Cognitive Deficits in Anti-LGI1 Encephalitis Are Linked to Immunotherapy-Resistant White Matter Network Changes

BACKGROUND AND OBJECTIVES

Cognitive deficits represent a major long-term complication of anti-leucine-rich, glioma-inactivated 1 encephalitis (LGI1-E). Although severely affecting patient outcomes, the structural brain changes underlying these deficits remain poorly understood. In this study, we hypothesized a link between white matter (WM) networks and cognitive outcomes in LGI1-E.

METHODS

In this cross-sectional study, we combined clinical assessments, comprehensive neuropsychological testing, diffusion tensor MRI, probabilistic WM tractography, and computational network analysis in patients with LGI1-E referred to Charité-Universitätsmedizin Berlin. Healthy individuals were recruited as control participants and matched to patients for age and sex with logistic regression propensity scores.

RESULTS

Twenty-five patients with LGI1-E (mean age = 63 ± 12 years, 76% male) and 25 healthy controls were enrolled. Eighty-eight percent of patients presented persistent cognitive symptoms at postacute follow-up (median: 12 months from onset, interquartile range: 6-23 months)-despite treatment with immunotherapy and good overall recovery (modified Rankin Scale [mRS] score at peak illness vs postacute: z = -4.1, p < 0.001, median mRS score at postacute visit: 1). Neuroimaging revealed that WM networks in LGI1-E are characterized by (1) a systematic reduction in whole-brain connectivity (t = -2.16, p = 0.036, d = -0.61), (2) a cortico-subcortical hypoconnectivity cluster affecting both limbic and extralimbic brain systems, and (3) a "topological reorganization" marked by a bidirectional shift in the relative importance of individual brain regions in the WM network. The extent of this WM reorganization was strongly associated with long-term deficits of verbal memory (r = -0.56), attention (r = -0.55), and executive functions (r = -0.60, all pFDR = 0.017).

DISCUSSION

Although traditionally viewed as a form of limbic encephalitis, our study characterizes LGI1-E as a "network disorder" that affects the whole brain. Structural reorganization of WM networks was linked to long-term and multidomain cognitive impairment, which was not prevented by immunotherapy. These findings highlight the need for closer monitoring and improved treatment strategies to mitigate long-term cognitive impairment in LGI1-E.

The impact of cannabidiol placebo on amygdala-based neural responses to an acute stressor

BACKGROUND

Cannabidiol (CBD) impacts brain regions implicated in anxiety reactivity and stress reactivity (e.g., amygdala, anterior cingulate cortex (ACC), anterior insula (AI)); however, placebo-controlled studies are mixed regarding CBD's anxiolytic effects. We previously reported that CBD expectancy alone can alter subjective, physiological, and endocrine markers of stress/anxiety; however, it is unclear whether these findings reflect altered brain reactivity. This study evaluated whether CBD expectancy independently alters amygdala resting-state functional connectivity (rsFC) with the ACC and AI following acute stress.

METHOD

Thirty-eight (20 females) healthy adults were randomly assigned to receive accurate or inaccurate information regarding the CBD content of a CBD-free oil administered during a single experimental session. Following a baseline resting state MRI scan, participants administered their assigned oil sublingually, engaged in a stress task (serial subtraction with negative feedback) inside the scanner, and underwent another resting state MRI scan. Amygdala rsFC with the ACC and AI was measured during each scan, and the subjective state was assessed at six time points. Outcomes were analyzed using ANCOVA.

RESULTS

CBD expectancy (vs CBD-free expectancy) was associated with significantly weaker rsFC between the left amygdala and right ACC (p = 0.042), but did not systematically alter amygdala-AI rsFC (p-values > 0.05). We also replicated our previously reported CBD expectancy effects on subjective stress/anxiety in the scanner context.

CONCLUSION

CBD placebo effects may be sufficient to alter neural responses relevant to its purported anxiolytic and stress-relieving properties. Future work is needed to replicate these results and determine whether CBD expectancy and pharmacology interact to alter neural anxiety reactivity and stress reactivity.

