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Sleep health is both conceptually and operationally a composite concept containing multiple domains of sleep. In line with this, high dependence and interaction across different domains of sleep health encourage a transition in sleep health research from categorical to dimensional approaches that integrate neuroscience and sleep health. Here, we seek to identify the covariance patterns between multiple-sleep health domains and distributed intrinsic functional connectivity by applying a multivariate approach (partial least squares). This multivariate analysis revealed a composite sleep health dimension co-varying with connectivity patterns involving the attentional and thalamic networks and which appear relevant at the neuromolecular level. These findings were further replicated and generalized to several unseen independent datasets. Critically, the identified sleep-health related connectome showed diagnostic potential for insomnia disorder. These results together delineate a brain connectome biomarker for sleep health with high potential for clinical translation.

Introduction: Hippocampal local and network dysfunction is the hallmark of Alzheimer's disease (AD).Methods: We characterized the spatial patterns of hippocampus differentiation based on brain co-metabolism in healthy elderly participants and demonstrated their relevance to study local metabolic changes and associated dysfunction in pathological aging.Results: The hippocampus can be differentiated into anterior/posterior and dorsal cornu ammonis (CA)/ventral (subiculum) subregions. While anterior/posterior CA show co-metabolism with different regions of the subcortical limbic networks, the anterior/posterior subiculum are parts of cortical networks supporting object-centered memory and higher cognitive demands, respectively. Both networks show relationships with the spatial patterns of gene expression pertaining to cell energy metabolism and AD's process. Finally, while local metabolism is generally lower in posterior regions, the anterior-posterior imbalance is maximal in late mild cognitive impairment with the anterior subiculum being relatively preserved.Discussion: Future studies should consider bidimensional hippocampal differentiation and in particular the posterior subicular region to better understand pathological aging.

ObjectivePedophilia is a disorder of public concern because of its association with child sexual offense and recidivism. Previous neuroimaging studies identified inconsistent brain abnormalities underlying pedophilic behavior both in idiopathic and acquired (i.e., emerging following brain damage) pedophilia. This study sought to explore the neural underpinnings of pedophilic behavior and to determine the extent to which brain alterations may be related to distinct psychopathological features in pedophilia.MethodsA coordinate based meta-analysis on previously published papers reporting whole brain analysis and a lesion network analysis using brain lesions as seeds in a resting state connectivity analysis were run to investigate the presence of consistent neural basis of idiopathic and acquired pedophilic behavior, respectively. The behavioral profiling approach was applied to link identified regions with corresponding psychological processes.ResultsWhile no consistent neuroanatomical alterations have been identified in idiopathic pedophilia, the current results support that all the lesions causing acquired pedophilia localized to a shared resting state network including posterior midlines structures, right inferior temporal gyrus and bilateral orbitofrontal cortex. These regions are associated with action inhibition and social cognition, abilities that are consistently described as severely impaired in acquired pedophiles.ConclusionsThis study suggests that idiopathic and acquired pedophilia may be two distinct disorders, in line with their distinctive clinical features, including age of onset, reversibility and modus operandi. Understanding the neurobiological underpinnings of pedophilic behavior may contribute to a more comprehensive characterization of these individuals on a clinical ground and to develop more efficient therapeutic rehabilitation strategies.

The human striatum is essential for both low- and high-level functions and has been implicated in the pathophysiology of various prevalent disorders, including Parkinson’s disease (PD) and schizophrenia (SCZ). It is known to consist of structurally and functionally divergent subdivisions. However, previous parcellations are based on a single neuroimaging modality, leaving the extent of the multi-modal organization of the striatum unknown. Here, we investigated the organization of the striatum across three modalities—resting-state functional connectivity, probabilistic diffusion tractography, and structural covariance—to provide a holistic convergent view of its structure and function. We found convergent clusters in the dorsal, dorsolateral, rostral, ventral, and caudal striatum. Functional characterization revealed the anterior striatum to be mainly associated with cognitive and emotional functions, while the caudal striatum was related to action execution. Interestingly, significant structural atrophy in the rostral and ventral striatum was common to both PD and SCZ, but atrophy in the dorsolateral striatum was specifically attributable to PD. Our study revealed a cross-modal convergent organization of the striatum, representing a fundamental topographical model that can be useful for investigating structural and functional variability in aging and in clinical conditions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12264-020-00543-1) contains supplementary material, which is available to authorized users.

