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Identifying associations between interindividual variability in brain structure and behaviour requires large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of-cohort replication. Moreover, the influence of nature vs nurture on brain-behaviour associations should be analysed. We analysed associations between brain structure (grey matter volume, cortical thickness, and surface area) and behaviour (spanning cognition, emotion, and alertness) using regularized canonical correlation analysis and a machine learning framework that tests the generalisability and stability of such associations. The replicability of brain-behaviour associations was assessed in two large, independent cohorts. The load of genetic factors on these associations was analysed with heritability and genetic correlation. We found one heritable and replicable latent dimension linking cognitive-control/executive-functions and positive affect to brain structural variability in areas typically associated with higher cognitive functions, and with areas typically associated with sensorimotor functions. These results revealed a major axis of interindividual behavioural variability linking to a whole-brain structural pattern.

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.

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.

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.

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.

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.

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.

The hippocampus is a plastic brain structure that has been associated with a range of behavioral aspects but also shows vulnerability to the most frequent neurocognitive diseases. Different aspects of its organization have been revealed by studies probing its different neurobiological properties. In particular, histological work has shown a pattern of differentiation along the proximal-distal dimension, while studies examining functional properties and large-scale functional integration have primarily highlighted a pattern of differentiation along the anterior-posterior dimension. To better understand how these organizational dimensions underlie the pattern of structural covariance (SC) in the human hippocampus, we here applied a non-linear decomposition approach, disentangling the major modes of variation, to the pattern of gray matter volume correlation of hippocampus voxels with the rest of the brain in a sample of 377 healthy young adults. We additionally investigated the consistency of the derived gradients in an independent sample of life-span adults and also examined the relationships between these major modes of variations and the patterns derived from microstructure and functional connectivity mapping. Our results showed that similar major modes of SC-variability are identified across the two independent datasets. The major dimension of variation found in SC runs along the hippocampal anterior-posterior axis and followed closely the principal dimension of functional differentiation, suggesting an influence of network level interaction in this major mode of morphological variability. The second main mode of variability in the SC showed a gradient along the dorsal-ventral axis, and was moderately related to variability in hippocampal microstructural properties. Thus our results depicting relatively reliable patterns of SC-variability within the hippocampus show an interplay between the already known organizational principles on the pattern of variability in hippocampus' macrostructural properties. This study hence provides a first insight on the underlying organizational forces generating different co-plastic modes within the human hippocampus that may, in turn, help to better understand different vulnerability patterns of this crucial structure in different neurological and psychiatric diseases.

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.

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.

This study investigated the impact of Alzheimer׳s disease (AD) on conjunctive and relational binding in episodic memory. Mild AD patients and controls had to remember item–color associations by imagining color either as a contextual association (relational memory) or as a feature of the item to be encoded (conjunctive memory). Patients׳ performance in each condition was correlated with cerebral metabolism measured by FDG-PET. The results showed that AD patients had an impaired capacity to remember item–color associations, with deficits in both relational and conjunctive memory. However, performance in the two kinds of associative memory varied independently across patients. Partial Least Square analyses revealed that poor conjunctive memory was related to hypometabolism in an anterior temporal-posterior fusiform brain network, whereas relational memory correlated with metabolism in regions of the default mode network. These findings support the hypothesis of distinct neural systems specialized in different types of associative memory and point to heterogeneous profiles of memory alteration in Alzheimer׳s disease as a function of damage to the respective neural networks.

