Jeroen van der Grond

YNICL Journal 2025 Journal Article

Cross-sectional and longitudinal quantification of total white matter perivascular space volume fraction in Dutch-type Cerebral Amyloid Angiopathy

• Manon R. Schipper
• Thijs W. van Harten
• Arie-Tjerk Razoux-Schultz
• Kanishk Kaushik
• Lydiane Hirschler
• Sabine Voigt
• Ingeborg Rasing
• Emma A. Koemans

Enlarged perivascular spaces (PVS) in the centrum semiovale are an important marker of Cerebral Amyloid Angiopathy (CAA) and are thought to reflect brain clearance dysfunction. However, the current golden standard for assessing PVS is limited to a unilateral, single slice, qualitative analysis, which has the disadvantage of a strong ceiling effect. We aim to introduce a whole-brain PVS volume fraction (PVSvf) measurement to assess cross-sectional and longitudinal PVSvf differences between pre-symptomatic and symptomatic Dutch-type CAA (D-CAA) mutation carriers and similar-age controls. PVSvf was assessed with a Frangi-vesselness filter-based, segmentation tool developed in-house and was compared cross-sectionally in 70 participants (28 symptomatic D-CAA, 17 pre-symptomatic D-CAA, 10 controls > 50 years, 17 controls ≤ 50 years) and longitudinally in 40 participants (16 symptomatic D-CAA, 13 pre-symptomatic D-CAA, 11 controls combined from both age groups). We found a higher baseline PVSvf in symptomatic D-CAA compared to controls ≤ 50 years (p < 0. 0001, 95% CI [−0. 051, −0. 025]) and controls > 50 years (p < 0. 0001, 95% CI [-0. 042, −0. 016]), in pre-symptomatic D-CAA compared to controls ≤ 50 years (p = 0. 023, 95% CI [−0. 035, −0. 002]), and in controls > 50 years compared to controls ≤ 50 years (p < 0. 001, 95% CI [0. 004, 0. 014]). We found no group differences in PVSvf change over time. The introduction of this quantitative measure of PVS volume in D-CAA showed cross-sectional differences already in pre-symptomatic D-CAA, indicating increased PVSvf in the early stages of D-CAA. We did not observe longitudinal differences over a four-year follow-up when analyzed at group level.

YNICL Journal 2021 Journal Article

Cerebral amyloid angiopathy is associated with decreased functional brain connectivity

• Nadieh Drenth
• Jeroen van der Grond
• Serge A.R.B. Rombouts
• Mark A. van Buchem
• Gisela M. Terwindt
• Marieke J.H. Wermer
• Jasmeer P. Chhatwal
• M. Edip Gurol

Cerebral amyloid angiopathy (CAA) is a major cause of intracerebral hemorrhage and neurological decline in the elderly. CAA results in focal brain lesions, but the influence on global brain functioning needs further investigation. Here we study functional brain connectivity in patients with Dutch type hereditary CAA using resting state functional MRI. Twenty-four DNA-proven Dutch CAA mutation carriers (11 presymptomatic, 13 symptomatic) and 29 age-matched control subjects were included. Using a set of standardized networks covering the entire cortex, we assessed both within- and between-network functional connectivity. We investigated group differences using general linear models corrected for age, sex and gray matter volume. First, all mutation carriers were contrasted against control subjects and subsequently presymptomatic- and symptomatic mutation carriers against control subjects separately, to assess in which stage of the disease differences could be found. All mutation carriers grouped together showed decreased connectivity in the medial and lateral visual networks, default mode network, executive control and bilateral frontoparietal networks. Symptomatic carriers showed diminished connectivity in all but one network, and between the left and right frontoparietal networks. Presymptomatic carriers also showed diminished connectivity, but only in the frontoparietal left network. In conclusion, global brain functioning is diminished in patients with CAA, predominantly in symptomatic CAA and can therefore be considered to be a late consequence of the disease.

YNICL Journal 2020 Journal Article

Pre-trained MRI-based Alzheimer's disease classification models to classify memory clinic patients

• Frank de Vos
• Tijn M. Schouten
• Marisa Koini
• Mark J.R.J. Bouts
• Rogier A. Feis
• Anita Lechner
• Reinhold Schmidt
• Mark A. van Buchem

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 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.

