 3-6 years. The Movement Assessment Battery for Children-2nd edition (MABC-II) was used to assess motor functioning. Partial correlations between DTI parameters and motor performance, controlling for sex and age, revealed a positive correlation between motor performance and fractional anisotropy of corpus callosum motor fibers, as well as negative correlations of motor performance with mean and radial diffusivity. These results appear to be driven by females, as correlations were significant in girls but not boys when analyzed separately. Mean corticospinal tract (CST) diffusion parameters were not significantly related to motor performance, but relationships were observed at regionally specific locations along the bilateral CST. These findings suggest preschool-aged children with better motor performance show more mature white matter patterns within motor pathways, and that the structural variation in these pathways may partially account for the natural variability in motor performance.

Hippocampal volumetry derived from structural MRI is increasingly used to delineate regions of interest for functional measurements, assess efficacy in therapeutic trials of Alzheimer's disease (AD) and has been endorsed by the new AD diagnostic guidelines as a radiological marker of disease progression. Unfortunately, morphological heterogeneity in AD can prevent accurate demarcation of the hippocampus. Recent developments in automated volumetry commonly use multi-template fusion driven by expert manual labels, enabling highly accurate and reproducible segmentation in disease and healthy subjects. However, there are several protocols to define the hippocampus anatomically in vivo, and the method used to generate atlases may impact automatic accuracy and sensitivity - particularly in pathologically heterogeneous samples. Here we report a fully automated segmentation technique that provides a robust platform to directly evaluate both technical and biomarker performance in AD among anatomically unique labeling protocols. For the first time we test head-to-head the performance of five common hippocampal labeling protocols for multi-atlas based segmentation, using both the Sunnybrook Longitudinal Dementia Study and the entire Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) baseline and 24-month dataset. We based these atlas libraries on the protocols of (Haller et al., 1997; Killiany et al., 1993; Malykhin et al., 2007; Pantel et al., 2000; Pruessner et al., 2000), and a single operator performed all manual tracings to generate de facto "ground truth" labels. All methods distinguished between normal elders, mild cognitive impairment (MCI), and AD in the expected directions, and showed comparable correlations with measures of episodic memory performance. Only more inclusive protocols distinguished between stable MCI and MCI-to-AD converters, and had slightly better associations with episodic memory. Moreover, we demonstrate that protocols including more posterior anatomy and dorsal white matter compartments furnish the best voxel-overlap accuracies (Dice Similarity Coefficient=0.87-0.89), compared to expert manual tracings, and achieve the smallest sample sizes required to power clinical trials in MCI and AD. The greatest distribution of errors was localized to the caudal hippocampus and the alveus-fimbria compartment when these regions were excluded. The definition of the medial body did not significantly alter accuracy among more comprehensive protocols. Voxel-overlap accuracies between automatic and manual labels were lower for the more pathologically heterogeneous Sunnybrook study in comparison to the ADNI-1 sample. Finally, accuracy among protocols appears to significantly differ the most in AD subjects compared to MCI and normal elders. Together, these results suggest that selection of a candidate protocol for fully automatic multi-template based segmentation in AD can influence both segmentation accuracy when compared to expert manual labels and performance as a biomarker in MCI and AD.

Generative models focused on multifactorial causal mechanisms in brain disorders are scarce and generally based on limited data. Despite the biological importance of the multiple interacting processes, their effects remain poorly characterized from an integrative analytic perspective. Here, we propose a spatiotemporal multifactorial causal model (MCM) of brain (dis)organization and therapeutic intervention that accounts for local causal interactions, effects propagation via physical brain networks, cognitive alterations, and identification of optimum therapeutic interventions. In this article, we focus on describing the model and applying it at the population-based level for studying late onset Alzheimer's disease (LOAD). By interrelating six different neuroimaging modalities and cognitive measurements, this model accurately predicts spatiotemporal alterations in brain amyloid-β (Aβ) burden, glucose metabolism, vascular flow, resting state functional activity, structural properties, and cognitive integrity. The results suggest that a vascular dysregulation may be the most-likely initial pathologic event leading to LOAD. Nevertheless, they also suggest that LOAD it is not caused by a unique dominant biological factor (e.g. vascular or Aβ) but by the complex interplay among multiple relevant direct interactions. Furthermore, using theoretical control analysis of the identified population-based multifactorial causal network, we show the crucial advantage of using combinatorial over single-target treatments, explain why one-target Aβ based therapies might fail to improve clinical outcomes, and propose an efficiency ranking of possible LOAD interventions. Although still requiring further validation at the individual level, this work presents the first analytic framework for dynamic multifactorial brain (dis)organization that may explain both the pathologic evolution of progressive neurological disorders and operationalize the influence of multiple interventional strategies.

