Nicolas Delcroix

Brain activity at rest: A multiscale hierarchical functional organization.

Spontaneous brain activity was mapped with functional MRI (fMRI) in a sample of 180 subjects while in a conscious resting-state condition. With the use of independent component analysis (ICA) of each individual fMRI signal and classification of the ICA-defined components across subjects, a set of 23 resting-state networks (RNs) was identified. Functional connectivity between each pair of RNs was assessed using temporal correlation analyses in the 0.01- to 0.1-Hz frequency band, and the corresponding set of correlation coefficients was used to obtain a hierarchical clustering of the 23 RNs. At the highest hierarchical level, we found two anticorrelated systems in charge of intrinsic and extrinsic processing, respectively. At a lower level, the intrinsic system appears to be partitioned in three modules that subserve generation of spontaneous thoughts (M1a; default mode), inner maintenance and manipulation of information (M1b), and cognitive control and switching activity (M1c), respectively. The extrinsic system was found to be made of two distinct modules: one including primary somatosensory and auditory areas and the dorsal attentional network (M2a) and the other encompassing the visual areas (M2b). Functional connectivity analyses revealed that M1b played a central role in the functioning of the intrinsic system, whereas M1c seems to mediate exchange of information between the intrinsic and extrinsic systems.

The resting state questionnaire: An introspective questionnaire for evaluation of inner experience during the conscious resting state

We designed a semi-structured questionnaire for the introspective evaluation of inner experience of participants undergoing functional magnetic resonance imaging (fMRI) in the resting state. This resting state questionnaire (ReSQ) consists of 62 items organized by five main types of mental activity: visual mental imagery (IMAG); inner language (LANG), split into two subtypes, inner speech (SPEE) and auditory mental imagery (AUDI); somatosensory awareness (SOMA); inner musical experience (MUSI); and mental manipulation of numbers (NUMB). For IMAG and LANG, additional questions estimated association of such activities with ongoing learning, retrospective memories, or prospective thoughts. Using a 0-100% scale, the participant quantitatively rated the proportion of time spent in each mental activity during the resting state fMRI acquisition. A total of 180 healthy volunteers completed the ReSQ immediately after being scanned with fMRI while at rest. Of these, 66% exhibited dominance of a type of mental activity at rest (IMAG: 35%; LANG: 17%; SOMA: 7%; MUSI: 6%; NUMB: 1%). A majority of participants reported either retrospective memories (82%) or prospective thoughts (78%), with 58% of participants reporting both in at least one type of mental activity. Thoughts related to ongoing learning were low (37% of participants). The present results are consistent with those of previous studies investigating inner experience in a natural environment. In conclusion, we provide a robust and easy-to-implement tool for the exploration of mental activities during rest of healthy participants undergoing fMRI. This tool relies on normative data acquired from a 180-participant sample balanced for sex and handedness.

Links among resting-state default-mode network, salience network, and symptomatology in schizophrenia

Neuroimaging data support the idea that schizophrenia is a brain disorder with altered brain structure and function. New resting-state functional connectivity techniques allow us to highlight synchronization of large-scale networks, such as the default-mode network (DMN) and salience network (SN). A large body of work suggests that disruption of these networks could give rise to specific schizophrenia symptoms. We examined the intra-network connectivity strength and gray matter content (GMC) of DMN and SN in 26 schizophrenia patients using resting-state functional magnetic resonance imaging and voxel-based morphometry. Resting-state data were analyzed with independent component analysis and dual-regression techniques. We reported reduced functional connectivity within both DMN and SN in patients with schizophrenia. Concerning the DMN, patients showed weaker connectivity in a cluster located in the right paracingulate cortex. Moreover, patients showed decreased GMC in this cluster. With regard to the SN, patients showed reduced connectivity in the left and right striatum. Decreased connectivity in the paracingulate cortex was correlated with difficulties in abstract thinking. The connectivity decrease in the left striatum was correlated with delusion and depression scores. Correlation between the connectivity of DMN frontal regions and difficulties in abstract thinking emphasizes the link between negative symptoms and the likely alteration of the frontal medial cortex in schizophrenia. Correlation between the connectivity of SN striatal regions and delusions supports the aberrant salience hypothesis. This work provides new insights into dysfunctional brain organization in schizophrenia and its contribution to specific schizophrenia symptoms.

