Non-parametric combination and related permutation tests for neuroimaging

Structure-function abnormalities in cortical sensory projections in embouchure dystonia

BACKGROUND

Embouchure dystonia (ED) is a task-specific focal dystonia in professional brass players leading to abnormal orofacial muscle posturing/spasms during performance. Previous studies have outlined abnormal cortical sensorimotor function during sensory/motor tasks and in the resting state as well as abnormal cortical sensorimotor structure. Yet, potentially underlying white-matter tract abnormalities in this network disease are unknown.

OBJECTIVE

To delineate structure-function abnormalities within cerebral sensorimotor trajectories in ED.

METHOD

Probabilistic tractography and seed-based functional connectivity analysis were performed in 16/16 ED patients/healthy brass players within a simple literature-informed network model of cortical sensorimotor processing encompassing supplementary motor, superior parietal, primary somatosensory and motor cortex as well as the putamen. Post-hoc grey matter volumetry was performed within cortices of abnormal trajectories.

RESULTS

ED patients showed average axial diffusivity reduction within projections between the primary somatosensory cortex and putamen, with converse increases within projections between supplementary motor and superior parietal cortex in both hemispheres. Increase in the mode of anisotropy in patients was accompanying the latter left-hemispheric projection, as well as in the supplementary motor area's projection to the left primary motor cortex. Patient's left primary somatosensory functional connectivity with the putamen was abnormally reduced and significantly associated with the axial diffusivity reduction. Left primary somatosensory grey matter volume was increased in patients.

CONCLUSION

Correlates of abnormal tract integrity within primary somatosensory cortico-subcortical projections and higher-order sensorimotor projections support the key role of dysfunctional sensory information propagation in ED pathophysiology. Differential directionality of cortico-cortical and cortico-subcortical abnormalities hints at non-uniform sensory system changes.

In vivo neuroinflammation and cerebral small vessel disease in mild cognitive impairment and Alzheimer's disease

INTRODUCTION

Associations between cerebral small vessel disease (SVD) and inflammation have been largely examined using peripheral blood markers of inflammation, with few studies measuring inflammation within the brain. We investigated the cross-sectional relationship between SVD and in vivo neuroinflammation using [11C]PK11195 positron emission tomography (PET) imaging.

METHODS

Forty-two participants were recruited (according to NIA-AA guidelines, 14 healthy controls, 14 mild Alzheimer's disease, 14 amyloid-positive mild cognitive impairment). Neuroinflammation was assessed using [11C]PK11195 PET imaging, a marker of microglial activation. To quantify SVD, we assessed white matter hyperintensities (WMH), enlarged perivascular spaces, cerebral microbleeds and lacunes. Composite scores were calculated for global SVD burden, and SVD subtypes of hypertensive arteriopathy and cerebral amyloid angiopathy (CAA). General linear models examined associations between SVD and [11C]PK11195, adjusting for sex, age, education, cognition, scan interval, and corrected for multiple comparisons via false discovery rate (FDR). Dominance analysis directly compared the relative importance of hypertensive arteriopathy and CAA scores as predictors of [11C]PK11195.

RESULTS

Global [11C]PK11195 binding was associated with SVD markers, particularly in regions typical of hypertensive arteriopathy: deep microbleeds (β=0.63, F(1,35)=35.24, p<0.001), deep WMH (β=0.59, t=4.91, p<0.001). In dominance analysis, hypertensive arteriopathy score outperformed CAA in predicting [11C]PK11195 binding globally and in 28 out of 37 regions of interest, especially the medial temporal lobe (β=0.66-0.76, t=3.90-5.58, FDR-corrected p (pFDR)=<0.001-0.002) and orbitofrontal cortex (β=0.51-0.57, t=3.53-4.30, pFDR=0.001-0.004).

CONCLUSION

Microglial activation is associated with SVD, particularly with the hypertensive arteriopathy subtype of SVD. Although further research is needed to determine causality, our study suggests that targeting neuroinflammation might represent a novel therapeutic strategy for SVD.

The effects of an aerobic training intervention on cognition, grey matter volumes and white matter microstructure

While there is strong evidence from observational studies that physical activity is associated with reduced risk of cognitive decline and dementia, the extent to which aerobic training interventions impact on cognitive health and brain structure remains subject to debate. In a pilot study of 46 healthy older adults (66.6 years ± 5.2 years, 63% female), we compared the effects of a twelve-week aerobic training programme to a waitlist control condition on cardiorespiratory fitness, cognition and magnetic resonance imaging (MRI) outcomes. Cardiorespiratory fitness was assessed by VO2 max testing. Cognitive assessments spanned executive function, memory and processing speed. Structural MRI analysis included examination of hippocampal volume, and voxel-wise assessment of grey matter volumes using voxel-based morphometry. Diffusion tensor imaging analysis of fractional anisotropy, axial diffusivity and radial diffusivity was performed using tract-based spatial statistics. While the intervention successfully increased cardiorespiratory fitness, there was no evidence that the aerobic training programme led to changes in cognitive functioning or measures of brain structure in older adults. Interventions that are longer lasting, multi-factorial, or targeted at specific high-risk populations, may yield more encouraging results.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

Scientific Hypothesis-Testing Strengthens Neuroscience Research

Science needs to understand the strength of its findings. This essay considers the evaluation of studies that test scientific (not statistical) hypotheses. A scientific hypothesis is a putative explanation for an observation or phenomenon; it makes (or "entails") testable predictions that must be true if the hypothesis is true and that lead to its rejection if they are false. The question is, "how should we judge the strength of a hypothesis that passes a series of experimental tests?" This question is especially relevant in view of the "reproducibility crisis" that is the cause of great unease. Reproducibility is said to be a dire problem because major neuroscience conclusions supposedly rest entirely on the outcomes of single, p valued statistical tests. To investigate this concern, I propose to (1) ask whether neuroscience typically does base major conclusions on single tests; (2) discuss the advantages of testing multiple predictions to evaluate a hypothesis; and (3) review ways in which multiple outcomes can be combined to assess the overall strength of a project that tests multiple predictions of one hypothesis. I argue that scientific hypothesis testing in general, and combining the results of several experiments in particular, may justify placing greater confidence in multiple-testing procedures than in other ways of conducting science.

Children with developmental coordination disorder show altered functional connectivity compared to peers

Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder that affects a child's ability to learn motor skills and participate in self-care, educational, and leisure activities. The cause of DCD is unknown, but evidence suggests that children with DCD have atypical brain structure and function. Resting-state MRI assesses functional connectivity by identifying brain regions that have parallel activation during rest. As only a few studies have examined functional connectivity in this population, our objective was to compare whole-brain resting-state functional connectivity of children with DCD and typically-developing children. Using Independent Component Analysis (ICA), we compared functional connectivity of 8-12 year old children with DCD (N = 35) and typically-developing children (N = 23) across 19 networks, controlling for age and sex. Children with DCD demonstrate altered functional connectivity between the sensorimotor network and the posterior cingulate cortex (PCC), precuneus, and the posterior middle temporal gyrus (pMTG) (p < 0.0001). Previous evidence suggests the PCC acts as a link between functionally distinct networks. Our results indicate that ineffective communication between the sensorimotor network and the PCC might play a role in inefficient motor learning seen in DCD. The pMTG acts as hub for action-related information and processing, and its involvement could explain some of the functional difficulties seen in DCD. This study increases our understanding of the neurological differences that characterize this common motor disorder.