Osteopontin predicts late-time salience network-related functional connectivity in multiple sclerosis

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely utilized to investigate plasticity mechanisms and functional reorganization in multiple sclerosis (MS). Among many resting state (RS) networks, a significant role is played by the salience network (SN, ventral attention network). Previous reports have demonstrated the involvement of osteopontin (OPN) in the pathogenesis of MS, which acts as a proinflammatory cytokine ultimately leading to neurodegeneration. Concentration of serum OPN was related to MRI findings 10.22±2.84 years later in 44 patients with MS. Local and interhemispheric correlations (LCOR, IHC), ROI-to-ROI and seed-based connectivity analyses were performed using serum OPN levels as independent variable along with age and gender as nuisance variables. We found significant associations between OPN levels and local correlation in right and left clusters encompassing the central opercular- and insular cortices (p-FDR = 0.0018 and p-FDR = 0.0205, respectively). Moreover, a significant association was identified between OPN concentration and interhemispheric correlation between central opercular- and insular cortices (p-FDR = 0.00015). Significant positive associations were found between OPN concentration and functional connectivity (FC) within the SN (FC strength between the anterior insula ventral division and 3 other insular regions, F(2,13) = 7.84, p-FDR = 0.0117). Seed-based connectivity analysis using the seven nodes of the SN resulted in several positive and inverse associations with OPN level. Serum OPN level may predict FC alterations within the SN in 10 years.

An externally validated resting-state brain connectivity signature of pain-related learning

Pain can be conceptualized as a precision signal for reinforcement learning in the brain and alterations in these processes are a hallmark of chronic pain conditions. Investigating individual differences in pain-related learning therefore holds important clinical and translational relevance. Here, we developed and externally validated a novel resting-state brain connectivity-based predictive model of pain-related learning. The pre-registered external validation indicates that the proposed model explains 8-12% of the inter-individual variance in pain-related learning. Model predictions are driven by connections of the amygdala, posterior insula, sensorimotor, frontoparietal, and cerebellar regions, outlining a network commonly described in aversive learning and pain. We propose the resulting model as a robust and highly accessible biomarker candidate for clinical and translational pain research, with promising implications for personalized treatment approaches and with a high potential to advance our understanding of the neural mechanisms of pain-related learning.

Functional brain networks in Developmental Topographical Disorientation

Despite a decade-long study on Developmental Topographical Disorientation, the underlying mechanism behind this neurological condition remains unknown. This lifelong selective inability in orientation, which causes these individuals to get lost even in familiar surroundings, is present in the absence of any other neurological disorder or acquired brain damage. Herein, we report an analysis of the functional brain network of individuals with Developmental Topographical Disorientation ($n = 19$) compared against that of healthy controls ($n = 21$), all of whom underwent resting-state functional magnetic resonance imaging, to identify if and how their underlying functional brain network is altered. While the established resting-state networks (RSNs) are confirmed in both groups, there is, on average, a greater connectivity and connectivity strength, in addition to increased global and local efficiency in the overall functional network of the Developmental Topographical Disorientation group. In particular, there is an enhanced connectivity between some RSNs facilitated through indirect functional paths. We identify a handful of nodes that encode part of these differences. Overall, our findings provide strong evidence that the brain networks of individuals suffering from Developmental Topographical Disorientation are modified by compensatory mechanisms, which might open the door for new diagnostic tools.

The role of stimulant washout status in functional connectivity of default mode and fronto-parietal networks in children with neurodevelopmental conditions

BACKGROUND

Stimulant medication is the primary pharmacological treatment for attention dysregulation and is commonly prescribed for children with Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism. Neuroimaging studies of these groups commonly use a 24-48-hour washout period to mediate the effects of stimulant medication on functional connectivity (FC) metrics. However, the impact of washout on functional connectivity has received limited study.

METHODS

We used fMRI data from participants with diagnosis of Autism and ADHD (and an off stimulant control) from the Adolescent Brain and Cognitive Development (ABCD) and Autism Brain Imaging Data Exchange (ABIDE) databases to explore the effect of simulant washout on FC. Connectivity within and between the default mode (DMN) and fronto-parietal networks (FPN) was examined, as these networks have previously been implicated in attention dysregulation and associated with stimulant medication usage. For each diagnostic group, we assessed effects in interconnectivity between DMN and FPN, intraconnectivity within DMN, and intraconnectivity within FPN.