What are the brain structural correlates of interindividual differences in behaviour? More than a decade ago, advances in structural MRI opened promising new avenues to address this question. The initial wave of research then progressively led to substantial conceptual and methodological shifts, and a replication crisis unveiled the limitations of traditional approaches, which involved searching for associations between local measurements of neuroanatomy and behavioural variables in small samples of healthy individuals. Given these methodological issues and growing scepticism regarding the idea of one-to-one mapping of psychological constructs to brain regions, new perspectives emerged. These not only embrace the multivariate nature of brain structure-behaviour relationships and promote generalizability but also embrace the representation of the relationships between brain structure and behavioural data by latent dimensions of interindividual variability. Here, we examine the past and present of the study of brain structure-behaviour associations in healthy populations and address current challenges and open questions for future investigations.

Item does not contain fulltext Understanding human white matter development is vital to characterize typical brain organization and developmental neurocognitive disorders. In this issue of Neuron, Nazeri and colleagues identify different parts of white matter in the neonatal brain and show their maturational trajectories in line with microstructural feature development. 4 p.

Purpose: Hippocampal dysfunction happens across many neuropsychiatric disorders and is the hallmark of Alzheimer’s disease with evidenced metabolic alterations. However, while metabolic changes are a key aspect of Alzheimer’s disease, hippocampal metabolic networks, as defined by metabolic covariance, haven’t been identified in healthy populations. As the hippocampus portrays cytoarchitectural, connectional, and functional heterogeneity, heterogeneous patterns of metabolic covariance could be expected. Methods: We first characterized this heterogeneity with a data-driven approach by identifying the spatial pattern of hippocampus differentiation based on metabolic covariance with the rest of the brain in FDG-PET data of large healthy elderly cohort (n=362). Then, we characterized the metabolic networks of the robustly defined subregions. In the following, we characterized the disentangled hippocampal metabolic networks with regards to behavioral and neurotransmitter systems using quantitative decoding. Finally, we examined how the local metabolism in the hippocampal subregions is influenced by Alzheimer’s disease pathology in a cohort of ADNI participants (n = 580). Results: Based on hippocampal-brain metabolic covariance in a healthy elderly cohort, we found a differentiation into primarily anterior vs. posterior and secondarily Cornu Ammonis (CA) vs. subiculum subregions. Characterizing the associated metabolic networks revealed that the anterior-subiculum network including temporal-pole and orbitofrontal regions relates to self, motivation and mentalizing behavior and is influenced by dopaminergic systems. In contrast, the posterior-subiculum shows a wide cortical network engaged in action- and world-oriented cognition targeted by serotoninergic systems. The anterior- and posterior-CA, connected respectively to amygdala and broader subcortical networks, are associated to several transporters release. Local metabolism comparison between Alzheimer’s disease-related diagnosis groups revealed early CA’s alterations while posterior subicular alterations appear at advanced stages in line with broader cortical atrophy and behavioral dysfunctions.Conclusion: Future studies should delineate patients’ individual profiles according to hippocampal subregions and networks.

Contains fulltext : 202546pub.pdf (Publisher’s version ) (Closed access) Abstract Women neuroscientists (please note that we refer to all who identify as such) are still underrepresented in various aspects of academic life. The efforts of the community to mitigate this issue are growing but can elicit adverse reactions (Moghaddam & Gur, 2016). In this opinion paper, we discuss the different approaches that have been taken at institutional, organizational and individual levels to counter gender bias and aim at addressing unfavorable comments. We base our reasoning on empirical data and on the feedback received after the release of the Women in Neuroscience Repository (WiNRepo, see Supplementary Table S1.a), an initiative we created to increase the visibility of women in neuroscience. While this feedback originated mainly from oral conversations and was not rigorously quantified, we believe the frequency of the comments justify their discussion, as performed in (Moghaddam & Gur, 2016). The aim of this piece (supported by a list of signatories, see Supplementary Table S2) is therefore to ‘debunk the myths’ related to gender bias and to affirmative actions in academia, as well as to propose concrete measures that can been implemented to counter such bias. This article is protected by copyright. All rights reserved.

The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions’ connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain–behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions’ connectivity profiles. We first illustrate two main applications: 1) single brain region’s predictive power for a range of psychometric variables and 2) single psychometric variable’s predictive power variation across brain region. We compare the patterns of brain–behavior provided by these approaches to the brain–behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain–behavior relationships, as well as the unique insight into brain–behavior relationships offered by this approach.

BackgroundSchizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks.MethodsWe applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls).ResultsWe demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal−parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05).ConclusionsThe compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory−motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

Inhibitory control underlies the ability to inhibit inappropriate responses and involves processes that suppress motor excitability. Such motor modulatory effect has been largely described during action preparation but very little is known about the neural circuit responsible for its implementation. Here, we addressed this point by studying the degree to which the extent of preparatory suppression relates to brain morphometry. We investigated this relationship in patients suffering from severe alcohol use disorder (AUD) because this population displays an inconsistent level of preparatory suppression and major structural brain damage, making it a suitable sample to measure such link. To do so, 45 detoxified patients underwent a structural magnetic resonance imaging (MRI) and performed a transcranial magnetic stimulation (TMS) experiment, in which the degree of preparatory suppression was quantified. Besides, behavioral inhibition and trait impulsivity were evaluated in all participants. Overall, whole-brain analyses revealed that a weaker preparatory suppression was associated with a decrease in cortical thickness of a medial prefrontal cluster, encompassing parts of the anterior cingulate cortex and superior-frontal gyrus. In addition, a negative association was observed between the thickness of the supplementary area (SMA)/pre-SMA and behavioral inhibition abilities. Finally, we did not find any significant correlation between preparatory suppression, behavioral inhibition and trait impulsivity, indicating that they represent different facets of inhibitory control. Altogether, the current study provides important insight on the neural regions underlying preparatory suppression and allows highlighting that the excitability of the motor system represents a valuable read-out of upstream cognitive processes.

Due to a lack of objective biomarkers, psychiatric diagnoses still rely strongly on patient reporting and clinician judgement. The ensuing subjectivity negatively affects the definition and reliability of psychiatric diagnoses1,2. Recent research has suggested that a combination of advanced neuroimaging and machine learning may provide a solution to this predicament by establishing such objective biomarkers for psychiatric conditions, improving the diagnostic accuracy, prognosis and development of novel treatments3.These promises led to widespread interest in machine learning applications for mental health4, including a recent paper that reports a biological marker for one of the most difficult yet momentous questions in psychiatry—the assessment of suicidal behaviour5. Just et al. compared a group of 17 participants with suicidal ideation with 17 healthy controls, reporting high discrimination accuracy using task-based functional magnetic resonance imaging signatures of life- and death-related concepts3. The authors further reported high discrimination between nine ideators who had attempted suicide versus eight ideators who had not. While being a laudable effort into a difficult topic, this study unfortunately illustrates some common conceptual and technical issues in the field that limit translation into clinical practice and raise unrealistic hopes when the results are communicated to the general public.From a conceptual point of view, machine learning studies aimed at clinical applications need to carefully consider any decisions that might hamper the interpretation or generalizability of their results. Restrictiveness to an arbitrary setting may become detrimental for machine learning applications by providing overly optimistic results that are unlikely to generalize. As an example, Just et al. excluded more than half of the patients and healthy controls initially enrolled in the study from the main analysis due to missing desired functional magnetic resonance imaging effects (a rank accuracy of at least 0.6 based on all 30 concepts). This exclusion introduces a non-assessable bias to the interpretation of the results, in particular when considering that only six of the 30 concepts were selected for the final classification procedure. While Just et al. attempt to address this question by applying the trained classifier to the initially excluded 21 suicidal ideators, they explicitly omit the excluded 24 controls from this analysis, preventing any interpretation of the extent to which the classifier decision is dependent on this initial choice.From a technical point of view, machine learning-based predictions based on neuroimaging data in small samples are intrinsically highly variable, as stable accuracy estimates and high generalizability are only achieved with several hundreds of participants6,7. The study by Just et al. falls into this category of studies with a small sample size. To estimate the impact of uncertainty on the results by Just et al., we adapted a simulation approach with the code and data kindly provided by the authors, randomly permuting (800 times) the labels across the groups using their default settings and computing the accuracies. These results showed that the 95% confidence interval for classification accuracy obtained using this dataset is about 20%, leaving large uncertainty with respect to any potential findings.Special care is also required with respect to any subjective choices in feature and classifier settings or group selection. While ad-hoc selection of a specific setting is subjective, testing of different ones and outcome-based post-hoc justification of such leads to overfitting, thus limiting the generalizability of any classification. Such overfitting may occur when multiple models or parameter choices are tested with respect to their ability to predict the testing data and only those that perform best are reported. To illustrate this issue, we performed an additional analysis with the code and data kindly provided by Just et al. More specifically, in the code and the manuscript, we identified the following non-exhaustive number of prespecified settings: (1) removal of occipital cortex data; (2) subdivision of clusters larger than 11 mm; (3) selection of voxels with at least four contributing participants in each group; (4) selection of stable clusters containing at least five voxels; (5) selection of the 1,200 most stable features; and (6) manual copying and replacing of a cluster for one control participant. Importantly, according to the publication or code documentation, all of these parameters were chosen ad hoc and for none of these settings was a parameter search performed. We systematically evaluated the effect of each of these choices on the accuracy for differentiation between suicide ideators and controls in the original dataset provided by Just et al. As shown in Fig. 1, each of the six parameters represents an optimum choice for differentiation accuracy in this dataset, with any (even minor) change often resulting in substantially lower accuracy estimates. Similarly, data leakage may also contribute to optimistic results when information outside the training set is used to build a prediction model. More generally, whenever human interventions guide the development of machine learning models for the prediction of clinical conditions, a careful evaluation and reporting of any researcher’s degrees of freedom is essential to avoid data leakage and overfitting. Subsequent sharing of data processing and analysis pipelines, as well as collected data, is a further key step to increase reproducibility and facilitate replication of potential findings.