An Assay System to Evaluate Riboflavin/UV-A Corneal Phototherapy Efficacy in a Porcine Corneal Organ Culture Model

Simple summary

The scope of this study is to quantitatively evaluate, with an automated digital image analysis method, the efficacy of riboflavin/UV-A corneal phototherapy on the cornea in a porcine corneal organ culture model of ulcerative melting keratitis. Riboflavin/UV-A corneal phototherapy provided a favorable outcome in the corneal wound healing process after chemical injury: the treatment restores the damaged corneas to the texture of healthy corneas. This automated image analysis method may be compared to clinical diagnostic methods, such as optical coherence tomography (OCT) imaging, for in vivo damaged ocular structural investigations. Positive results from this research could provide an opportunity for studying the effects of this method in other economically and emotionally valued species, such as dogs, cats, and horses. The relatively overall low treatment cost and the ease of performing the procedure make riboflavin/UV-A corneal phototherapy accessible to the veterinary market.

Abstract

The purpose of this study was to investigate the response of porcine corneal organ cultures to riboflavin/UV-A phototherapy in the injury healing of induced lesions. A porcine corneal organ culture model was established. Corneal alterations in the stroma were evaluated using an assay system, based on an automated image analysis method able to (i) localize the holes and gaps within the stroma and (ii) measure the brightness values in these patches. The analysis has been performed by dividing the corneal section in 24 regions of interest (ROIs) and integrating the data analysis with a “multi-aspect approach.” Three group of corneas were analyzed: healthy, injured, and injured-and-treated. Our study revealed a significant effect of the riboflavin/UV-A phototherapy in the injury healing of porcine corneas after induced lesions. The injured corneas had significant differences of brightness values in comparison to treated (p < 0.00) and healthy (p < 0.001) corneas, whereas the treated and healthy corneas showed no significant difference (p = 0.995). Riboflavin/UV-A phototherapy shows a significant effect in restoring the brightness values of damaged corneas to the values of healthy corneas, suggesting treatment restores the injury healing of corneas after lesions. Our assay system may be compared to clinical diagnostic methods, such as optical coherence tomography (OCT) imaging, for in vivo damaged ocular structure investigations.

Neurophysiological correlates of religious coping to stress: a preliminary EEG power spectra investigation

Religious coping to psychological stress has been linked to positive outcomes on both physical and mental health, but no studies have explored its neurophysiological correlates. Ninety-six participants (43 men and 53 women, mean age: 22.30 ± 2.48 years) were enrolled in the present study; they underwent an evaluation of coping with the brief version of the Coping Orientation to Problems Experienced (brief-COPE) scale and performed an eyes-closed resting state electroencephalographic (EEG) recording. EEG analyses were conducted with the exact Low-Resolution Electromagnetic Tomography software (eLORETA). Positive correlations between religious coping and EEG activity were observed in the theta frequency band in the right hemisphere, specifically in the superior temporal, inferior frontal, and middle temporal gyri. Religious coping scores were significantly positively associated with active coping and positive reframing coping strategies, with the latter not being significantly associated with EEG data. Taken together our results contribute to increase the knowledge on the neurophysiological concomitants of religious coping to stress.

White matter structure and myelin-related gene expression alterations with experience in adult rats

White matter (WM) plasticity during adulthood is a recently described phenomenon by which experience can shape brain structure. It has been observed in humans using diffusion tensor imaging (DTI) and myelination has been suggested as a possible mechanism. Here, we set out to identify molecular and cellular changes associated with WM plasticity measured by DTI. We combined DTI, immunohistochemistry and mRNA expression analysis and examined the effects of somatosensory experience in adult rats. First, we observed experience-induced DTI differences in WM and in grey matter structure. C-Fos mRNA expression, a marker of cortical activity, in the barrel cortex correlated with the MRI WM metrics, indicating that molecular correlates of cortical activity relate to macroscale measures of WM structure. Analysis of myelin-related genes revealed higher myelin basic protein (MBP) mRNA expression. Higher MBP protein expression was also found via immunohistochemistry in WM. Finally, unbiased RNA sequencing analysis identified 134 differentially expressed genes encoding proteins involved in functions related to cell proliferation and differentiation, regulation of myelination and neuronal activity modulation. In conclusion, macroscale measures of WM plasticity are supported by both molecular and cellular evidence and confirm that myelination is one of the underlying mechanisms.

Multiple testing correction over contrasts for brain imaging

The multiple testing problem arises not only when there are many voxels or vertices in an image representation of the brain, but also when multiple contrasts of parameter estimates (that represent hypotheses) are tested in the same general linear model. We argue that a correction for this multiplicity must be performed to avoid excess of false positives. Various methods for correction have been proposed in the literature, but few have been applied to brain imaging. Here we discuss and compare different methods to make such correction in different scenarios, showing that one classical and well known method is invalid, and argue that permutation is the best option to perform such correction due to its exactness and flexibility to handle a variety of common imaging situations.

Colocalized White Matter Plasticity and Increased Cerebral Blood Flow Mediate the Beneficial Effect of Cardiovascular Exercise on Long-Term Motor Learning

Cardiovascular exercise (CE) is a promising intervention strategy to facilitate cognition and motor learning in healthy and diseased populations of all ages. CE elevates humoral parameters, such as growth factors, and stimulates brain changes potentially relevant for learning and behavioral adaptations. However, the causal relationship between CE-induced brain changes and human's ability to learn remains unclear. We tested the hypothesis that CE elicits a positive effect on learning via alterations in brain structure (morphological changes of gray and white matter) and function (functional connectivity and cerebral blood flow in resting state). We conducted a randomized controlled trial with healthy male and female human participants to compare the effects of a 2 week CE intervention against a non-CE control group on subsequent learning of a challenging new motor task (dynamic balancing; DBT) over 6 consecutive weeks. We used multimodal neuroimaging [T1-weighted magnetic resonance imaging (MRI), diffusion-weighted MRI, perfusion-weighted MRI, and resting state functional MRI] to investigate the neural mechanisms mediating between CE and learning. As expected, subjects receiving CE subsequently learned the DBT at a higher rate. Using a modified nonparametric combination approach along with multiple mediator analysis, we show that this learning boost was conveyed by CE-induced increases in cerebral blood flow in frontal brain regions and changes in white matter microstructure in frontotemporal fiber tracts. Our study revealed neural mechanisms for the CE-learning link within the brain, probably allowing for a higher flexibility to adapt to highly novel environmental stimuli, such as learning a complex task.SIGNIFICANCE STATEMENT It is established that cardiovascular exercise (CE) is an effective approach to promote learning and memory, yet little is known about the underlying neural transfer mechanisms through which CE acts on learning. We provide evidence that CE facilitates learning in human participants via plasticity in prefrontal white matter tracts and a colocalized increase in cerebral blood flow. Our findings are among the first to demonstrate a transfer potential of experience-induced brain plasticity. In addition to practical implications for health professionals and coaches, our work paves the way for future studies investigating effects of CE in patients suffering from prefrontal hypoperfusion or white matter diseases.

