Anatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to... Show moreAnatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to build models for discriminating AD patients from control subjects, but it is not clear if these models can also discriminate AD in diverse clinical populations as found in memory clinics.To study this, we trained MRI-based AD classification models on a single centre data set consisting of AD patients (N = 76) and controls (N = 173), and used these models to assign AD scores to subjective memory complainers (N = 67), mild cognitive impairment (MCI) patients (N = 61), and AD patients (N = 61) from a multi-centre memory clinic data set. The anatomical MRI scans were used to calculate grey matter density, subcortical volumes and cortical thickness, the diffusion MRI scans were used to calculate fractional anisotropy, mean, axial and radial diffusivity, and the rs-fMRI scans were used to calculate functional connectivity between resting state networks and amplitude of low frequency fluctuations. Within the multi-centre memory clinic data set we removed scan site differences prior to applying the models.For all models, on average, the AD patients were assigned the highest AD scores, followed by MCI patients, and later followed by SMC subjects. The anatomical MRI models performed best, and the best performing anatomical MRI measure was grey matter density, separating SMC subjects from MCI patients with an AUC of 0.69, MCI patients from AD patients with an AUC of 0.70, and SMC patients from AD patients with an AUC of 0.86. The diffusion MRI models did not generalise well to the memory clinic data, possibly because of large scan site differences. The functional connectivity model separated SMC subjects and MCI patients relatively good (AUC = 0.66). The multimodal MRI model did not improve upon the anatomical MRI model.In conclusion, we showed that the grey matter density model generalises best to memory clinic subjects. When also considering the fact that grey matter density generally performs well in AD classification studies, this feature is probably the best MRI-based feature for AD diagnosis in clinical practice. Show less
The main objective of this thesis was to investigate the serotonergic and cholinergic neurotransmitter systems, and the way these are altered in older age and Alzheimer’s disease. For that purpose,... Show moreThe main objective of this thesis was to investigate the serotonergic and cholinergic neurotransmitter systems, and the way these are altered in older age and Alzheimer’s disease. For that purpose, the neuroimaging technique resting state fMRI (RS-fMRI) was used to measure whole brain functional connectivity with and without pharmacological stimulation. The first part of the thesis concerns two pharmacological RS-fMRI studies that were executed in young adults. Pharmacological challenge effects of two selective serotonin reuptake inhibitors (sertraline and citalopram) and a cholinesterase inhibitor (galantamine) on brain connectivity were examined to gain more insight into the underlying neurotransmitter systems and the mechanisms of drug action in the central nervous system. The second part of this thesis was aimed at discovering changes in brain connectivity and serotonergic and cholinergic system functioning in aging and Alzheimer’s disease, by comparing brain network connections and the pharmacological response of this measure between young and older adults and patients with Alzheimer’s disease. 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
The present study examined the association between psychopathic traits and functional connectivity in 177 incarcerated male adolescents. We hypothesized that psychopathic symptoms would be... Show moreThe present study examined the association between psychopathic traits and functional connectivity in 177 incarcerated male adolescents. We hypothesized that psychopathic symptoms would be associated with functional connectivity within networks encompassing limbic and paralimbic regions, such as the default mode (DMN), salience networks (SN), and executive control network (ECN). The present sample was drawn from the Southwest Advanced Neuroimaging Cohort, Youth sample, and from research at a youth detention facility in Wisconsin. All participants were scanned at maximum-security facilities. Psychopathic traits were assessed using Hare's Psychopathy Checklist-Youth Version. Resting-state networks were computed using group Independent Component Analysis. Associations between psychopathic traits and resting-state connectivity were assessed using Mancova analyses. PCL-YV Total score and Factor 1 score (interpersonal and affective traits) were associated with the power spectra of the DMN. Factor 1 score was associated with SN and ECN spatial maps. Factor 2 score (lifestyle and antisocial traits) was associated with spatial map of the ECN. Only the Factor 1 association with DMN power spectrum survived correction for multiple testing. Comparable to adult psychopathy, adolescent psychopathic traits were associated with networks implicated in self-referential thought, moral behavior, cognition, and saliency detection: functions previously reported to be disrupted in adult psychopaths. Show less
This thesis describes neuroimaging techniques to investigate brain networks in healthy aging and dementia. Functional and structural brain networks change with healthy and pathological... Show more This thesis describes neuroimaging techniques to investigate brain networks in healthy aging and dementia. Functional and structural brain networks change with healthy and pathological aging, with differences in network degeneration between different types of dementia. These disease-specific network differences suggest the potential of brain networks to improve diagnostic accuracy. However, at this moment, our findings are only applicable for groups of patients and not yet suitable as a diagnostic tool on an individual basis. Show less
Dumas, E.M.; Bogaard, S.J.A. van den; Hart, E.P.; Soeter, R.P.; Buchem, M.A. van; Grond, J. van der; ... ; TRACK-HD Investigator Grp 2013