High co-morbidity and substantial overlap across psychiatric disorders encourage a transition in psychiatry research from categorical to dimensional approaches that integrate neuroscience and psychopathology. Converging evidence suggests that the cerebellum is involved in a wide range of cognitive functions and mental disorders. An important question thus centers on the extent to which cerebellar function can be linked to transdiagnostic dimensions of psychopathology. To address this question, we used a multivariate data-driven statistical technique (partial least squares) to identify latent dimensions linking human cerebellar connectome as assessed by functional MRI to a large set of clinical, cognitive, and trait measures across 198 participants, including healthy controls (n = 92) as well as patients diagnosed with attention-deficit/hyperactivity disorder (n = 35), bipolar disorder (n = 36), and schizophrenia (n = 35). Macroscale spatial gradients of connectivity at voxel level were used to characterize cerebellar connectome properties, which provide a low-dimensional representation of cerebellar connectivity, i.e., a sensorimotor-supramodal hierarchical organization. This multivariate analysis revealed significant correlated patterns of cerebellar connectivity gradients and behavioral measures that could be represented into four latent dimensions: general psychopathology, impulsivity and mood, internalizing symptoms and executive dysfunction. Each dimension was associated with a unique spatial pattern of cerebellar connectivity gradients across all participants. Multiple control analyses and 10-fold cross-validation confirmed the robustness and generalizability of the yielded four dimensions. These findings highlight the relevance of cerebellar connectivity as a necessity for the study and classification of transdiagnostic dimensions of psychopathology and call on researcher to pay more attention to the role of cerebellum in the dimensions of psychopathology, not just within the cerebral cortex.

Botvinik-Nezer, Rotem et al., Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., European Union’s Horizon 2020 research and innovation programme (https://ec.europa.eu/programmes/horizon2020/en) under the Marie Skłodowska-Curie grant agreement no. 665501. A. Eed was supported by a predoctoral fellowship La Caixa-Severo Ochoa from Obra Social La Caixa. S.B.E. was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 785907 (HBP SGA2).

For decades, schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state functional magnetic resonance imaging (rsfMRI) to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients vs. 122 controls). Using these techniques, we demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and frontoparietal and ventral attention regions along the classical sensation-to-cognition continuum established in prior neuroanatomical work. The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascaded impairments stemming from the disrupted somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

The recent availability of population-based studies with standard neuroimaging measurements and extensive psychometric characterization opens promising perspectives to investigate the relationships between interindividual variability in brain regions’ connectivity and behavioral phenotypes. However, the multivariate nature of the prediction model based on connectivity within a network of brain regions severely limits the interpretation of the brain-behavior patterns from a cognitive neuroscience perspective. To address this issue, we here propose a connectivity-based psychometric prediction (CBPP) framework based on individual region’s connectivity profile. Preliminary to the development of this region-wise machine learning approach, we performed an extensive assessment of the general CBPP framework based on whole-brain connectivity information. Because a systematic evaluation of different parameters was lacking from previous literature, we evaluated several approaches pertaining to the different steps of a CBPP study. We hence tested 72 different approach combinations in a cohort of over 900 healthy adults across 98 psychometric variables. Overall, our extensive evaluation combined to an innovative region-wise machine learning approach, offering a framework that optimizes both prediction performance and neurobiological validity (and hence interpretability) to study brain-behavior relationships.