General principles of machine learning for brain-computer interfacing

Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair, typewriting applications, or games directly by modulating our brain activity. For this purpose, BCI systems rely on signal processing and machine learning algorithms to decode the brain activity. This chapter provides an overview of the main steps required to do such a process, including signal preprocessing, feature extraction and selection, and decoding. Given the large amount of possible methods that can be used for these processes, a comprehensive review of them is beyond the scope of this chapter, and it is focused instead on the general principles that should be taken into account, as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems.

Increased Resting State Triple Network Functional Connectivity in Undergraduate Problematic Cannabis Users: A Preliminary EEG Coherence Study

An increasing body of experimental data have suggested that aberrant functional interactions between large-scale networks may be the most plausible explanation of psychopathology across multiple mental disorders, including substance-related and addictive disorders. In the current research, we have investigated the association between problematic cannabis use (PCU) and triple-network electroencephalographic (EEG) functional connectivity. Twelve participants with PCU and 24 non-PCU participants were included in the study. EEG recordings were performed during resting state (RS). The exact Low-Resolution Electromagnetic Tomography software (eLORETA) was used for all EEG analyses. Compared to non-PCU, PCU participants showed an increased delta connectivity between the salience network (SN) and central executive network (CEN), specifically, between the dorsal anterior cingulate cortex and right posterior parietal cortex. The strength of delta connectivity between the SN and CEN was positively and significantly correlated with higher problematic patterns of cannabis use after controlling for age, sex, educational level, tobacco use, problematic alcohol use, and general psychopathology (r_p = 0.40, p = 0.030). Taken together, our results show that individuals with PCU could be characterized by a specific dysfunctional interaction between the SN and CEN during RS, which might reflect the neurophysiological underpinnings of attentional and emotional processes of cannabis-related thoughts, memories, and craving.

Influence of multiple hypothesis testing on reproducibility in neuroimaging research: A simulation study and Python-based software

BACKGROUND

Reproducibility of research findings has been recently questioned in many fields of science, including psychology and neurosciences. One factor influencing reproducibility is the simultaneous testing of multiple hypotheses, which entails false positive findings unless the analyzed p-values are carefully corrected. While this multiple testing problem is well known and studied, it continues to be both a theoretical and practical problem.

NEW METHOD

Here we assess reproducibility in simulated experiments in the context of multiple testing. We consider methods that control either the family-wise error rate (FWER) or false discovery rate (FDR), including techniques based on random field theory (RFT), cluster-mass based permutation testing, and adaptive FDR. Several classical methods are also considered. The performance of these methods is investigated under two different models.

RESULTS

We found that permutation testing is the most powerful method among the considered approaches to multiple testing, and that grouping hypotheses based on prior knowledge can improve power. We also found that emphasizing primary and follow-up studies equally produced most reproducible outcomes.

COMPARISON WITH EXISTING METHOD(S)

We have extended the use of two-group and separate-classes models for analyzing reproducibility and provide a new open-source software "MultiPy" for multiple hypothesis testing.

CONCLUSIONS

Our simulations suggest that performing strict corrections for multiple testing is not sufficient to improve reproducibility of neuroimaging experiments. The methods are freely available as a Python toolkit "MultiPy" and we aim this study to help in improving statistical data analysis practices and to assist in conducting power and reproducibility analyses for new experiments.

White matter integrity differences associated with post-traumatic stress disorder are not normalized by concurrent marijuana use

Marijuana (MJ) use and post-traumatic stress disorder (PTSD) have both been associated with abnormalities in brain white matter tracts, including the cingulum and the anterior thalamic radiations (ATR), which project from subcortical regions to frontal cortex. Studies have not assessed the integrity of these tracts in patients with comorbid PTSD and MJ use. To examine effects of PTSD and MJ use on brain structure, we performed diffusion tensor imaging scans on seventy-two trauma-exposed participants, categorized into four groups: those with PTSD who used MJ at least weekly (PTSD+MJ; n = 20), those with PTSD with no regular MJ use (PTSD; n = 19), trauma-exposed controls without PTSD who used MJ (TEC+MJ; n = 14) and trauma-exposed controls with no PTSD or MJ use (TEC; n = 19). White matter integrity was evaluated by calculating fractional anisotropy (FA). Results showed that while FA values in the right ATR and the cingulum differed across groups, there were no significant interactions between PTSD and MJ in any white matter tracts, indicating that MJ exposure neither normalizes nor worsens white matter abnormalities in those with PTSD. Further study is needed to evaluate the impact of MJ use on other neurobiological markers of PTSD.

Gray matter atrophy cannot be fully explained by white matter damage in patients with MS

BACKGROUND

Source-based morphometry (SBM) was recently used for non-random "patterns" of gray matter (GM) atrophy or white matter (WM) microstructural damage.

OBJECTIVE

To assess whether and to what extent such patterns may be inter-related in MS.

METHODS

SBM was applied to images of GM concentration and fractional anisotropy (FA) in MS patients (n = 41, median EDSS = 1) and normal controls (NC, n = 28). The same procedure was repeated on an independent and similar data set (39 MS patients and 13 NC).

RESULTS

We found in MS patterns of GM atrophy and reduced FA (p < 0.05, corrected). Deep GM atrophy was mostly (70%) explained by lesion load in projection tracts and lower FA in posterior corona radiata and thalamic radiation. By contrast, sensorimotor and posterior cortex atrophy was less (50%) dependent from WM damage. All patterns correlated with EDSS (r from -0.33 to -0.56, p < 0.03) while the only cognition-related correlation was between posterior GM atrophy pattern and processing speed (r = 0.45, p = 0.014). Reliability analysis showed similar results.

CONCLUSION

In relatively early MS, we found a close link between deep GM atrophy pattern and WM damage while sensorimotor and posterior cortex patterns were partially independent from WM damage and perhaps related to primary mechanisms. Patterns were clinically relevant.

Combining fMRI during resting state and an attention bias task in children

Neuroimaging studies typically focus on either resting state or task-based fMRI data. Prior research has shown that similarity in functional connectivity between rest and cognitive tasks, interpreted as reconfiguration efficiency, is related to task performance and IQ. Here, we extend this approach from adults to children, and from cognitive tasks to a threat-based attention task. The goal of the current study was to examine whether similarity in functional connectivity during rest and an attention bias task relates to threat bias, IQ, anxiety symptoms, and social reticence. fMRI was measured during resting state and during the dot-probe task in 41 children (M = 13.44, SD = 0.70). Functional connectivity during rest and dot-probe was positively correlated, suggesting that functional hierarchies in the brain are stable. Similarity in functional connectivity between rest and the dot-probe task only related to threat bias (puncorr < .03). This effect did not survive correction for multiple testing. Overall, children who allocate more attention towards threat also may possess greater reconfiguration efficiency in switching from intrinsic to threat-related attention states. Finally, functional connectivity correlated negatively across the two conditions of the dot-probe task. Opposing patterns of modulation of functional connectivity by threat-congruent and threat-incongruent trials may reflect task-specific network changes during two different attentional processes.