Sleep benefits motor memory consolidation. This mnemonic process is thought to be mediated by thalamo-cortical spindle activity during NREM-stage2 sleep episodes as well as changes in striatal and hippocampal activity. However, direct experimental evidence supporting the contribution of such sleep-dependent physiological mechanisms to motor memory consolidation in humans is lacking. In the present study, we combined EEG and fMRI sleep recordings following practice of a motor sequence learning (MSL) task to determine whether spindle oscillations support sleep-dependent motor memory consolidation by transiently synchronizing and coordinating specialized cortical and subcortical networks. To that end, we conducted EEG source reconstruction on spindle epochs in both cortical and subcortical regions using novel deep-source localization techniques. Coherence-based metrics were adopted to estimate functional connectivity between cortical and subcortical structures over specific frequency bands. Our findings not only confirm the critical and functional role of NREM-stage2 sleep spindles in motor skill consolidation, but provide first-time evidence that spindle oscillations [11-17 Hz] may be involved in sleep-dependent motor memory consolidation by locally reactivating and functionally binding specific task-relevant cortical and subcortical regions within networks including the hippocampus, putamen, thalamus and motor-related cortical regions.

Manganese-enhanced magnetic resonance imaging (MEMRI) is a widely used technique in rodent neuroimaging studies. Traditionally, Mn2+ is delivered to animals via a systemic injection; however, this can lead to toxic effects at high doses. Recent studies have shown that subcutaneously implanted mini-osmotic pumps can be used to continuously deliver manganese chloride (MnCl2), and that they produce satisfactory contrast while circumventing many of the toxic side effects. However, neither the time-course of signal enhancement nor the effect of continuous Mn2+ delivery on behaviour, particularly learning and memory, have been well-characterized. Here, we investigated the effect of MnCl2 dose and route of administration on a) spatial learning in the Morris Water Maze and b) tissue signal enhancement in the mouse brain. Even as early as 3 days after pump implantation, infusion of 25-50 mg/kg/day MnCl2 via osmotic pump produced signal enhancement as good as or better than that achieved 24 h after a single 50 mg/kg intraperitoneal injection. Neither route of delivery nor MnCl2 dose adversely affected spatial learning and memory on the water maze. However, especially at higher doses, mice receiving MnCl2 via osmotic pumps developed skin ulceration which limited the imaging window. With these findings, we provide recommendations for route and dose of MnCl2 to use for different study designs.

Converging areas of research have implicated glutamate and γ-aminobutyric acid (GABA) as key players in neuronal signalling and other central functions. Further research is needed, however, to identify microstructural and behavioral links to regional variability in levels of these neurometabolites, particularly in the presence of demyelinating disease. Thus, we sought to investigate the extent to which regional glutamate and GABA levels are related to a neuroimaging marker of microstructural damage and to motor and cognitive performance. Twenty-one healthy volunteers and 47 people with multiple sclerosis (all right-handed) participated in this study. Motor and cognitive abilities were assessed with standard tests used in the study of multiple sclerosis. Proton magnetic resonance spectroscopy data were acquired from sensorimotor and parietal regions of the brains' left cerebral hemisphere using a MEGA-PRESS sequence. Our analysis protocol for the spectroscopy data was designed to account for confounding factors that could contaminate the measurement of neurometabolite levels due to disease, such as the macromolecule signal, partial volume effects, and relaxation effects. Glutamate levels in both regions of interest were lower in people with multiple sclerosis. In the sensorimotor (though not the parietal) region, GABA concentration was higher in the multiple sclerosis group compared to controls. Lower magnetization transfer ratio within grey and white matter regions from which spectroscopy data were acquired was linked to neurometabolite levels. When adjusting for age, normalized brain volume, MTR, total N-acetylaspartate level, and glutamate level, significant relationships were found between lower sensorimotor GABA level and worse performance on several tests, including one of upper limb motor function. This work highlights important methodological considerations relevant to analysis of spectroscopy data, particularly in the afflicted human brain. These findings support that regional neurotransmitter levels are linked to local microstructural integrity and specific behavioral abilities that can be affected in diseases such as multiple sclerosis.

Biological sex influences brain anatomy across many species. Sex differences in brain anatomy have classically been attributed to differences in sex chromosome complement (XX versus XY) and/or in levels of gonadal sex steroids released from ovaries and testes. Using the four core genotype (4CG) mouse model in which gonadal sex and sex chromosome complement are decoupled, we previously found that sex hormones and chromosomes influence the volume of distinct brain regions. However, recent studies suggest there may be more complex interactions between hormones and chromosomes, and that circulating steroids can compensate for and/or mask underlying chromosomal effects. Moreover, the impact of pre vs post-pubertal sex hormone exposure on this sex hormone/sex chromosome interplay is not well understood. Thus, we used whole brain high-resolution ex-vivo MRI of intact and pre-pubertally gonadectomized 4CG mice to investigate two questions: 1) Do circulating steroids mask sex differences in brain anatomy driven by sex chromosome complement? And 2) What is the contribution of pre- versus post-pubertal hormones to sex-hormone-dependent differences in brain anatomy? We found evidence of both cooperative and compensatory interactions between sex chromosomes and sex hormones in several brain regions, but the interaction effects were of low magnitude. Additionally, most brain regions affected by sex hormones were sensitive to both pre- and post-pubertal hormones. This data provides further insight into the biological origins of sex differences in brain anatomy.