The study of associations between inter-individual differences in brain structure and behaviour has a long history in psychology and neuroscience. Many associations between psychometric data, particularly intelligence and personality measures and local variations of brain cortical thickness (CT) have been reported. While the impact of such findings often go beyond scientific communities, resonating in the public mind, their replicability is rarely evidenced.Here, we empirically investigated the replicability of associations of an extended range of psychometric variables and CT in a large cohort of healthy adults. Our analyses revealed low likelihood of significant associations. Furthermore, significant associations from exploratory analyses showed overestimated effect sizes and were rarely replicable in an independent sample.We discuss the interpretation and implications of these findings within the context of accumulating evidence of the poor replicability of structural-brain-behaviour associations using grey matter volume, and more broadly of the replicability crisis of brain and behaviour sciences.

A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation - defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions - is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies.

Contains fulltext : 187792.pdf (Publisher’s version ) (Closed access) Despite the common conception of the dorsal premotor cortex (PMd) as a single brain region, its diverse connectivity profiles and behavioral heterogeneity argue for a differentiated organization of the PMd. A previous study revealed that the right PMd is characterized by a rostro-caudal and a ventro-dorsal distinction dividing it into five subregions: rostral, central, caudal, ventral and dorsal. The present study assessed whether a similar organization is present in the left hemisphere, by capitalizing on a multimodal data-driven approach combining connectivity-based parcellation (CBP) based on meta-analytic modeling, resting-state functional connectivity, and probabilistic diffusion tractography. The resulting PMd modules were then characterized based on multimodal functional connectivity and a quantitative analysis of associated behavioral functions. Analyzing the clusters consistent across all modalities revealed an organization of the left PMd that mirrored its right counterpart to a large degree. Again, caudal, central and rostral modules reflected a cognitive-motor gradient and a premotor eye-field was found in the ventral part of the left PMd. In addition, a distinct module linked to abstract cognitive functions was observed in the rostro-ventral left PMd across all CBP modalities, implying greater differentiation of higher cognitive functions for the left than the right PMd. 12 p.

Item does not contain fulltext Recently, we showed that the functional heterogeneity of the right dorsal premotor (PMd) cortex could be better understood by dividing it into five subregions that showed different behavioral associations according to task-based activations studies. The present study investigated whether the revealed behavioral profile could be corroborated and complemented by a structural brain behavior correlation approach in two healthy adults cohorts. Grey matter volume within the five volumes of interest (VOI-GM) was computed using voxel-based morphometry. Associations between the inter-individual differences in VOI-GM and performance across a range of neuropsychological tests were assessed in the two cohorts with and without correction for demographical variables. Additional analyses were performed in random smaller subsamples drawn from each of the two cohorts. In both cohorts, correlation coefficients were low; only few were significant and a considerable number of correlations were counterintuitive in their directions (i.e., higher performance related to lower GMV). Furthermore, correlation patterns were inconsistent between the two cohorts. Subsampling revealed that correlation patterns could vary widely across small samples and that negative correlations were as likely as positive correlations. Thus, the structural brain/behavior approach did not corroborate the functional profiles of the PMd subregions inferred from activation studies, suggesting that local recruitment by fMRI studies does not necessarily imply covariance of local structure with behavioral performance in healthy adults. We discuss the limitations of such studies and related recommendations for future studies. 13 p.