Converging patterns of aging-associated brain volume loss and tissue microstructure differences

Given the worldwide increasing socioeconomic burden of aging-associated brain diseases, there is pressing need to gain in-depth knowledge about the neurobiology of brain anatomy changes across the life span. Advances in quantitative magnetic resonance imaging sensitive to brain's myelin, iron, and free water content allow for a detailed in vivo investigation of aging-related changes while reducing spurious morphometry differences. Main aim of our study is to link previous morphometry findings in aging to microstructural tissue properties in a large-scale cohort (n = 966, age range 46-86 y). Addressing previous controversies in the field, we present results obtained with different approaches to adjust local findings for global effects. Beyond the confirmation of age-related atrophy, myelin, and free water decreases, we report proportionally steeper volume, iron, and myelin decline in sensorimotor and subcortical areas paralleled by free water increase. We demonstrate aging-related white matter volume, myelin, and iron loss in frontostriatal projections. Our findings provide robust evidence for spatial overlap between volume and tissue property differences in aging that affect predominantly motor and executive networks.

Replicable brain signatures of emotional bias and memory based on diffusion kurtosis imaging of white matter tracts

Diffusion MRI (dMRI) is sensitive to anisotropic diffusion within bundles of nerve axons and can be used to make objective measurements of brain networks. Many brain disorders are now recognised as being caused by network dysfunction or are secondarily associated with changes in networks. There is therefore great potential in using dMRI measures that reflect network integrity as a future clinical tool to help manage these conditions. Here, we used dMRI to identify replicable, robust and objective markers that meaningfully reflect cognitive and emotional performance. Using diffusion kurtosis analysis and a battery of cognitive and emotional tests, we demonstrated strong relationships between white matter structure across networks of anatomically and functionally specific brain regions with both emotional bias and emotional memory performance in a large healthy cohort. When the connectivity of these regions was examined using diffusion tractography, the terminations of the identified tracts overlapped precisely with cortical loci relating to these domains, drawn from an independent spatial meta‐analysis of available functional neuroimaging literature. The association with emotional bias was then replicated using an independently acquired healthy cohort drawn from the Human Connectome Project. These results demonstrate that, even in healthy individuals, white matter dMRI structural features underpin important cognitive and emotional functions. Our robust cross‐correlation and replication supports the potential of structural brain biomarkers from diffusion kurtosis MRI to characterise early neurological changes and risk in individuals with a reduced threshold for cognitive dysfunction, with further testing required to demonstrate clinical utility.

Motion sickness-susceptible participants exposed to coherent rotating dot patterns show excessive N2 amplitudes and impaired theta-band phase synchronization

Visually induced motion sickness (VIMS) can occur via prolonged exposure to visual stimulation that generates the illusion of self-motion (vection). Not everyone is susceptible to VIMS and the neural mechanism underlying susceptibility is unclear. This study explored the differences of electroencephalographic (EEG) signatures between VIMS-susceptible and VIMS-resistant groups. Thirty-two-channel EEG data were recorded from 12 VIMS-susceptible and 15 VIMS-resistant university students while they were watching two patterns of moving dots: (1) a coherent rotation pattern (vection-inducing and potentially VIMS-provoking pattern), and (2) a random movement pattern (non-VIMS-provoking control). The VIMS-susceptible group exhibited a significantly larger increase in the parietal N2 response when exposed to the coherent rotating pattern than when exposed to control patterns. In members of the VIMS-resistant group, before vection onset, global connectivity from all other EEG electrodes to the right-temporal-parietal and to the right-central areas increased, whereas after vection onset the global connectivity to the right-frontal area reduced. Such changes were not observed in the susceptible group. Further, the increases in N2 amplitude and the identified phase synchronization index were significantly correlated with individual motion sickness susceptibility. Results suggest that VIMS susceptibility is associated with systematic impairment of dynamic cortical coordination as captured by the phase synchronization of cortical activities. Analyses of dynamic EEG signatures could be a means to unlock the neural mechanism of VIMS.

Default Mode Network Lateralization and Memory in Healthy Aging and Alzheimer's Disease

Lateralization of default mode network (DMN) functioning has been shown to change with age. Similarly, lateralization of frontal lobe function has been shown to decline in age. The impact of amyloid pathology and the progression of Alzheimer's disease (AD) on resting state lateralization has not been investigated. Due to the preferential involvement of the left hemisphere in verbal tasks, there may be a benefit to higher levels of left-lateralization in the performance of verbal memory tasks. Here we compared functional lateralization of the anterior and posterior DMN between four groups of participants: amyloid negative (Aβ-) and amyloid positive (Aβ+) groups with normal cognition (NC), and Aβ+ groups with mild cognitive impairment (Aβ+MCI) or dementia (Aβ+AD). Differences were evident between groups in posterior DMN; the Aβ-NC group was more left-lateralized than both cognitively impaired Aβ+ groups. There was no difference in anterior DMN. No differences in overall network connectivity between groups were observed, suggesting that the functional lateralization finding is not secondary to general changes in connectivity. Left-lateralization of both networks was associated with better verbal recall performance. Older subjects, overall, had less left functional lateralization of the anterior DMN.

Robust nonparametric tests of general linear model coefficients: A comparison of permutation methods and test statistics

Statistical inference in neuroimaging research often involves testing the significance of regression coefficients in a general linear model. In many applications, the researcher assumes a model of the form Y=α+Xβ+Zγ+ε, where Y is the observed brain signal, and X and Z contain explanatory variables that are thought to be related to the brain signal. The goal is to test the null hypothesis H₀:β=0 with the nuisance parameters γ included in the model. Several nonparametric (permutation) methods have been proposed for this problem, and each method uses some variant of the F ratio as the test statistic. However, recent research suggests that the F ratio can produce invalid permutation tests of H₀:β=0 when the ε terms are heteroscedastic (i.e., have non-constant variance), which can occur for a variety of reasons. This study compares the classic F test statistic to the robust W (Wald) test statistic using eight different permutation methods. The results reveal that permutation tests using the F ratio can produce accurate results when the errors are homoscedastic, but high false positive rates when the errors are heteroscedastic. In contrast, permutation tests using the W test statistic produced valid results when the errors were homoscedastic, and asymptotically valid results when the errors were heteroscedastic. In the situation with homoscedastic errors, permutation tests using the W statistic showed slightly reduced power compared to the F statistic, but the difference disappeared as the sample size n increased. Consequently, the W test statistic is recommended for robust nonparametric hypothesis tests of regression coefficients in neuroimaging research.