MR-guided focused ultrasound (MRgFUS) can temporarily permeabilize the blood-brain barrier (BBB), noninvasively, to allow therapeutics access to the central nervous system. However, its secondary and potential neuromodulation effects are not well understood. We aimed to characterize the functional impact of MRgFUS BBB opening in human subjects, based on the phase I trial in patients with Alzheimer's disease. We analyzed for changes in bilateral frontoparietal networks in resting state functional MRI from five subjects after BBB opening in the right frontal lobe. We found a transient functional connectivity decrease within only the ipsilateral frontoparietal network that was recovered by the next day. Additionally, baseline to month three comparisons did not reveal any significant differences from matched-controls from the Alzheimer's Disease Neuroimaging Initiative. Overall, MRgFUS may transiently affect neurologic function, but the functional organization is restored at one day and remains unchanged at three months. This first in human data has implications for the development of MRgFUS as a drug delivery platform to pathologic brain tissue and potential use for non-invasive neuromodulation.

Learning associations between stimuli and responses is essential to everyday life. Dorsal striatum (DS) has long been implicated in stimulus-response learning, though recent results challenge this contention. We have proposed that discrepant findings arise because stimulus-response learning methodology generally confounds learning and response selection processes. In 19 patients with Parkinson's disease (PD) and 18 age-matched controls, we found that dopaminergic therapy decreased the efficiency of stimulus-response learning, with corresponding attenuation of ventral striatum (VS) activation. In contrast, exogenous dopamine improved response selection accuracy related to enhanced DS BOLD signal. Contrasts between PD patients and controls fully support these within-subject patterns. These double dissociations in terms of behaviour and neural activity related to VS and DS in PD and in response to dopaminergic therapy, strongly refute the view that DS mediates stimulus-response learning through feedback. Our findings integrate with a growing literature favouring a role for DS in decision making rather than learning, and unite two literature that have been evolving independently.

The internal structure of the human hippocampus is challenging to map using histology or neuroimaging due to its complex archicortical folding. Here, we aimed to overcome this challenge using a unique combination of three methods. First, we leveraged a histological dataset with unprecedented 3D coverage, BigBrain. Second, we imposed a computational unfolding framework that respects the topological continuity of hippocampal subfields, which are traditionally defined by laminar composition. Third, we adapted neocortical parcellation techniques to map the hippocampus with respect to not only laminar but also morphological features. Unsupervised clustering of these features revealed subdivisions that closely resemble gold standard manual subfield segmentations. Critically, we also show that morphological features alone are sufficient to derive most hippocampal subfield boundaries. Moreover, some features showed differences within subfields along the hippocampal longitudinal axis. Our findings highlight new characteristics of internal hippocampal structure, and offer new avenues for its characterization with in-vivo neuroimaging.

Recent findings suggest that oscillatory alpha activity (7-13Hz) is associated with functional inhibition of sensory regions by filtering incoming information. Accordingly the alpha power in visual regions varies in anticipation of upcoming, predictable stimuli which has consequences for visual processing and subsequent behavior. In covert spatial attention studies it has been demonstrated that performance correlates with the adaptation of alpha power in response to explicit spatial cueing. However it remains unknown whether such an adaptation also occurs in response to implicit statistical properties of a task. In a covert attention switching paradigm, we here show evidence that individuals differ on how they adapt to implicit statistical properties of the task. Subjects whose behavioral performance reflects the implicit change in switch trial likelihood show strong adjustment of anticipatory alpha power lateralization. Most importantly, the stronger the behavioral adjustment to the switch trial likelihood was, the stronger the adjustment of anticipatory posterior alpha lateralization. We conclude that anticipatory spatial attention is reflected in the distribution of posterior alpha band power which is predictive of individual detection performance in response to the implicit statistical properties of the task.