RESULTS

We found no significant effect of medication status in intra- and inter-connectivity of the DMN and the FPN in either diagnostic group.

IMPLICATIONS

Our findings suggest that more information is needed about the effect of stimulant medication, and washout, on the FC of attention networks in clinical populations.

Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.

Brain decoding of the Human Connectome Project tasks in a dense individual fMRI dataset

Brain decoding aims to infer cognitive states from patterns of brain activity. Substantial inter-individual variations in functional brain organization challenge accurate decoding performed at the group level. In this paper, we tested whether accurate brain decoding models can be trained entirely at the individual level. We trained several classifiers on a dense individual functional magnetic resonance imaging (fMRI) dataset for which six participants completed the entire Human Connectome Project (HCP) task battery >13 times over ten separate fMRI sessions. We evaluated nine decoding methods, from Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) to Graph Convolutional Neural Networks (GCN). All decoders were trained to classify single fMRI volumes into 21 experimental conditions simultaneously, using ∼7 h of fMRI data per participant. The best prediction accuracies were achieved with GCN and MLP models, whose performance (57-67 % accuracy) approached state-of-the-art accuracy (76 %) with models trained at the group level on >1 K hours of data from the original HCP sample. Our SVM model also performed very well (54-62 % accuracy). Feature importance maps derived from MLP -our best-performing model- revealed informative features in regions relevant to particular cognitive domains, notably in the motor cortex. We also observed that inter-subject classification achieved substantially lower accuracy than subject-specific models, indicating that our decoders learned individual-specific features. This work demonstrates that densely-sampled neuroimaging datasets can be used to train accurate brain decoding models at the individual level. We expect this work to become a useful benchmark for techniques that improve model generalization across multiple subjects and acquisition conditions.

Graph analysis of cortical reorganization after virtual reality-based rehabilitation following stroke: a pilot randomized study

Introduction

Stroke is the leading cause of functional disability worldwide. With the increase of the global population, motor rehabilitation of stroke survivors is of ever-increasing importance. In the last decade, virtual reality (VR) technologies for rehabilitation have been extensively studied, to be used instead of or together with conventional treatments such as physiotherapy or occupational therapy. The aim of this work was to evaluate the GestureCollection VR-based rehabilitation tool in terms of the brain changes and clinical outcomes of the patients.

Methods

Two groups of chronic patients underwent a rehabilitation treatment with (experimental) or without (control) complementation with GestureCollection. Functional magnetic resonance imaging exams and clinical assessments were performed before and after the treatment. A functional connectivity graph-based analysis was used to assess differences between the connections and in the network parameters strength and clustering coefficient.

Results

Patients in both groups showed improvement in clinical scales, but there were more increases in functional connectivity in the experimental group than in the control group.

Discussion

The experimental group presented changes in the connections between the frontoparietal and the somatomotor networks, associative cerebellum and basal ganglia, which are regions associated with reward-based motor learning. On the other hand, the control group also had results in the somatomotor network, in its ipsilateral connections with the thalamus and with the motor cerebellum, which are regions more related to a purely mechanical activity. Thus, the use of the GestureCollection system was successfully shown to promote neuroplasticity in several motor-related areas.

Neural underpinnings of preferential pain learning and the modulatory role of fear

Due to its unique biological relevance, pain-related learning might differ from learning from other aversive experiences. This functional magnetic resonance imaging study compared neural mechanisms underlying the acquisition and extinction of different threats in healthy humans. We investigated whether cue-pain associations are acquired faster and extinguished slower than cue associations with an equally unpleasant tone. Additionally, we studied the modulatory role of stimulus-related fear. Therefore, we used a differential conditioning paradigm, in which somatic heat pain stimuli and unpleasantness-matched auditory stimuli served as US. Our results show stronger acquisition learning for pain- than tone-predicting cues, which was augmented in participants with relatively higher levels of fear of pain. These behavioral findings were paralleled by activation of brain regions implicated in threat processing (insula, amygdala) and personal significance (ventromedial prefrontal cortex). By contrast, extinction learning seemed to be less dependent on the threat value of the US, both on the behavioral and neural levels. Amygdala activity, however, scaled with pain-related fear during extinction learning. Our findings on faster and stronger (i.e. "preferential") pain learning and the role of fear of pain are consistent with the biological relevance of pain and may be relevant to the development or maintenance of chronic pain.

Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark is implemented in a fully reproducible framework, where the provided research objects enable readers to reproduce or modify core computations, as well as the figures of the article using the Jupyter Book project and the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep software package. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods. Our reproducible benchmark infrastructure will facilitate such continuous evaluation in the future, and may also be applied broadly to different tools or even research fields.

The interacting brain: Dynamic functional connectivity among canonical brain networks dissociates cooperative from competitive social interactions

We spend much our lives interacting with others in various social contexts. Although we deal with this myriad of interpersonal exchanges with apparent ease, each one relies upon a broad array of sophisticated cognitive processes. Recent research suggests that the cognitive operations supporting interactive behaviour are themselves underpinned by several canonical functional brain networks (CFNs) that integrate dynamically with one another in response to changing situational demands. Dynamic integrations among these CFNs should therefore play a pivotal role in coordinating interpersonal behaviour. Further, different types of interaction should present different demands on cognitive systems, thereby eliciting distinct patterns of dynamism among these CFNs. To investigate this, the present study performed functional magnetic resonance imaging (fMRI) on 30 individuals while they interacted with one another cooperatively or competitively. By applying a novel combination of analytical techniques to these brain imaging data, we identify six states of dynamic functional connectivity characterised by distinct patterns of integration and segregation among specific CFNs that differ systematically between these opposing types of interaction. Moreover, applying these same states to fMRI data acquired from an independent sample engaged in the same kinds of interaction, we were able to classify interpersonal exchanges as cooperative or competitive. These results provide the first direct evidence for the systematic involvement of CFNs during social interactions, which should guide neurocognitive models of interactive behaviour and investigations into biomarkers for the interpersonal dysfunction characterizing many neurological and psychiatric disorders.

A longitudinal mediation study of peer victimization and resting-state functional connectivity as predictors of development of adolescent psychopathology

Background

Peer victimization (PV) is associated with alterations in neural responses in regions subserving emotional regulatory processes and with increased risk of psychopathology during adolescence. The present study examined the longitudinal mediating effects of resting-state functional connectivity (rsFC) between adolescent PV and subsequent internalizing (depression and anxiety), and externalizing (conduct and hyperactivity/inattention) symptoms.

Methods

151 adolescents (baseline mean age 12-14; 54% males) were assessed and imaged three times during a five-year period. We focused on rsFC of a priori determined Regions-of-Interest (ROIs) guided by the literature (i.e., amygdala, anterior and posterior insula, anterior cingulate cortex, and medial prefrontal cortex). Multilevel mediation (MLM) analyses simultaneously examined the between-person, concurrent within-person, and lagged within-person associations between PV and internalizing/externalizing symptoms through changes in couplings of the amygdala with the other four ROIs. All models controlled for the effects of self-reported childhood maltreatment and sex differences.

Results

An increased rsFC of the amygdala-posterior insula significantly mediated the lagged within-person association of PV and internalizing symptoms (β = 0.144; 95% CI [0.018, 0.332]). This effect was significant regardless of childhood maltreatment, concurrent externalizing symptoms, and sex differences. The rsFC did not mediate the relationship between PV and externalizing symptoms.

Conclusions

Results of this study suggest that adolescent PV may lead to long-lasting maladaptive neural communication between emotional response and sensory perception of pain (i.e., bottom-up emotion regulation) and that these neural responses may serve as unique markers for increased internalizing symptoms that appear in later adolescence in peer-victimized youth. These findings have implications for interventions targeting internalizing symptoms in victimized adolescents.

Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study

The use of predefined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed preprocessed structural magnetic resonance imaging (sMRI) data from the Adolescent Brain Cognitive Development Study® to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9- to 10-year-old children (N = 9,432). Choice of machine learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger number of parcels (up to ~4,000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector-based pipeline, and ensembling across multiple parcellations, respectively, as the highest performing. These findings highlight the choice of parcellation as an important influence on downstream predictive performance, showing in some cases that switching to a higher resolution parcellation can yield a relatively large boost to performance.

Cross-lagged Relationships Between Depressive Symptoms and Altered Default Mode Network Connectivity Over the Course of Adolescence

BACKGROUND

Although the peak onset of depressive symptoms occurs during adolescence, very few studies have directly examined depression-related changes in resting-state (RS) default mode network activity during adolescence, controlling for potential neural markers of risk.