Interindividual differences in gray and white matter properties are associated with early complex motor skill acquisition

Brain circuits mediate but also constrain experience-induced plasticity and corresponding behavioral changes. Here we tested whether interindividual behavioral differences in learning a challenging new motor skill correlate with variations in brain anatomy. Young, healthy participants were scanned using structural magnetic resonance imaging (T1-weighted MPRAGE, n = 75 and/or diffusion-weighted MRI, n = 59) and practiced a complex whole-body balancing task on a seesaw-like platform. Using conjunction tests based on the nonparametric combination (NPC) methodology, we found that gray matter volume (GMV) in the right orbitrofrontal cortex was positively related to the subjects' initial level of proficiency and their ability to improve performance during practice. Similarly, we obtained a strong trend toward a positive correlation between baseline fractional anisotropy (FA) in commissural prefrontal fiber pathways and later motor learning. FA results were influenced more strongly by radial than axial diffusivity. However, we did not find unique anatomical correlates of initial performance and learning to rate. Our findings reveal structural predispositions for successful motor skill performance and acquisition in frontal brain structures and underlying frontal white matter tracts. Together with previous results, these findings support the view that structural constraints imposed by the brain determine subsequent behavioral success and underline the importance of structural brain network constitution before learning starts.

Statistical Challenges in "Big Data" Human Neuroimaging

Smith and Nichols discuss "big data" human neuroimaging studies, with very large subject numbers and amounts of data. These studies provide great opportunities for making new discoveries about the brain but raise many new analytical challenges and interpretational risks.

Towards Algorithmic Analytics for Large-scale Datasets

The traditional goals of quantitative analytics cherish simple, transparent models to generate explainable insights. Large-scale data acquisition, enabled for instance by brain scanning and genomic profiling with microarray-type techniques, has prompted a wave of statistical inventions and innovative applications. Modern analysis approaches 1) tame large variable arrays capitalizing on regularization and dimensionality-reduction strategies, 2) are increasingly backed up by empirical model validations rather than justified by mathematical proofs, 3) will compare against and build on open data and consortium repositories, as well as 4) often embrace more elaborate, less interpretable models in order to maximize prediction accuracy. Here we review these trends in learning from "big data" and illustrate examples from imaging neuroscience.

Cholinergic and serotonergic modulation of resting state functional brain connectivity in Alzheimer's disease

Disruption of cholinergic and serotonergic neurotransmitter systems is associated with cognitive, emotional and behavioural symptoms of Alzheimer's disease (AD). To investigate the responsiveness of these systems in AD we measured the effects of a single-dose of the selective serotonin reuptake inhibitor citalopram and acetylcholinesterase inhibitor galantamine in 12 patients with AD and 12 age-matched controls on functional brain connectivity with resting state functional magnetic resonance imaging. In this randomized, double blind, placebo-controlled crossover study, functional magnetic resonance images were repeatedly obtained before and after dosing, resulting in a dataset of 432 scans. Connectivity maps of ten functional networks were extracted using a dual regression method and drug vs. placebo effects were compared between groups with a multivariate analysis with signals coming from cerebrospinal fluid and white matter as covariates at the subject level, and baseline and heart rate measurements as confound regressors in the higher-level analysis (at p < 0.05, corrected). A galantamine induced difference between groups was observed for the cerebellar network. Connectivity within the cerebellar network and between this network and the thalamus decreased after galantamine vs. placebo in AD patients, but not in controls. For citalopram, voxelwise network connectivity did not show significant group × treatment interaction effects. However, we found default mode network connectivity with the precuneus and posterior cingulate cortex to be increased in AD patients, which could not be detected within the control group. Further, in contrast to the AD patients, control subjects showed a consistent reduction in mean connectivity with all networks after administration of citalopram. Since AD has previously been characterized by reduced connectivity between the default mode network and the precuneus and posterior cingulate cortex, the effects of citalopram on the default mode network suggest a restoring potential of selective serotonin reuptake inhibitors in AD. The results of this study also confirm a change in cerebellar connections in AD, which is possibly related to cholinergic decline.

Joint Analysis of Cortical Area and Thickness as a Replacement for the Analysis of the Volume of the Cerebral Cortex

Cortical surface area is an increasingly used brain morphology metric that is ontogenetically and phylogenetically distinct from cortical thickness and offers a separate index of neurodevelopment and disease. However, the various existing methods for assessment of cortical surface area from magnetic resonance images have never been systematically compared. We show that the surface area method implemented in FreeSurfer corresponds closely to the exact, but computationally more demanding, mass-conservative (pycnophylactic) method, provided that images are smoothed. Thus, the data produced by this method can be interpreted as estimates of cortical surface area, as opposed to areal expansion. In addition, focusing on the joint analysis of thickness and area, we compare an improved, analytic method for measuring cortical volume to a permutation-based nonparametric combination (NPC) method. We use the methods to analyze area, thickness and volume in young adults born preterm with very low birth weight, and show that NPC analysis is a more sensitive option for studying joint effects on area and thickness, giving equal weight to variation in both of these 2 morphological features.

Parsing neurodevelopmental features of irritability and anxiety: Replication and validation of a latent variable approach

Irritability and anxiety are two common clinical phenotypes that involve high-arousal negative affect states (anger and fear), and that frequently co-occur. Elucidating how these two forms of emotion dysregulation relate to perturbed neurodevelopment may benefit from alternate phenotyping strategies. One such strategy applies a bifactor latent variable approach that can parse shared versus unique mechanisms of these two phenotypes. Here, we aim to replicate and extend this approach and examine associations with neural structure in a large transdiagnostic sample of youth (N = 331; M = 13.57, SD = 2.69 years old; 45.92% male). FreeSurfer was used to extract cortical thickness, cortical surface area, and subcortical volume. The current findings replicated the bifactor model and demonstrate measurement invariance as a function of youth age and sex. There were no associations of youth's factor scores with cortical thickness, surface area, or subcortical volume. However, we found strong convergent and divergent validity between parent-reported irritability and anxiety factors with clinician-rated symptoms and impairment. A general negative affectivity factor was robustly associated with overall functional impairment across symptom domains. Together, these results support the utility of the bifactor model as an alternative phenotyping strategy for irritability and anxiety, which may aid in the development of targeted treatments.

Short-term meditation modulates EEG activity in subjects with post-traumatic residual disabilities

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We aimed to detect EEG changes induced by meditative interventions in PTRD subjects.

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PTRD subjects exhibited increased gamma activity in the IPL relative to controls.

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Changes of delta activity in the precuneus correlated with changes of the QOL scale.

Objective

Neurophysiological changes related to meditation have recently attracted scientific attention. We aimed to detect changes in electroencephalography (EEG) parameters induced by a meditative intervention in subjects with post-traumatic residual disability (PTRD), which has been confirmed for effectiveness and safety in a previous study. This will allow us to estimate the objective effect of this intervention at the neurophysiological level.

Methods

Ten subjects with PTRD were recruited and underwent psychological assessment and EEG recordings before and after the meditative intervention. Furthermore, 10 additional subjects were recruited as normal controls. Source current density as an EEG parameter was estimated by exact Low Resolution Electromagnetic Tomography (eLORETA). Comparisons of source current density in PTRD subjects after the meditative intervention with normal controls were investigated. Additionally, we compared source current density in PTRD subjects between before and after meditative intervention. Correlations between psychological assessments and source current density were also explored.

Results

After meditative intervention, PTRD subjects exhibited increased gamma activity in the left inferior parietal lobule relative to normal controls. In addition, changes of delta activity in the right precuneus correlated with changes in the psychological score on role physical item, one of the quality of life scales reflecting the work or daily difficulty due to physical problems.