Magnetic resonance spectroscopy (MRS) is a non-invasive neuroimaging technique used to measure brain chemistry in vivo and has been used to study the healthy brain as well as neuropathology in numerous neurological disorders. The number of multi-site studies using MRS are increasing; however, non-biological variability introduced during data collection across multiple sites, such as differences in scanner vendors and site-specific acquisition implementations for MRS, can obscure detection of biological effects of interest. ComBat is a data harmonization technique that can remove non-biological sources of variance in multisite studies. It has been validated for use with structural and functional MRI metrics but not for MRS measured metabolites. This study investigated the validity of using ComBat to harmonize MRS metabolites for vendor and site differences. Analyses were performed using data acquired across 20 sites and included edited MRS for GABA+ (N = 218) and macromolecule-suppressed GABA data (N = 209), as well as standard PRESS data to quantify NAA, creatine, choline, and glutamate (N = 190). ComBat harmonization successfully mitigated vendor and site differences for all metabolites of interest. Moreover, significant associations were detected between sex and choline levels and between age and glutamate and GABA+ levels that were not detectable prior to harmonization, confirming the importance of removing site and vendor effects in multi-site data. In conclusion, ComBat harmonization can be successfully applied to MRS data in multi-site MRS studies.

Adequate cerebral blood flow (CBF) is essential to proper brain development and function. Detailed characterization of CBF developmental trajectories will lead to better understanding of the development of cognitive, motor, and sensory functions, as well as behaviour in children. Previous studies have shown CBF increases during infancy and decreases during adolescence; however, the trajectories during childhood, and in particular the timing of peak CBF, remain unclear. Here, we used arterial spin labeling to map age-related changes of CBF across a large longitudinal sample that included 279 scans on 96 participants (46 girls and 50 boys) aged 2-7 years. CBF maps were analyzed using hierarchical linear regression for every voxel inside the grey matter mask, controlling for multiple comparisons. The results revealed a significant positive linear association between CBF and age in distributed brain regions including prefrontal, temporal, parietal, and occipital cortex, and in the cerebellum. There were no differences in developmental trajectories between males and females. Our findings show that CBF continues to increase until the age of 7 years, likely supporting ongoing improvements in behaviour, cognition, motor, and sensory functions in early childhood.

Simultaneous recordings of Electro-EncephaloGraphy (EEG) with Near InfraRed Spectroscopy (NIRS) allow measuring hemodynamic changes (changes in the concentration of oxy- and deoxyhemoglobin) at the time of epileptic discharges detected on scalp EEG. Two NIRS detection methods based on the General Linear Model (GLM) respectively in the time domain and in the time-frequency domain are investigated in this study using realistic simulations of spontaneous interictal epileptic activity. We evaluated the sensitivity at different Signal to Noise Ratios (SNR), the effect of either a large or a small number of discharges and the impact of model misspecification (e.g. omission or false detection of epileptic discharges). We also explored the effect on the estimation of key parameters, which set the model order. Simulations showed that both methods become inaccurate in lower SNR conditions, leading to many false positive detections. However, the time-frequency estimator showed better performance than the time-domain one. Key parameters for each algorithm were identified and results suggest to model confounds in the GLM differently for oxy- and deoxyhemoglobin. We also demonstrated that an inaccurate marking of epileptic events has a small impact on the detection statistics whereas an inaccurate specification of the hemodynamic response function delay decreases drastically the detection abilities. Finally, we illustrated the two methods on clinical EEG/NIRS data of one patient with focal epilepsy, showing an increase of regional Cerebral Blood Volume (rCBV) spatially concordant with the presumed epileptogenic focus.

We investigate the ability of diffusion tensor imaging (DTI) to distinguish between three experimental rat models of spinal cord injury mechanism - contusion, dislocation, and distraction. Ex vivo DTI scans were performed on cord specimens that were preserved at different time points of the acute injury (3 hr, 24 hr, and 7 days post-injury) across all three injury mechanisms. White matter was classified as abnormal if their DTI metric was substantially different from regional values measured from a set of uninjured controls, thus allowing generation of binary "white matter damage maps" which categorizes each pixel in the DTI image as "normal" or "damaged". Damage classification was most robust using thresholds in the longitudinal diffusivity, which supports previous studies that show that longitudinal diffusivity is the most robust DTI metric in depicting damage in SCI. Furthermore, the spatial damage patterns from all subjects in the same group were consolidated into a "damage occurrence ratio map", which illustrates an average damage shape that characterizes the injury mechanism. Our analysis has yielded a dataset which highlights the differences in injury pattern due to the initial mode of mechanical injury. For example, contusion produced an initial injury that emanated radially outward from the central canal, with subsequent damage along the caudal corticospinal tract and rostral gracile fasciculus; dislocation injuries showed a high level of involvement in the lateral and ventral white matter which became less apparent by 7 days post-injury, and distraction injuries were found to be less focal and more distributed rostrocaudally. This work represents a first step in adopting the use of the primary injury mechanism as a clinical prognostic factor in SCI, which may help to inform the trialing of existing neuroprotective treatment candidates, the development of new therapies as well as personalize the management of SCI for the individual patient.