Typical Alzheimer's disease (AD) is characterized by an impaired form of associative memory, recollection, that includes the controlled retrieval of associations. In contrast, familiarity-based memory for individual items may sometimes be preserved in the early stages of the disease. This is the first study that directly examines whole-brain regional activity during one core aspect of the recollection function: associative controlled episodic retrieval (CER), contrasted to item familiarity in AD patients. Cerebral activity related to associative CER and item familiarity in AD patients and healthy controls (HCs) was measured with functional magnetic resonance imaging during a word-pair recognition task to which the process dissociation procedure was applied. Some patients had null CER estimates (AD-), whereas others did show some CER abilities (AD+), although significantly less than HC. In contrast, familiarity estimates were equivalent in the three groups. In AD+, as in controls, associative CER activated the inferior precuneus/posterior cingulate cortex (PCC). When performing group comparisons, no region was found to be significantly more activated during CER in HC than AD+ and vice versa. However, during associative CER, functional connectivity between this region and the hippocampus, the inferior parietal and dorsolateral prefrontal cortex (DLPFC) was significantly higher in HC than in AD+. In all three groups, item familiarity was related to activation along the intraparietal sulcus (IPS). In conclusion, whereas the preserved automatic detection of an old item (without retrieval of accurate word association) is related to parietal activation centred on the IPS, the inferior precuneus/PCC supports associative CER ability in AD patients, as in HC. However, AD patients have deficient functional connectivity during associative CER, suggesting that the residual recollection function in these patients might be impoverished by the lack of some recollection-related aspects such as autonoetic quality, episodic details and verification.

The aim of this study was to examine correlations between acquisition and short-delay consolidation and brain metabolism at rest measured by fluorodeoxyglucose positron emission tomography (FDG-PET) in 44 Alzheimer's disease (AD) patients, 16 patients with mild cognitive impairment (MCI) who progressed to dementia (MCI-AD), 15 MCI patients who remained stable (MCI-S, 4-8 years of follow-up), and 20 healthy older participants. Acquisition and short-delay consolidation were calculated respectively as mean gained (MG) and lost (ML) access to items of the California Verbal Learning Task. MG performance suggests that acquisition is impaired in AD patients even at predementia stage (MCI-AD). ML performance suggests that short-delay consolidation is deficient only in confirmed AD patients. Variations in acquisition performance in control participants are related to metabolic activity in the anterior parietal cortex, an area supporting task-positive attentional processes. In contrast, the acquisition deficit is related to decreased activity in the lateral temporal cortex, an area supporting semantic processes, in patients at an early stage of AD and is related to metabolic activity in the hippocampus, an area supporting associative processes, in confirmed AD patients.

Contains fulltext : 168981.pdf (Publisher’s version ) (Closed access) Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test-retest reliability of functional-connectivity estimates in two previously defined functional brain networks. Our results showed that gray matter masking improved the reliability of connectivity estimates, whereas denoising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test–retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test-retest reliability and removing variance that may be attributable to non-neuronal sources. 22 p.

Item does not contain fulltext Little is known about how several functions are organized or how putative subregions are coupled with wider brain networks in the human right PMd. To address these issues, we performed connectivity-based parcellation (CBP) based on PMd voxels' coactivations yielded by meta-analytic connectivity modeling (MACM). We additionally search for a similar cluster solution with CBP based on resting-state functional connectivity (RSFC) and probabilistic diffusion tractography. MACM and RSFC assessed FC of the MACM-CBP-derived subregions while functional characterization revealed their behavioral associations. This procedure showed that the right PMd could be divided into five distinct clusters highlighting a cognitive-motor gradient along a rostro-caudal axis including a rostral cluster functionally interacting with prefrontal cortex in supporting high-level cognitive processes and a caudal cluster closely integrated with the motor system. MACM-CBP also revealed a ventral cluster, whose functional profile fits the concept of an eye field, and a dorsal cluster, preferentially hand-related and connected to both cognitive and motor regions. Finally, MACM-CBP yielded a central cluster showing a mixed behavioral profile and FC to parietal regions of the dorsal attention network. Thus, our results showed that the right PMd may be considered as a functional mosaic formed by five subregions. 1 p.