Conclusions

These results show that the meditative intervention used in this study produces neurophysiological changes, in particular the modulation of oscillatory activity of the brain.

Significance

Our meditative interventions might induce the neurophysiological changes associated with the improvement of psychological symptoms in the PTRD subjects.

Acute tryptophan loading decreases functional connectivity between the default mode network and emotion-related brain regions

It has been shown that the functional architecture of the default mode network (DMN) can be affected by serotonergic challenges and these effects may provide insights on the neurobiological bases of depressive symptomatology. To deepen our understanding of this possible interplay, we used a double-blind, randomized, cross-over design, with a control condition and two interventions to decrease (tryptophan depletion) and increase (tryptophan loading) brain serotonin synthesis. Resting-state fMRI from 85 healthy subjects was acquired for all conditions 3 hr after the ingestion of an amino acid mixture containing different amounts of tryptophan, the dietary precursor of serotonin. The DMN was derived for each participant and session. Permutation testing was performed to detect connectivity changes within the DMN as well as between the DMN and other brain regions elicited by the interventions. We found that tryptophan loading increased tryptophan plasma levels and decreased DMN connectivity with visual cortices and several brain regions involved in emotion and affect regulation (i.e., putamen, subcallosal cortex, thalamus, and frontal cortex). Tryptophan depletion significantly reduced tryptophan levels but did not affect brain connectivity. Subjective ratings of mood, anxiety, sleepiness, and impulsive choice were not strongly affected by any intervention. Our data indicate that connectivity between the DMN and emotion-related brain regions might be modulated by changes in the serotonergic system. These results suggest that functional changes in the brain associated with different brain serotonin levels may be relevant to understand the neural bases of depressive symptoms.

Characterisation of brain volume and microstructure at term-equivalent age in infants born across the gestational age spectrum

BACKGROUND

Risk of morbidity differs between very preterm (VP; <32 weeks' gestational age (GA)), moderate preterm (MP; 32-33 weeks' GA), late preterm (LP; 34-36 weeks' GA), and full-term (FT; ≥37 weeks' GA) infants. However, brain structure at term-equivalent age (TEA; 38-44 weeks) remains to be characterised in all clinically important GA groups. We aimed to compare global and regional brain volumes, and regional white matter microstructure, between VP, MP, LP and FT groups at TEA, in order to establish the magnitude and anatomical locations of between-group differences.

METHODS

Structural images from 328 infants (91 VP, 63 MP, 104 LP and 70 FT) were segmented into white matter, cortical grey matter, cerebrospinal fluid (CSF), subcortical grey matter, brainstem and cerebellum. Global tissue volumes were analysed, and additionally, cortical grey matter and white matter volumes were analysed at the regional level using voxel-based morphometry. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) images from 361 infants (92 VP, 69 MP, 120 LP and 80 FT) were analysed using Tract-Based Spatial Statistics. Statistical analyses involved examining the overall effect of GA group on global volumes (using linear regressions) and regional volumes and microstructure (using non-parametric permutation testing), as well performing post-hoc comparisons between the GA sub-groups.

RESULTS

On global analysis, cerebrospinal fluid (CSF) volume was larger in all preterm sub-groups compared with the FT group. On regional analysis, volume was smaller in parts of the temporal cortical grey matter, and parts of the temporal white matter and corpus callosum, in all preterm sub-groups compared with the FT group. FA was lower, and RD and MD were higher in voxels located in much of the white matter in all preterm sub-groups compared with the FT group. The anatomical locations of group differences were similar for each preterm vs. FT comparison, but the magnitude and spatial extent of group differences was largest for the VP, followed by the MP, and then the LP comparison. Comparing within the preterm groups, the VP sub-group had smaller frontal and temporal grey and white matter volume, and lower FA and higher MD and RD within voxels in the approximate location of the corpus callosum compared with the MP sub-group. There were few volume and microstructural differences between the MP and LP sub-groups.

CONCLUSION

All preterm sub-groups had atypical brain volume and microstructure at TEA when compared with a FT group, particularly for the CSF, temporal grey and white matter, and corpus callosum. In general, the groups followed a gradient, where the differences were most pronounced for the VP group, less pronounced for the MP group, and least pronounced for the LP group. The VP sub-group was particularly vulnerable compared with the MP and LP sub-groups.

Fast track to the neocortex: A memory engram in the posterior parietal cortex

Models of systems memory consolidation postulate a fast-learning hippocampal store and a slowly developing, stable neocortical store. Accordingly, early neocortical contributions to memory are deemed to reflect a hippocampus-driven online reinstatement of encoding activity. In contrast, we found that learning rapidly engenders an enduring memory engram in the human posterior parietal cortex. We assessed microstructural plasticity via diffusion-weighted magnetic resonance imaging as well as functional brain activity in an object–location learning task. We detected neocortical plasticity as early as 1 hour after learning and found that it was learning specific, enabled correct recall, and overlapped with memory-related functional activity. These microstructural changes persisted over 12 hours. Our results suggest that new traces can be rapidly encoded into the parietal cortex, challenging views of a slow-learning neocortex.

Partially impaired functional connectivity states between right anterior insula and default mode network in autism spectrum disorder

Time-invariant resting-state functional connectivity studies have illuminated the crucial role of the right anterior insula (rAI) in prominent social impairments of autism spectrum disorder (ASD). However, a recent dynamic connectivity study demonstrated that rather than being stationary, functional connectivity patterns of the rAI vary significantly across time. The present study aimed to explore the differences in functional connectivity in dynamic states of the rAI between individuals with ASD and typically developing controls (TD). Resting-state functional magnetic resonance imaging data obtained from a publicly available database were analyzed in 209 individuals with ASD and 298 demographically matched controls. A k-means clustering algorithm was utilized to obtain five dynamic states of functional connectivity of the rAI. The temporal properties, frequency properties, and meta-analytic decoding were first identified in TD group to obtain the characteristics of each rAI dynamic state. Multivariate analysis of variance was then performed to compare the functional connectivity patterns of the rAI between ASD and TD groups in obtained states. Significantly impaired connectivity was observed in ASD in the ventral medial prefrontal cortex and posterior cingulate cortex, which are two critical hubs of the default mode network (DMN). States in which ASD showed decreased connectivity between the rAI and these regions were those more relevant to socio-cognitive processing. From a dynamic perspective, these findings demonstrate partially impaired resting-state functional connectivity patterns between the rAI and DMN across states in ASD, and provide novel insights into the neural mechanisms underlying social impairments in individuals with ASD.

Stable between-subject statistical inference from unstable within-subject functional connectivity estimates

Spatial or temporal aspects of neural organization are known to be important indices of how cognition is organized. However, measurements and estimations are often noisy and many of the algorithms used are probabilistic, which in combination have been argued to limit studies exploring the neural basis of specific aspects of cognition. Focusing on static and dynamic functional connectivity estimations, we propose to leverage this variability to improve statistical efficiency in relating these estimations to behavior. To achieve this goal, we use a procedure based on permutation testing that provides a way of combining the results from many individual tests that refer to the same hypothesis. This is needed when testing a measure whose value is obtained from a noisy process, which can be repeated multiple times, referred to as replications. Focusing on functional connectivity, this noisy process can be: (a) computational, for example, when using an approximate inference algorithm for which different runs can produce different results or (b) observational, if we have the capacity to acquire data multiple times, and the different acquired data sets can be considered noisy examples of some underlying truth. In both cases, we are not interested in the individual replications but on the unobserved process generating each replication. In this note, we show how results can be combined instead of choosing just one of the estimated models. Using both simulations and real data, we show the benefits of this approach in practice.