METHODS

This study used data from a longitudinal adolescent cohort to investigate age-specific, persistent (i.e., lagged), and dynamic associations between RS functional connectivity within the default mode network and depressive symptoms during adolescence using a random intercept cross-lagged panel framework. The Neuroventure sample consisted of 151 adolescents ages 12-14 at study entry without any neurological illness who were assessed three times during a 5-year follow-up with 97% follow-up across the three assessments. Depressive symptoms were measured using the depression subscale of the Brief Symptoms Inventory. RS functional magnetic resonance imaging data were collected using a 3T Siemens Magnetom Trio scanner in a single 6-minute sequence.

RESULTS

After controlling for relationships between random intercepts, future depression risk was predicted by RS couplings in the perigenual anterior cingulate cortex and anterior dorsomedial prefrontal cortex (β = -0.69, p = .014) and in the left inferior parietal lobule and anterior superior frontal gyrus (β = -0.43, p = .035). Increases in depressive symptoms at previous time points significantly predicted changes in functional connectivity between the posterior cingulate cortex and the precuneus and posterior middle temporal gyrus (β = 0.37, p = .039) and between the dorsal precuneus and posterior middle temporal gyrus (β = 0.47, p = .036).

CONCLUSIONS

This study was able to disassociate the RS brain markers of depression from those that appear to follow early-onset depression.

Functional connectomes become more longitudinally self-stable, but not more distinct from others, across early childhood

Functional connectomes, as measured with functional magnetic resonance imaging (fMRI), are highly individualized, and evidence suggests this individualization may increase across childhood. A connectome can become more individualized either by increasing self-stability or decreasing between-subject-similarity. Here we used a longitudinal early childhood dataset to investigate age associations with connectome self-stability, between-subject-similarity, and developmental individualization, defined as an individual's self-stability across a 12-month interval relative to their between-subject-similarity. fMRI data were collected during an 18-minute passive viewing scan from 73 typically developing children aged 4-7 years, at baseline and 12-month follow-up. We found that young children had highly individualized connectomes, with sufficient self-stability across 12-months for 98% identification accuracy. Linear models showed a significant relationship between age and developmental individualization across the whole brain and in most networks. This association appeared to be largely driven by an increase in self-stability with age, with only weak evidence for relationships between age and similarity across participants. Together our findings suggest that children's connectomes become more individualized across early childhood, and that this effect is driven by increasing self-stability rather than decreasing between-subject-similarity.

Combined Multi-Atlas and Multi-Layer Perception for Alzheimer's Disease Classification

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. To distinguish the stage of the disease, AD classification technology challenge has been proposed in Pattern Recognition and Computer Vision 2021 (PRCV 2021) which provides the gray volume and average cortical thickness data extracted in multiple atlases from magnetic resonance imaging (MRI). Traditional methods either train with convolutional neural network (CNN) by MRI data to adapt the spatial features of images or train with recurrent neural network (RNN) by temporal features to predict the next stage. However, the morphological features from the challenge have been extracted into discrete values. We present a multi-atlases multi-layer perceptron (MAMLP) approach to deal with the relationship between morphological features and the stage of the disease. The model consists of multiple multi-layer perceptron (MLP) modules, and morphological features extracted from different atlases will be classified by different MLP modules. The final vote of all classification results obtains the predicted disease stage. Firstly, to preserve the diversity of brain features, the most representative atlases are chosen from groups of similar atlases, and one atlas is selected in each group. Secondly, each atlas is fed into one MLP to fetch the score of the classification. Thirdly, to obtain more stable results, scores from different atlases are combined to vote the result of the classification. Based on this approach, we rank 10th among 373 teams in the challenge. The results of the experiment indicate as follows: (1) Group selection of atlas reduces the number of features required without reducing the accuracy of the model; (2) The MLP architecture achieves better performance than CNN and RNN networks in morphological features; and (3) Compared with other networks, the combination of multiple MLP networks has faster convergence of about 40% and makes the classification more stable.

