Subjective cognitive decline, a perceived worsening of cognitive functioning without objective deficit onassessment, could indicate incipient dementia. However, the neural correlates of subjective... Show moreSubjective cognitive decline, a perceived worsening of cognitive functioning without objective deficit onassessment, could indicate incipient dementia. However, the neural correlates of subjective cognitive decline asassessed by magnetic resonance imaging remain somewhat unclear. Here, we evaluated differences in functionalconnectivity across memory regions, and cognitive performance, between healthy older adults aged 50 to 85 with(n¼35,Age¼68.57.7, 22 female), and without (n¼48,Age¼67.08.8, 29 female) subjective cognitivedecline. We also evaluated neurite density, fractional anisotropy, and mean diffusivity of the parahippocampalcingulum, cingulate gyrus cingulum, and uncinatefiber bundles in a subsample of participants (n¼37). Partic-ipants with subjective cognitive decline displayed lower average functional connectivity across regions of a pu-tative posterior memory system, and lower retrosplenial-precuneus functional connectivity specifically, than thosewithout memory complaints. Furthermore, participants with subjective cognitive decline performed poorer thancontrols on visual working memory. However, groups did not differ in cingulum or uncinate diffusion measures.Our results show differences in functional connectivity and visual working memory in participants with subjectivecognitive decline that could indicate potential incipient dementia. Show less
Palombo, M.; Shemesh, N.; Ronen, I.; Valette, J. 2018
Aging is accompanied by changes in neurotransmission. To advance our understanding of how aging modifies specific neural circuitries, we examined serotonergic and cholinergic stimulation with... Show moreAging is accompanied by changes in neurotransmission. To advance our understanding of how aging modifies specific neural circuitries, we examined serotonergic and cholinergic stimulation with resting state functional magnetic resonance imaging (RS-fMRI) in young and older adults. The instant response to the selective serotonin reuptake inhibitor citalopram (30 mg) and the acetylcholinesterase inhibitor galantamine (8 mg) was measured in 12 young and 17 older volunteers during a randomized, double blind, placebo-controlled, crossover study. A powerful dataset consisting of 522 RS-fMRI scans was obtained by acquiring multiple scans per subject before and after drug administration. Group x treatment interaction effects on voxelwise connectivity with ten functional networks were investigated (p < .05, FWE-corrected) using a non-parametric multivariate analysis technique with cerebrospinal fluid, white matter, heart rate and baseline measurements as covariates. Both groups showed a decrease in sensorimotor network connectivity after citalopram administration. The comparable findings after citalopram intake are possibly due to relatively similar serotonergic systems in the young and older subjects. Galantamine altered connectivity between the occipital visual network and regions that are implicated in learning and memory in the young subjects. The lack of a cholinergic response in the elderly might relate to the well-known association between cognitive and cholinergic deterioration at older age. Show less
Vos, F. de; Koini, M.; Schouten, T.M.; Seiler, S.; Grond, J. van der; Lechner, A.; ... ; Rombouts, S.A.R.B. 2018
Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI... Show moreAlzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly. Show less
Kepinska, O.; Rover, M. de; Caspers, J.; Schiller, N.O. 2017
Following Opitz and Friederici (2003) suggesting interactions of the hippocampal system and the prefrontal cortex as the neural mechanism underlying novel grammar learning, the present fMRI study... Show moreFollowing Opitz and Friederici (2003) suggesting interactions of the hippocampal system and the prefrontal cortex as the neural mechanism underlying novel grammar learning, the present fMRI study investigated functional connectivity of bilateral BA 44/45 and the hippocampus during an artificial grammar learning (AGL) task. Our results, contrary to the previously reported interactions, demonstrated parallel (but separate) contributions of both regions, each with their own interactions, to the process of novel grammar acquisition. The functional connectivity pattern of Broca's area pointed to the importance of coherent activity of left frontal areas around the core language processing region for successful grammar learning. Furthermore, connectivity patterns of left and right hippocampi (predominantly with occipital areas) were found to be a strong predictor of high performance on the task. Finally, increasing functional connectivity over time of both left and right BA 44/45 with the right posterior cingulate cortex and the right temporo-parietal areas points to the importance of multimodal and attentional processes supporting novel grammar acquisition. Moreover, it highlights the right-hemispheric involvement in initial stages of L2 learning. These latter interactions were found to operate irrespective of the task performance, making them an obligatory mechanism accompanying novel grammar learning. Show less
Schouten, T.M.; Koini, M.; Vos, F. de; Seiler, S.; Rooij, M. de; Lechner, A.; ... ; Rombouts, S.A.R.B. 2017
Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods... Show moreMultivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or ‘decode’ psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction—based on population-level predictive maps from prior groups—and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N = 180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker—in this case, the Neurologic Pain Signature (NPS)—improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study. Show less
Klaassens, B.L.; Gorsel, H.C. van; Khalili-Mahani, N.; Grond, J. van der; Wyman, B.T.; Whitcher, B.; ... ; Gervend, J.A. van 2015