LISA improves statistical analysis for fMRI

One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. Here, we propose a non-parametric and threshold-free framework called LISA to address this demand. It uses a non-linear filter for incorporating spatial context without sacrificing spatial precision. Multiple comparison correction is achieved by controlling the false discovery rate in the filtered maps. Compared to widely used other methods, it shows a boost in statistical power and allows to find small activation areas that have previously evaded detection. The spatial sensitivity of LISA makes it especially suitable for the analysis of high-resolution fMRI data acquired at ultrahigh field (≥7 Tesla).

Quantitative EEG Tomography of Early Childhood Malnutrition

The goal of this study is to identify the quantitative electroencephalographic (qEEG) signature of early childhood malnutrition [protein-energy malnutrition (PEM)]. To this end, archival digital EEG recordings of 108 participants in the Barbados Nutrition Study (BNS) were recovered and cleaned of artifacts (46 children who suffered an episode of PEM limited to the first year of life) and 62 healthy controls). The participants of the still ongoing BNS were initially enrolled in 1973, and EEGs for both groups were recorded in 1977-1978 (at 5-11 years). Scalp and source EEG Z-spectra (to correct for age effects) were obtained by comparison with the normative Cuban Human Brain Mapping database. Differences between both groups in the z spectra (for all electrode locations and frequency bins) were assessed by t-tests with thresholds corrected for multiple comparisons by permutation tests. Four clusters of differences were found: (a) increased theta activity (3.91-5.86 Hz) in electrodes T4, O2, Pz and in the sources of the supplementary motor area (SMA); b) decreased alpha1 (8.59-8.98 Hz) in Fronto-central electrodes and sources of widespread bilateral prefrontal are; (c) increased alpha2 (11.33-12.50 Hz) in Temporo-parietal electrodes as well as in sources in Central-parietal areas of the right hemisphere; and (d) increased beta (13.67-18.36 Hz), in T4, T5 and P4 electrodes and decreased in the sources of bilateral occipital-temporal areas. Multivariate Item Response Theory of EEGs scored visually by experts revealed a neurophysiological latent variable which indicated excessive paroxysmal and focal abnormality activity in the PEM group. A robust biomarker construction procedure based on elastic-net regressions and 1000-cross-validations was used to: (i) select stable variables and (ii) calculate the area under ROC curves (AUC). Thus, qEEG differentiate between the two nutrition groups (PEM vs Control) performing as well as visual inspection of the EEG scored by experts (AUC = 0.83). Since PEM is a global public health problem with lifelong neurodevelopmental consequences, our finding of consistent differences between PEM and controls, both in qualitative and quantitative EEG analysis, suggest that this technology may be a source of scalable and affordable biomarkers for assessing the long-term brain impact of early PEM.

Effects of SYN1Q555X mutation on cortical gray matter microstructure

A new Q555X mutation on the SYN1 gene was recently found in several members of a family segregating dyslexia, epilepsy, and autism spectrum disorder. To describe the effects of this mutation on cortical gray matter microstructure, we performed a surface-based group study using novel diffusion and quantitative multiparametric imaging on 13 SYN1Q555X mutation carriers and 13 age- and sex-matched controls. Specifically, diffusion kurtosis imaging (DKI) and neurite orientation and dispersion and density imaging (NODDI) were used to analyze multi-shell diffusion data and obtain parametric maps sensitive to tissue structure, while quantitative metrics sensitive to tissue composition (T1, T2* and relative proton density [PD]) were obtained from a multi-echo variable flip angle FLASH acquisition. Results showed significant microstructural alterations in several regions usually involved in oral and written language as well as dyslexia. The most significant changes in these regions were lowered mean diffusivity and increased fractional anisotropy. This study is, to our knowledge, the first to successfully use diffusion imaging and multiparametric mapping to detect cortical anomalies in a group of subjects with a well-defined genotype linked to language impairments, epilepsy and autism spectrum disorder (ASD).

Thalamic white matter in multiple sclerosis: A combined diffusion-tensor imaging and quantitative susceptibility mapping study

Thalamic white matter (WM) injury in multiple sclerosis (MS) remains relatively poorly understood. Combining multiple imaging modalities, sensitive to different tissue properties, may aid in further characterizing thalamic damage. Forty-five MS patients and 17 demographically-matched healthy controls (HC) were scanned with 3T MRI to obtain quantitative measures of diffusivity and magnetic susceptibility. Participants underwent cognitive evaluation with the Brief International Cognitive Assessment for Multiple Sclerosis battery. Tract-based spatial statistics identified thalamic WM. Non-parametric combination (NPC) analysis was used to perform joint inference on fractional anisotropy (FA), mean diffusivity (MD) and magnetic susceptibility measures. The association of surrounding WM lesions and thalamic WM pathology was investigated with lesion probability mapping. Compared to HCs, the greatest extent of thalamic WM damage was reflected by the combination of increased MD and decreased magnetic susceptibility (63.0% of thalamic WM, peak p = .001). Controlling for thalamic volume resulted in decreased FA and magnetic susceptibility (34.1%, peak p = .004) as showing the greatest extent. In MS patients, the most widespread association with information processing speed was found with the combination of MD and magnetic susceptibility (67.6%, peak p = .0005), although this was not evident after controlling for thalamic volume. For memory measures, MD alone yielded the most widespread associations (45.9%, peak p = .012 or 76.7%, peak p = .001), even after considering thalamic volume, albeit with smaller percentages. White matter lesions were related to decreased FA (peak p = .0063) and increased MD (peak p = .007), but not magnetic susceptibility, of thalamic WM. Our study highlights the complex nature of thalamic pathology in MS.

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal FWERs. Those rejected clusters were outlying values in the distribution of cluster size but cannot be distinguished from true positive findings without further analyses, including assessing whether fMRI signal in those regions correlates with other clinical, behavioral, or cognitive measures. Rejecting the large clusters, however, significantly reduced the statistical power of nonparametric methods in detecting true findings compared with parametric methods, which would have detected most true findings that are essential for making valid biological inferences in MRI data. Parametric analyses, in contrast, detected most true findings while generating relatively few false positives: on average, less than one of those very large clusters would be deemed a true finding in each brain-wide analysis. We therefore recommend the continued use of parametric methods that model nonstationary smoothness for cluster-level, familywise control of false positives, particularly when using a Cluster Defining Threshold of 2.5 or higher, and subsequently assessing rigorously the biological plausibility of the findings, even for large clusters. Finally, because nonparametric methods yielded a large reduction in statistical power to detect true positive findings, we conclude that the modest reduction in false positive findings that nonparametric analyses afford does not warrant a re-analysis of previously published fMRI studies using nonparametric techniques.