Benchmarking common preprocessing strategies in early childhood functional connectivity and intersubject correlation fMRI

Preprocessing choices present a particular challenge for researchers working with functional magnetic resonance imaging (fMRI) data from young children. Steps which have been shown to be important for mitigating head motion, such as censoring and global signal regression (GSR), remain controversial, and benchmarking studies comparing preprocessing pipelines have been conducted using resting data from older participants who tend to move less than young children. Here, we conducted benchmarking of fMRI preprocessing steps in a population with high head-motion, children aged 4-8 years, leveraging a unique longitudinal, passive viewing fMRI dataset. We systematically investigated combinations of global signal regression (GSR), volume censoring, and ICA-AROMA. Pipelines were compared using previously established metrics of noise removal as well as metrics sensitive to recovery of individual differences (i.e., connectome fingerprinting), and stimulus-evoked responses (i.e., intersubject correlations; ISC). We found that: 1) the most efficacious pipeline for both noise removal and information recovery included censoring, GSR, bandpass filtering, and head motion parameter (HMP) regression, 2) ICA-AROMA performed similarly to HMP regression and did not obviate the need for censoring, 3) GSR had a minimal impact on connectome fingerprinting but improved ISC, and 4) the strictest censoring approaches reduced motion correlated edges but negatively impacted identifiability.

Inattentive and hyperactive traits differentially associate with inter-individual functional synchrony during video viewing in young children without ADHD

Inattention and hyperactivity present on a spectrum and may influence the way children perceive and interact with the world. We investigated whether normative variation in inattentive and hyperactive traits was associated with differences in brain function, while children watched clips from an age-appropriate television program. Functional magnetic resonance imaging (fMRI) data and parent reports of inattention and hyperactivity traits were collected from 81 children 4–7 years of age with no parent-reported diagnoses. Data were analyzed using intersubject correlations (ISCs) in mixed effects models to determine if inattentive and hyperactive traits were associated with idiosyncrasy of fMRI response to the video. We hypothesized that pairs of children with higher average inattention and hyperactivity scores would show less interindividual brain synchrony to one another than pairs with lower average scores on these traits. Video watching engaged widespread visual, auditory, default mode and dorsal prefrontal regions. Inattention and hyperactivity were separably associated with ISC in many of these regions. Our findings suggest that the spectrum of inattention and hyperactivity traits in children without ADHD are differentially associated with neural processing of naturalistic video stimuli, which may have implications for understanding how children with different levels of these traits process audiovisual information in unconstrained conditions.

Functional brain network community structure in childhood: Unfinished territories and fuzzy boundaries

Adult cortex is organized into distributed functional communities. Yet, little is known about community architecture of children's brains. Here, we uncovered the community structure of cortex in childhood using fMRI data from 670 children aged 9-11 years (48% female, replication sample n=544, 56% female) from the Adolescent Brain and Cognitive Development study. We first applied a data-driven community detection approach to cluster cortical regions into communities, then employed a generative model-based approach called the weighted stochastic block model to further probe community interactions. Children showed similar community structure to adults, as defined by Yeo and colleagues in 2011, in early-developing sensory and motor communities, but differences emerged in transmodal areas. Children have more cortical territory in the limbic community, which is involved in emotion processing, than adults. Regions in association cortex interact more flexibly across communities, creating uncertainty for the model-based assignment algorithm, and perhaps reflecting cortical boundaries that are not yet solidified. Uncertainty was highest for cingulo-opercular areas involved in flexible deployment of cognitive control. Activation and deactivation patterns during a working memory task showed that both the data-driven approach and a set of adult communities statistically capture functional organization in middle childhood. Collectively, our findings suggest that community boundaries are not solidified by middle childhood.

Cerebello-limbic functional connectivity patterns in youth at clinical high risk for psychosis