An automated procedure for the assessment of white matter hyperintensities by multispectral (T1, T2, PD) MRI and an evaluation of its between-centre reproducibility based on two large community databases

IntroductionAn automated procedure for the detection, quantification, localization and statistical mapping of white matter hyperintensities (WMH) on T2-weighted magnetic resonance (MR) images is presented and validated based on the results of a between-centre reproducibility study.MethodsThe first step is the identification of white matter (WM) tissue using a multispectral (T1, T2, PD) segmentation. In a second step, WMH are identified within the WM tissue by segmenting T2 images, isolating two different classes of WMH voxels – low- and high-contrast WMH voxels, respectively. The reliability of the whole procedure was assessed by applying it to the analysis of two large MR imaging databases (n = 650 and n= 710, respectively) of healthy elderly subjects matched for demographic characteristics.ResultsAverage overall WMH load and spatial distribution were found to be similar in the two samples, (1.81 and 1.79% of the WM volume, respectively). White matter hyperintensity load was found to be significantly associated with both age and high blood pressure, with similar effects in both samples. With specific reference to the 650 subject cohort, we also found that WMH load provided by this automated procedure was significantly associated with visual grading of the severity of WMH, as assessed by a trained neurologist.ConclusionThe results show that this method is sensitive, well correlated with semi-quantitative visual rating and highly reproducible.

Effects of ApoE-ɛ4 allele load and age on the rates of grey matter and hippocampal volumes loss in a longitudinal cohort of 1186 healthy elderly persons

In a sample of 1186 healthy subjects aged 65 to 89 years who were scanned twice with MRI 3.6 years apart, we studied the effects of age and ApoE-epsilon4 allele load on the rate of atrophy of grey matter and hippocampus. Rates of grey matter and hippocampal volumes loss were computed from T1-weighted magnetic resonance images using voxel-based morphometry and region of interest analysis. Longitudinal analysis showed that an age-related annual rate of grey matter volume loss was only seen in epsilon4 homozygotes only (n=14) whereas no age effect was seen epsilon4 heterozygotes (n=239) and in noncarriers (n=933). ApoE-epsilon4 homozygotes also had a significantly larger rate of hippocampal volume loss than heterozygotes or noncarriers. During the same period, no effect or interaction of ApoE genotype and age was observed on cognitive decline, as assessed by the Mini Mental State Examination (MMSE). These data do not suggest an epsilon4 gene dose effect on the rate of hippocampal volume loss in healthy elderly subjects as most of the effect was limited to homozygotes. Hippocampal volume loss may not be a good imaging marker to understand the effect of the ApoE-epsilon4 allele on the risk of dementia in a population-based setting. It could be hypothesized that the impact of a single ApoE-epsilon4 allele on brain structures is largely delayed in time.

Functional Magnetic Resonance Imaging Study of Piaget's Conservation-of-Number Task in Preschool and School-Age Children: A Neo-Piagetian Approach.

Jean Piagets theory is a central reference point in the study of logico-mathematical development in children. One of the most famous Piagetian tasks is number conservation. Failures and successes in this task reveal two fundamental stages in childrens thinking and judgment, shifting at approximately 7 years of age from visuospatial intuition to number conservation. In the current study, preschool children (nonconservers, 5-6 years of age) and school-age children (conservers, 9-10 years of age) were presented with Piagets conservation-of-number task and monitored by functional magnetic resonance imaging (fMRI). The cognitive change allowing children to access conservation was shown to be related to the neural contribution of a bilateral parietofrontal network involved in numerical and executive functions. These fMRI results highlight how the behavioral and cognitive stages Piaget formulated during the 20th century manifest in the brain with age.

AICHA: An atlas of intrinsic connectivity of homotopic areas.