Serotonergic and cholinergic modulation of functional brain connectivity: A comparison between young and older adults

Aging is accompanied by changes in neurotransmission. To advance our understanding of how aging modifies specific neural circuitries, we examined serotonergic and cholinergic stimulation with resting state functional magnetic resonance imaging (RS-fMRI) in young and older adults. The instant response to the selective serotonin reuptake inhibitor citalopram (30 mg) and the acetylcholinesterase inhibitor galantamine (8 mg) was measured in 12 young and 17 older volunteers during a randomized, double blind, placebo-controlled, crossover study. A powerful dataset consisting of 522 RS-fMRI scans was obtained by acquiring multiple scans per subject before and after drug administration. Group × treatment interaction effects on voxelwise connectivity with ten functional networks were investigated (p < .05, FWE-corrected) using a non-parametric multivariate analysis technique with cerebrospinal fluid, white matter, heart rate and baseline measurements as covariates. Both groups showed a decrease in sensorimotor network connectivity after citalopram administration. The comparable findings after citalopram intake are possibly due to relatively similar serotonergic systems in the young and older subjects. Galantamine altered connectivity between the occipital visual network and regions that are implicated in learning and memory in the young subjects. The lack of a cholinergic response in the elderly might relate to the well-known association between cognitive and cholinergic deterioration at older age.

Effects of Field-Map Distortion Correction on Resting State Functional Connectivity MRI

Magnetic field inhomogeneities cause geometric distortions of echo planar images used for functional magnetic resonance imaging (fMRI). To reduce this problem, distortion correction (DC) with field map is widely used for both task and resting-state fMRI (rs-fMRI). Although DC with field map has been reported to improve the quality of task fMRI, little is known about its effects on rs-fMRI. Here, we tested the influence of field-map DC on rs-fMRI results using two rs-fMRI datasets derived from 40 healthy subjects: one with DC (DC+) and the other without correction (DC−). Independent component analysis followed by the dual regression approach was used for evaluation of resting-state functional connectivity networks (RSN). We also obtained the ratio of low-frequency to high-frequency signal power (0.01–0.1 Hz and above 0.1 Hz, respectively; LFHF ratio) to assess the quality of rs-fMRI signals. For comparison of RSN between DC+ and DC− datasets, the default mode network showed more robust functional connectivity in the DC+ dataset than the DC− dataset. Basal ganglia RSN showed some decreases in functional connectivity primarily in white matter, indicating imperfect registration/normalization without DC. Supplementary seed-based and simulation analyses supported the utility of DC. Furthermore, we found a higher LFHF ratio after field map correction in the anterior cingulate cortex, posterior cingulate cortex, ventral striatum, and cerebellum. In conclusion, field map DC improved detection of functional connectivity derived from low-frequency rs-fMRI signals. We encourage researchers to include a DC step in the preprocessing pipeline of rs-fMRI analysis.

Differential inter-subject correlation of brain activity when kinship is a variable in moral dilemma

Previous behavioural studies have shown that humans act more altruistically towards kin. Whether and how knowledge of genetic relatedness translates into differential neurocognitive evaluation of observed social interactions has remained an open question. Here, we investigated how the human brain is engaged when viewing a moral dilemma between genetic vs. non-genetic sisters. During functional magnetic resonance imaging, a movie was shown, depicting refusal of organ donation between two sisters, with subjects guided to believe the sisters were related either genetically or by adoption. Although 90% of the subjects self-reported that genetic relationship was not relevant, their brain activity told a different story. Comparing correlations of brain activity across all subject pairs between the two viewing conditions, we found significantly stronger inter-subject correlations in insula, cingulate, medial and lateral prefrontal, superior temporal, and superior parietal cortices, when the subjects believed that the sisters were genetically related. Cognitive functions previously associated with these areas include moral and emotional conflict regulation, decision making, and mentalizing, suggesting more similar engagement of such functions when observing refusal of altruism from a genetic sister. Our results show that mere knowledge of a genetic relationship between interacting persons robustly modulates social cognition of the perceiver.

Identifying functional network changing patterns in individuals at clinical high-risk for psychosis and patients with early illness schizophrenia: A group ICA study

Although individuals at clinical high risk (CHR) for psychosis exhibit a psychosis-risk syndrome involving attenuated forms of the positive symptoms typical of schizophrenia (SZ), it remains unclear whether their resting-state brain intrinsic functional networks (INs) show attenuated or qualitatively distinct patterns of functional dysconnectivity relative to SZ patients. Based on resting-state functional magnetic imaging data from 70 healthy controls (HCs), 53 CHR individuals (among which 41 subjects were antipsychotic medication-naive), and 58 early illness SZ (ESZ) patients (among which 53 patients took antipsychotic medication) within five years of illness onset, we estimated subject-specific INs using a novel group information guided independent component analysis (GIG-ICA) and investigated group differences in INs. We found that when compared to HCs, both CHR and ESZ groups showed significant differences, primarily in default mode, salience, auditory-related, visuospatial, sensory-motor, and parietal INs. Our findings suggest that widespread INs were diversely impacted. More than 25% of voxels in the identified significant discriminative regions (obtained using all 19 possible changing patterns excepting the no-difference pattern) from six of the 15 interrogated INs exhibited monotonically decreasing Z-scores (in INs) from the HC to CHR to ESZ, and the related regions included the left lingual gyrus of two vision-related networks, the right postcentral cortex of the visuospatial network, the left thalamus region of the salience network, the left calcarine region of the fronto-occipital network and fronto-parieto-occipital network. Compared to HCs and CHR individuals, ESZ patients showed both increasing and decreasing connectivity, mainly hypo-connectivity involving 15% of the altered voxels from four INs. The left supplementary motor area from the sensory-motor network and the right inferior occipital gyrus in the vision-related network showed a common abnormality in CHR and ESZ groups. Some brain regions also showed a CHR-unique alteration (primarily the CHR-increasing connectivity). In summary, CHR individuals generally showed intermediate connectivity between HCs and ESZ patients across multiple INs, suggesting that some dysconnectivity patterns evident in ESZ predate psychosis in attenuated form during the psychosis risk stage. Hence, these connectivity measures may serve as possible biomarkers to predict schizophrenia progression.

High-Level Prediction Signals in a Low-Level Area of the Macaque Face-Processing Hierarchy

Theories like predictive coding propose that lower-order brain areas compare their inputs to predictions derived from higher-order representations and signal their deviation as a prediction error. Here, we investigate whether the macaque face-processing system, a three-level hierarchy in the ventral stream, employs such a coding strategy. We show that after statistical learning of specific face sequences, the lower-level face area ML computes the deviation of actual from predicted stimuli. But these signals do not reflect the tuning characteristic of ML. Rather, they exhibit identity specificity and view invariance, the tuning properties of higher-level face areas AL and AM. Thus, learning appears to endow lower-level areas with the capability to test predictions at a higher level of abstraction than what is afforded by the feedforward sweep. These results provide evidence for computational architectures like predictive coding and suggest a new quality of functional organization of information-processing hierarchies beyond pure feedforward schemes.