Youth at clinical high risk (CHR) for psychosis can present not only with characteristic attenuated psychotic symptoms but also may have other comorbid conditions, including anxiety and depression. These undifferentiated mood symptoms can overlap with the clinical presentation of youth with Distress syndromes. Increased resting-state functional connectivity within cerebello-thalamo-cortical (CTC) pathways has been proposed as a trait-specific biomarker for CHR. However, it is unclear whether this functional neural signature remains specific when compared to a different risk group: youth with Distress syndromes. The purpose of the present work was to describe CTC alterations that distinguish between CHR and Distressed individuals. Using machine learning algorithms, we analyzed CTC connectivity features of CHR (n = 51), Distressed (n = 41), and healthy control (n = 36) participants. We found four cerebellar (lobes VII and left Crus II anterior/posterior) and two basal ganglia (right putamen and right thalamus) nodes containing a set of specific connectivity features that distinguished between CHR, Distressed and healthy control groups. Hyperconnectivity between medial lobule VIIb, somatomotor network and middle temporal gyrus was associated with CHR status and more severe symptoms. Detailed atlas parcellation suggested that CHR individuals may have dysfunction mainly within the associative (cognitive) pathways, particularly, between those brain areas responsible for the multi-sensory signal integration.

Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels

Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models.

The structural and functional connectivities of the brain, which reflect the anatomical connections of axonal bundles and the amount of coactivation between brain regions, respectively, only weakly correlate with each other. In order to enhance and investigate this relationship, large-scale whole-brain dynamical models were involved in this branch of research. However, how the definitions of the brain regions parcellated according to a so-called brain atlas influence these models has so far not been systematically assessed. In this article, we show that this influence can be large, and link group-averaged, atlas-induced deviations to network properties extracted from both types of connectivity. Additionally, we demonstrate that the same association does not apply to subject-specific variations. These results may contribute to the further personalization of the whole-brain models.

Dissecting autism and schizophrenia through neuroimaging genomics

Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a 'top-down' approach, beginning with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In this review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioural and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions' phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes.

Cortical and Subcortical Grey Matter Abnormalities in White Matter Hyperintensities and Subsequent Cognitive Impairment

Grey matter (GM) alterations may contribute to cognitive decline in individuals with white matter hyperintensities (WMH) but no consensus has yet emerged. Here, we investigated cortical thickness and grey matter volume in 23 WMH patients with mild cognitive impairment (WMH-MCI), 43 WMH patients without cognitive impairment, and 55 healthy controls. Both WMH groups showed GM atrophy in the bilateral thalamus, fronto-insular cortices, and several parietal-temporal regions, and the WMH-MCI group showed more extensive and severe GM atrophy. The GM atrophy in the thalamus and fronto-insular cortices was associated with cognitive decline in the WMH-MCI patients and may mediate the relationship between WMH and cognition in WMH patients. Furthermore, the main results were well replicated in an independent dataset from the Alzheimer's Disease Neuroimaging Initiative database and in other control analyses. These comprehensive results provide robust evidence of specific GM alterations underlying WMH and subsequent cognitive impairment.

Vascular risk factors are associated with a decline in resting-state functional connectivity in cognitively unimpaired individuals at risk for Alzheimer's disease: Vascular risk factors and functional connectivity changes

Resting-state functional connectivity is suggested to be cross-sectionally associated with both vascular burden and Alzheimer's disease (AD) pathology. However, evidence is lacking regarding longitudinal changes in functional connectivity. This study includes 247 cognitively unimpaired individuals with a family history of sporadic AD (185 women/ 62 men; mean [SD] age of 63 [5.3] years). Plasma total-, HDL-, and LDL-cholesterol and systolic and diastolic blood pressure were measured at baseline. Global (whole-brain) brain functional connectivity and connectivity from canonical functional networks were computed from resting-state functional MRI obtained at baseline and ~3.5 years of annual follow-ups, using a predefined functional parcellation. A subsample underwent Aβ- and tau-PET (n=91). Linear mixed-effects models demonstrated that global functional connectivity increased over time across the entire sample. In contrast, higher total-cholesterol and LDL-cholesterol levels were associated with greater reduction of functional connectivity in the default-mode network over time. In addition, higher diastolic blood pressure was associated with global functional connectivity reduction. The associations were similar when the analyses were repeated using two other functional brain parcellations. Aβ and tau deposition in the brain were not associated with changes in functional connectivity over time in the subsample. These findings provide evidence that vascular burden is associated with a decrease in functional connectivity over time in older adults with elevated risk for AD. Future studies are needed to determine if the impact of vascular risk factors on functional brain changes precede the impact of AD pathology on functional brain changes.