Research on the neural correlates of anosognosia in Alzheimer's disease varied according to methods and objectives: they compared different measures, used diverse neuroimaging modalities, explored connectivity between brain networks, addressed the role of specific brain regions or tried to give support to theoretical models of unawareness. We used resting‐state fMRI connectivity with two different seed regions and two measures of anosognosia in different patient samples to investigate consistent modifications of default mode subnetworks and we aligned the results with the Cognitive Awareness Model. In a first study, patients and their relatives were presented with the Memory Awareness Rating Scale. Anosognosia was measured as a patient‐relative discrepancy score and connectivity was investigated with a parahippocampal seed. In a second study, anosognosia was measured in patients with brain amyloid (taken as a disease biomarker) by comparing self‐reported rating with memory performance, and connectivity was examined with a hippocampal seed. In both studies, anosognosia was consistently related to disconnection within the medial temporal subsystem of the default mode network, subserving episodic memory processes. Importantly, scores were also related to disconnection between the medial temporal and both the core subsystem (participating to self‐reflection) and the dorsomedial subsystem of the default mode network (the middle temporal gyrus that might subserve a personal database in the second study). We suggest that disparity in connectivity within and between subsystems of the default mode network may reflect impaired functioning of pathways in cognitive models of awareness.

Sex/gender differences in the human brain attract attention far beyond the neuroscience community. Given the interest of nonspecialists, it is important that researchers studying human female–male brain difference assume greater responsibility for the accurate communication of their findings., Mind your language! This Perspective article points out that the source of misreporting in the field of sex/gender differences can often be found in the unjustified overstatements used by researchers themselves in reporting their findings; caution is needed when addressing differences between human beings, and hype and hyperbole should be avoided.

International audience; Patients with Alzheimer's disease (AD) show impairment of episodic memory and related metacognitive processes. The present study examined subjective metacognitive judgments preceding objective memory retrieval and investigated the neural correlates of pessimistic predictions for successfully retrieved memories in AD patients. AD patients and healthy older (HO) participants provided predictive judgments on their recognition performance before retrieval of famous (semantic) and recently learned (episodic) names. Correlations between gray matter volume (GMV) in T1 images and behavioral scores were examined with multivariate (partial least square - PLS) and univariate (general linear model - GLM) analyses in AD patients. AD patients showed a significant proportion of successful name recognition preceded by pessimistic prediction in episodic memory. PLS revealed that the behavioral pattern in AD patients was related with a mainly right lateralized pattern of GMV decrease including medial temporal lobe and posterior cingulate cortex (PCC), but also right ventrolateral prefrontal cortex (VLPFC). GLM further confirmed that pessimistic prediction negatively correlated with GMV in VLPFC. Thus, impaired monitoring processes (possibly influenced by inaccurate beliefs) allowing inferences about one's own memory performance are primarily related to decrease GMV in VLPFC in AD patients.

Item does not contain fulltext Meta-analytic techniques allow cognitive neuroscientists to pool large amounts of data across many individual task-based functional neuroimaging experiments. These methods have been aided by the introduction of online databases such as Brainmap.org or Neurosynth.org, which collate peak activation coordinates obtained from thousands of published studies. Findings from meta-analytic studies typically include brain regions which are consistently activated across studies for specific contrasts, investigating cognitive or clinical hypotheses. These regions can be subsequently used as the basis for seed-based connectivity analysis, or formally compared to neuroimaging data in order to help interpret new findings. To facilitate such approaches, we have developed a new online repository of meta-analytic neuroimaging results, named the Archive of Neuroimaging Meta-analyses (ANIMA). The ANIMA platform consists of an intuitive online interface for querying, downloading, and contributing data from published meta-analytic studies. Additionally, to aid the process of organizing, visualizing, and working with these data, we present an open-source desktop application called Volume Viewer. Volume Viewer allows users to easily arrange imaging data into composite stacks, and save these sessions as individual files, which can also be uploaded to the ANIMA database. The application also allows users to perform basic functions, such as computing conjunctions between images, or extracting regions-of-interest or peak coordinates for further analysis. The introduction of this new resource will enhance the ability of researchers to both share their findings and incorporate existing meta-analytic results into their own research. 9 p.

Linking interindividual differences in psychological phenotype to variations in brain structure is an old dream for psychology and a crucial question for cognitive neurosciences. Yet, replicability of the previously-reported “structural brain behavior” (SBB)-associations has been questioned, recently. Here, we conducted an empirical investigation, assessing replicability of SBB among heathy adults. For a wide range of psychological measures, the replicability of associations with gray matter volume was assessed. Our results revealed that among healthy individuals 1) finding an association between performance at standard psychological tests and brain morphology is relatively unlikely 2) significant associations, found using an exploratory approach, have overestimated effect sizes and 3) can hardly be replicated in an independent sample. After considering factors such as sample size and comparing our findings with more replicable SBB-associations in a clinical cohort and replicable associations between brain structure and non-psychological phenotype, we discuss the potential causes and consequences of these findings.