Item does not contain fulltext Recently, we functionally characterized five right dorsal premotor (PMdr) subregions (identified by coactivation based parcellation) by quantitative forward and reverse inference based on a wide range of task-based activation studies (Genon et al, in prep.). We found that the rostral subregion was associated to high-level cognitive tasks, the caudal one to motor functions, the dorsal one to hand-movements, the ventral one to visual functions while the central one showed a heterogeneous profile. Here, we aimed to reinforce and complement this characterization by using correlations between brain morphology and standard neuropsychological tests in two independent large datasets, which were matched for age, gender, education, depression and handedness (Forschungszentrum Jülich [FZJ, n=87, age range: 21-71] and Nathan Kline Institute [NKI; 135: 20-75]). T1 images were processed with the VBM8 toolbox (Friston using linear and non-linear modulation). The five PMdr subregions were used as regions of interest (ROIs), from which regional gray matter volume (GMV) was extracted. We performed partial correlations to examine correlation between each ROI GMV and neuropsychological test performance when controlling for confounding effects of age, education and handedness. To further examine the reliability of correlation, we also performed the analyses in 1000 subsamples of 30 randomly selected subjects within each sample individually. Our analyses yielded generally low correlation coefficients of which only few were statistically significant even at a lenient threshold of p < .05, uncorrected for multiple testing. Notably, a considerable number of correlations showed an inverse relationship between test performance and brain structure, i.e., higher performance was associated with lower GMV. In addition, the patterns of correlations were inconsistent across samples in magnitude, significance and direction. Finally, subsampling revealed that the pattern of correlations that could be observed varied widely when analyses were based on randomly drawn small samples. Our correlational approach, relating GMV and performance across a wide range of standard neuropsychological tests in two large population-based samples did not corroborate the profiles previously revealed using functional decoding based on fMRI-activations. On the one hand, these findings suggest that the functional specialization of some cortical regions, as highlighted by fMRI studies, does not necessarily imply a significant covariance of their actual structure with related standard task performance across individuals in the healthy population. That is, a cognitive-morphologic approach based on healthy interindividual differences may not mirror functional characterization revealed by activation studies in some brain regions. On the other hand, the fact that we found (all kinds of) significant associations in the smaller, randomly assembled subsamples also highlights the high likelihood of spurious findings when performing such correlation analyses in small samples as prevalent in current neuroimaging studies. 2 p.

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. 2 p.

et al., in press). In a recent study, we suggested that selfreferential processing at encoding is related to the engagement of the ventromedial prefrontal cortex (VMPFC) in AD patients as it is in healthy older people, whereas reduced and variable SRE in AD patients is related to grey matter volume reduction in the lateral prefrontal cortex (LPFC) across the middle and superior frontal gyri (Genon et al., in press). In a relevant commentary on our study, Irish (in press) insightfully raised the issue of the medial temporal lobe and posterior cingulate cortex (PCC)’s role in the impaired interaction between self andmemory in AD patients. Indeed, given

Introduction Genetic variability related to the catechol-O-methyltransferase (COMT) gene (Val 158 Met polymorphism) has received increasing attention as a possible modulator of cognitive control functions. Methods In an event-related functional magnetic resonance imaging (fMRI) study, a modified version of the Stroop task was administered to three groups of 15 young adults according to their COMT Val 158 Met genotype [Val/Val (VV), Val/Met (VM) and Met/Met (MM)]. Based on the theory of dual mechanisms of control ( Braver et al., 2007 ), the Stroop task has been built to induce proactive or reactive control processes according to the task context. Results Behavioral results did not show any significant group differences for reaction times but Val allele carriers individuals are less accurate in the processing of incongruent items. fMRI results revealed that proactive control is specifically associated with increased activity in the anterior cingulate cortex (ACC) in carriers of the Met allele, while increased activity is observed in the middle frontal gyrus (MFG) in carriers of the Val allele. Conclusion These observations, in keeping with a higher cortical dopamine level in MM individuals, support the hypothesis of a COMT Val 158 Met genotype modulation of the brain regions underlying proactive control, especially in frontal areas as suggested by Braver et al.

Recognition memory can rely on recollection (recall of the details from the encoding episode) and familiarity (feeling that some information is old without any recollection). In Alzheimer's disease (AD), where there is a clear deficit of recollection, the evidence regarding familiarity is mixed, with some studies showing preserved familiarity and others reporting impairment. The current study examined whether recognition memory performance can be improved in AD when the use of familiarity is facilitated by the salience of processing fluency due to an earlier encounter with the information. Fifteen AD patients and 16 healthy controls performed a verbal recognition memory task where the salience of fluency was manipulated by means of letters overlap. Studied and unstudied words were constituted of either two separate sets of letters (no-overlap condition, high fluency salience) or the same set of letters (overlap condition, low fluency salience). The results showed that, although performance was globally poorer in AD patients than in the controls, both groups performed significantly better in the no-overlap condition than in the overlap condition. This suggests that AD patients benefited as much as the controls from the salience of fluency. 8 9 10 11 12 13 14 15 16 17