Decoding the neural dynamics of free choice in humans

How do we choose a particular action among equally valid alternatives? Nonhuman primate findings have shown that decision-making implicates modulations in unit firing rates and local field potentials (LFPs) across frontal and parietal cortices. Yet the electrophysiological brain mechanisms that underlie free choice in humans remain ill defined. Here, we address this question using rare intracerebral electroencephalography (EEG) recordings in surgical epilepsy patients performing a delayed oculomotor decision task. We find that the temporal dynamics of high-gamma (HG, 60-140 Hz) neural activity in distinct frontal and parietal brain areas robustly discriminate free choice from instructed saccade planning at the level of single trials. Classification analysis was applied to the LFP signals to isolate decision-related activity from sensory and motor planning processes. Compared with instructed saccades, free-choice trials exhibited delayed and longer-lasting HG activity during the delay period. The temporal dynamics of the decision-specific sustained HG activity indexed the unfolding of a deliberation process, rather than memory maintenance. Taken together, these findings provide the first direct electrophysiological evidence in humans for the role of sustained high-frequency neural activation in frontoparietal cortex in mediating the intrinsically driven process of freely choosing among competing behavioral alternatives.

Adolescent Resting-State Brain Networks and Unique Variability of Conduct Problems Within the Externalizing Dimension

The externalizing psychopathological dimension is associated with alterations in adolescents' functional brain connectivity. The current study aims to identify the functional correlates of the unique variability in conduct problems within the context of the broad externalizing dimension. The broad externalizing dimension and unique variability in conduct problems were estimated using a bifactor model. Resting-state data were available for a sample of 125 adolescents. Based on multiresolution parcellation of functional brain networks atlas, major resting-state functional brain networks and the connectivity correlates of unique conduct problems and the broad externalizing dimension were established. The broad externalizing dimension was related to connectivity alterations in the ventral attention/salience network, while unique variability in conduct problems dimension was related to connectivity alterations in the cerebellum crusi as well as the mesolimbic network. The current study is a first step toward the identification of functional resting-state network correlates of broad and specific variability in the externalizing dimension.

Fine-grain atlases of functional modes for fMRI analysis

Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 ​TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional "soft" functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results.

Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks

The past decade has witnessed a proliferation of studies aimed at characterizing the human connectome. These projects map the brain regions comprising large-scale systems underlying cognition using non-invasive neuroimaging approaches and advanced analytic techniques adopted from network science. While the idea that the human brain is composed of multiple macro-scale functional networks has been gaining traction in cognitive neuroscience, the field has yet to reach consensus on several key issues regarding terminology. What constitutes a functional brain network? Are there "core" functional networks, and if so, what are their spatial topographies? What naming conventions, if universally adopted, will provide the most utility and facilitate communication amongst researchers? Can a taxonomy of functional brain networks be delineated? Here we survey the current landscape to identify six common macro-scale brain network naming schemes and conventions utilized in the literature, highlighting inconsistencies and points of confusion where appropriate. As a minimum recommendation upon which to build, we propose that a scheme incorporating anatomical terminology should provide the foundation for a taxonomy of functional brain networks. A logical starting point in this endeavor might delineate systems that we refer to here as "occipital", "pericentral", "dorsal frontoparietal", "lateral frontoparietal", "midcingulo-insular", and "medial frontoparietal" networks. We posit that as the field of network neuroscience matures, it will become increasingly imperative to arrive at a taxonomy such as that proposed here, that can be consistently referenced across research groups.

Structural and functional multi-platform MRI series of a single human volunteer over more than fifteen years

We present MRI data from a single human volunteer consisting in over 599 multi-contrast MR images (T1-weighted, T2-weighted, proton density, fluid-attenuated inversion recovery, T2* gradient-echo, diffusion, susceptibility-weighted, arterial-spin labelled, and resting state BOLD functional connectivity imaging) acquired in over 73 sessions on 36 different scanners (13 models, three manufacturers) over the course of 15+ years (cf. Data records). Data included planned data collection acquired within the Consortium pour l'identification précoce de la maladie Alzheimer - Québec (CIMA-Q) and Canadian Consortium on Neurodegeneration in Aging (CCNA) studies, as well as opportunistic data collection from various protocols. These multiple within- and between-centre scans over a substantial time course of a single, cognitively healthy volunteer can be useful to answer a number of methodological questions of interest to the community.