Many brain regions have been defined, but a comprehensive formalization of each region's function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have diverse conceptions of 'functions'. We briefly review these fields and outline how the heterogeneity of associations could be harnessed to disclose the computational function of any region. Aggregating activation data from neuroimaging studies allows us to characterize the functional engagement of a region across a range of experimental conditions. Furthermore, large-sample data can disclose covariation between brain region features and ecological behavioral phenotyping. Combining these two approaches opens a new perspective to determine the behavioral associations of a brain region, and hence its function and broader role within large-scale functional networks.

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals’ age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer’s disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50 to 690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging.

Functional magnetic resonance imaging (fMRI) plays a vital role in understanding normal and clinical brain function and relies on detecting changes in blood oxygenation (i.e., BOLD). Synchronized signal fluctuations can be observed even when the subject is at rest, i.e., without performing any task. Therefore, analyzing resting-state data has become one way of studying ongoing brain activity and interrelation of brain regions. Apparent brain activity can be influenced by unwanted signals including system noise, thermal noise and noise induced by non-neuronal physiological processes. The latter induced noise is mostly the unwanted noise that affects the BOLD signal. Globally influencing nuisance regressors are derived from either whole brain or specific tissues types and removed from the main signal ( Behzadi et al., 2007 , Murphy et al., 2009 ). Removal of various global nuisance regressors alters the variance of the residual signal. Functional connectivity (FC) in general measures, how well different regions in the brain relate to each other. It estimates the common variance of the signal fluctuations within these regions by linear correlation. In this study, we tested the reliability of FC after removing confounding noise regressors. Here we focused on the effects of various commonly used confound removals in the resting state studies, such as PCA de-noising, global mean signal regression including white matter (WM) and CSF mean signal regression, tissue signal regression (Grey matter (GM), WM and CSF). Additionally, we examined GM specific time series extraction from seed regions. We conducted a seed based FC analysis on 42 subjects scanned twice with an interval of 100–250 day and tested the reliability between scans. In order to compute the seed based functional connectivity, a priori defined networks were analyzed (extended socioaffective default mode network and the working-memory network). Both these networks show robust within network resting state connectivity, as well as anti-correlation between each other. The reliability of functional connectivity is measured using two different measures i.e., by computing the spearman correlations and the absolute differences of the functional connectivity scores. Our results ( Fig. 1 ) showed that GM masking of the seed regions based on the group-average GM probabilities is advisable. Also, PCA de-noising reduces the reliability of connectivity estimates. Finally, with respect to global signal regression, we observed that refraining from this approach enhances test-retest reliability but comes at the expense of potentially poorer biological validity, indicated by missing anti-correlations between what has been previously described as antagonistic networks. Here removal of global WM and CSF signals seems to provide a good compromise, as this approach yielded reliable and meaningful estimates of within and between-network connections. Importantly, reliability showed correlation with the retained variance, presumably including structured noise. Consequently, noise removal from fMRI data requires a compromise between maximizing the test-retest reliability and removing variance that may be attributable to non-neuronal sources.

Contains fulltext : 169032.pdf (Publisher’s version ) (Closed access) The right dorsal premotor cortex (PMd) of humans has been reported to be involved in a broad range of motor and cognitive functions. We explored the basis of this behavioral heterogeneity by performing a connectivity-based parcellation using meta-analytic approach applied to PMd coactivations. We compared our connectivity-based parcellation results with parcellations obtained through resting-state functional connectivity and probabilistic diffusion tractography. Functional connectivity profiles and behavioral decoding of the resulting PMd subregions allowed characterizing their respective behavior profile. These procedures divided the right PMd into 5 distinct subregions that formed a cognitive-motor gradient along a rostro-caudal axis. In particular, we found 1) a rostral subregion functionally connected with prefrontal cortex, which likely supports high-level cognitive processes, such as working memory, 2) a central subregion showing a mixed behavioral profile and functional connectivity to parietal regions of the dorsal attention network, and 3) a caudal subregion closely integrated with the motor system. Additionally, we found 4) a dorsal subregion, preferentially related to hand movements and connected to both cognitive and motor regions, and 5) a ventral subregion, whose functional profile fits the concept of an eye movement-related field. In conclusion, right PMd may be considered as a functional mosaic formed by 5 subregions. 16 p.