BACKGROUND Atlases of brain anatomical ROIs are widely used for functional MRI data analysis. Recently, it was proposed that an atlas of ROIs derived from a functional brain parcellation could be advantageous, in particular for understanding how different regions share information. However, functional atlases so far proposed do not account for a crucial aspect of cerebral organization, namely homotopy, i.e. that each region in one hemisphere has a homologue in the other hemisphere. NEW METHOD We present AICHA (for Atlas of Intrinsic Connectivity of Homotopic Areas), a functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover the whole cerebrum, each having 1-homogeneity of its constituting voxels intrinsic activity, and 2-a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA was built in 4 steps: (1) estimation of resting-state networks (RSNs) using individual resting-state fMRI independent components, (2) k-means clustering of voxel-wise group level profiles of connectivity, (3) homotopic regional grouping based on maximal inter-hemispheric functional correlation, and (4) ROI labeling. RESULTS AICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 gray nuclei). As an application, we report inter-hemispheric (homotopic and heterotopic) and intra-hemispheric connectivity patterns at different sparsities. COMPARISON WITH EXISTING METHOD ROI functional homogeneity was higher for AICHA than for anatomical ROI atlases, but slightly lower than for another functional ROI atlas not accounting for homotopy. CONCLUSION AICHA is ideally suited for intrinsic/effective connectivity analyses, as well as for investigating brain hemispheric specialization.

A Novel Group ICA Approach Based on Multi-scale Individual Component Clustering. Application to a Large Sample of fMRI Data

Functional connectivity-based analysis of functional magnetic resonance imaging data (fMRI) is an emerging technique for human brain mapping. One powerful method for the investigation of functional connectivity is independent component analysis (ICA) of concatenated data. However, this research field is evolving toward processing increasingly larger database taking into account inter-individual variability. Concatenated data analysis only handles these features using some additional procedures such as bootstrap or including a model of between-subject variability during the preprocessing step of the ICA. In order to alleviate these limitations, we propose a method based on group analysis of individual ICA components, using a multi-scale clustering (MICCA). MICCA start with two steps repeated several times: 1) single subject data ICA followed by 2) clustering of all subject independent components according to a spatial similarity criterion. A final third step consists in selecting reproducible clusters across the repetitions of the two previous steps. The core of the innovation lies in the multi-scale and unsupervised clustering algorithm built as a chain of three processes: robust proto-cluster creation, aggregation of the proto-clusters, and cluster consolidation. We applied MICCA to the analysis of 310 fMRI resting state dataset. MICCA identified 28 resting state brain networks. Overall, the cluster neuroanatomical substrate included 98% of the cerebrum gray matter. MICCA results proved to be reproducible in a random splitting of the data sample and more robust than the classical concatenation method.

Relationships between corpus callosum and language lateralization in patients with schizophrenia and bipolar disorders

The question of whether there is a continuum or a dichotomy among patients with schizophrenia (SZ) and bipolar disorders (BD) has not been clearly resolved and remains a challenge. Thus, the identification of specific biomarkers of these disorders might be helpful. The present study investigated the volume of the corpus callosum (CC) and functional lateralization for language as potential biomarkers and their relationships in SZ and BD.

Helping reasoners succeed in the Wason selection task: when executive learning discourages heuristic response but does not necessarily encourage logic.

Reasoners make systematic logical errors by giving heuristic responses that reflect deviations from the logical norm. Influential studies have suggested first that our reasoning is often biased because we minimize cognitive effort to surpass a cognitive conflict between heuristic response from system 1 and analytic response from system 2 thinking. Additionally, cognitive control processes might be necessary to inhibit system 1 responses to activate a system 2 response. Previous studies have shown a significant effect of executive learning (EL) on adults who have transferred knowledge acquired on the Wason selection task (WST) to another isomorphic task, the rule falsification task (RFT). The original paradigm consisted of teaching participants to inhibit a classical matching heuristic that sufficed the first problem and led to significant EL transfer on the second problem. Interestingly, the reasoning tasks differed in inhibiting-heuristic metacognitive cost. Success on the WST requires half-suppression of the matching elements. In contrast, the RFT necessitates a global rejection of the matching elements for a correct answer. Therefore, metacognitive learning difficulty most likely differs depending on whether one uses the first or second task during the learning phase. We aimed to investigate this difficulty and various matching-bias inhibition effects in a new (reversed) paradigm. In this case, the transfer effect from the RFT to the WST could be more difficult because the reasoner learns to reject all matching elements in the first task. We observed that the EL leads to a significant reduction in matching selections on the WST without increasing logical performances. Interestingly, the acquired metacognitive knowledge was too “strictly” transferred and discouraged matching rather than encouraging logic. This finding underlines the complexity of learning transfer and adds new evidence to the pedagogy of reasoning.