Typical Alzheimer's disease (AD) is characterized by an impaired form of associative memory, recollection, that includes the controlled retrieval of associations. In contrast, familiarity-based memory for individual items may sometimes be preserved in the early stages of the disease. This is the first study that directly examines whole-brain regional activity during one core aspect of the recollection function: associative controlled episodic retrieval (CER), contrasted to item familiarity in AD patients. Cerebral activity related to associative CER and item familiarity in AD patients and healthy controls (HCs) was measured with functional magnetic resonance imaging during a word-pair recognition task to which the process dissociation procedure was applied. Some patients had null CER estimates (AD-), whereas others did show some CER abilities (AD+), although significantly less than HC. In contrast, familiarity estimates were equivalent in the three groups. In AD+, as in controls, associative CER activated the inferior precuneus/posterior cingulate cortex (PCC). When performing group comparisons, no region was found to be significantly more activated during CER in HC than AD+ and vice versa. However, during associative CER, functional connectivity between this region and the hippocampus, the inferior parietal and dorsolateral prefrontal cortex (DLPFC) was significantly higher in HC than in AD+. In all three groups, item familiarity was related to activation along the intraparietal sulcus (IPS). In conclusion, whereas the preserved automatic detection of an old item (without retrieval of accurate word association) is related to parietal activation centred on the IPS, the inferior precuneus/PCC supports associative CER ability in AD patients, as in HC. However, AD patients have deficient functional connectivity during associative CER, suggesting that the residual recollection function in these patients might be impoverished by the lack of some recollection-related aspects such as autonoetic quality, episodic details and verification.

Introduction Un deficit en memoire episodique est caracteristique dans la maladie d’Alzheimer (AD) et chez les patients âges presentant une alteration cognitive legere (MCI). Cette alteration peut s’expliquer par un deficit d’acquisition et/ou un deficit de consolidation. Toutefois, les modifications cerebrales responsables de ces deficits ne sont pas encore parfaitement elucidees. L’objectif de notre etude etait de mettre en relation le profil d’apprentissage deficitaire de ces patients avec leur metabolisme cerebral au repos. Methode L’epreuve du California Verbal Learning Test (CVLT) a ete administree a 51 patients AD, 18 patients MCI qui sont restes stables (suivi longitudinal de 18 mois, MCI-S), 16 patients MCI qui ont developpe la maladie d’Alzheimer (MCI-C) durant les 18 mois de suivi et 12 participants de controle. La mesure d’acquisition est la proportion moyenne de gain a travers les 5 essais d’apprentissage du CVLT. La mesure de consolidation est la proportion totale de pertes a travers ces 5 essais. La mesure du metabolisme cerebral au repos a ete effectuee en tomographie par emission de positions (18FDG-TEP). Les mesures de correlation cognitivometabolique ont ete realisees au moyen du logiciel SPM8 (p non-corrige avec hypothese a priori Resultats Les groupes AD et MCI-C ont un gain moyen moindre que les groupes MCI-S et controles. L’ampleur du gain inter-essais est positivement correlee a l’activite metabolique au niveau de l’hippocampe posterieur chez les patients AD, a l’activite parietale inferieure chez les patients MCI-S, et a l’activite frontale posterieure dans le groupe controle. Par ailleurs, le groupe AD presente plus de pertes inter-essais que les trois autres groupes. Toutefois, aucune correlation significative apparait entre le total des pertes inter-essais et le metabolisme cerebral. Discussion Ces donnees suggerent que les patients AD, meme a un stade tres precoce (MCI-C) presentent un deficit d’acquisition de l’information. Ce deficit pourrait etre soustendu par des perturbations metaboliques dans le reseau d’apprentissage fronto-parietal et hippocampique. Il existe de plus un deficit de consolidation inter-essais specifique aux patients AD mais ce deficit n’est correle a aucune region cerebrale au seuil statistique utilise.