YNIMG Journal 2019 Journal Article

Aberrant memory system connectivity and working memory performance in subjective cognitive decline

• Raymond P. Viviano
• Jessica M. Hayes
• Patrick J. Pruitt
• Zachary J. Fernandez
• Sanneke van Rooden
• Jeroen van der Grond
• Serge A.R.B. Rombouts
• Jessica S. Damoiseaux

Subjective cognitive decline, a perceived worsening of cognitive functioning without objective deficit on assessment, could indicate incipient dementia. However, the neural correlates of subjective cognitive decline as assessed by magnetic resonance imaging remain somewhat unclear. Here, we evaluated differences in functional connectivity across memory regions, and cognitive performance, between healthy older adults aged 50 to 85 with (n = 35, Age = 68. 5 ± 7. 7, 22 female), and without (n = 48, Age = 67. 0 ± 8. 8, 29 female) subjective cognitive decline. We also evaluated neurite density, fractional anisotropy, and mean diffusivity of the parahippocampal cingulum, cingulate gyrus cingulum, and uncinate fiber bundles in a subsample of participants (n = 37). Participants with subjective cognitive decline displayed lower average functional connectivity across regions of a putative posterior memory system, and lower retrosplenial-precuneus functional connectivity specifically, than those without memory complaints. Furthermore, participants with subjective cognitive decline performed poorer than controls 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 subjective cognitive decline that could indicate potential incipient dementia.

YNIMG Journal 2019 Journal Article

Cholinergic and serotonergic modulation of resting state functional brain connectivity in Alzheimer's disease

• Bernadet L. Klaassens
• Joop M.A. van Gerven
• Erica S. Klaassen
• Jeroen van der Grond
• Serge A.R.B. Rombouts

Disruption of cholinergic and serotonergic neurotransmitter systems is associated with cognitive, emotional and behavioural symptoms of Alzheimer's disease (AD). To investigate the responsiveness of these systems in AD we measured the effects of a single-dose of the selective serotonin reuptake inhibitor citalopram and acetylcholinesterase inhibitor galantamine in 12 patients with AD and 12 age-matched controls on functional brain connectivity with resting state functional magnetic resonance imaging. In this randomized, double blind, placebo-controlled crossover study, functional magnetic resonance images were repeatedly obtained before and after dosing, resulting in a dataset of 432 scans. Connectivity maps of ten functional networks were extracted using a dual regression method and drug vs. placebo effects were compared between groups with a multivariate analysis with signals coming from cerebrospinal fluid and white matter as covariates at the subject level, and baseline and heart rate measurements as confound regressors in the higher-level analysis (at p < 0. 05, corrected). A galantamine induced difference between groups was observed for the cerebellar network. Connectivity within the cerebellar network and between this network and the thalamus decreased after galantamine vs. placebo in AD patients, but not in controls. For citalopram, voxelwise network connectivity did not show significant group × treatment interaction effects. However, we found default mode network connectivity with the precuneus and posterior cingulate cortex to be increased in AD patients, which could not be detected within the control group. Further, in contrast to the AD patients, control subjects showed a consistent reduction in mean connectivity with all networks after administration of citalopram. Since AD has previously been characterized by reduced connectivity between the default mode network and the precuneus and posterior cingulate cortex, the effects of citalopram on the default mode network suggest a restoring potential of selective serotonin reuptake inhibitors in AD. The results of this study also confirm a change in cerebellar connections in AD, which is possibly related to cholinergic decline.

YNICL Journal 2019 Journal Article

Corrigendum to ‘Single-subject classification of presymptomatic frontotemporal dementia mutation carriers user multimodal MRI’ NeuroImage: Clinical 20 (2018) 188–196

• Rogier A. Feis
• Mark J.R.J. Bouts
• Jessica L. Panman
• Lize C. Jiskoot
• Elise G.P. Dopper
• Tijn M. Schouten
• Frank de Vos
• Jeroen van der Grond

YNICL Journal 2019 Journal Article

Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI

• Rogier A. Feis
• Mark J.R.J. Bouts
• Jessica L. Panman
• Lize C. Jiskoot
• Elise G.P. Dopper
• Tijn M. Schouten
• Frank de Vos
• Jeroen van der Grond

Background: Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. Methods: Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). Results: The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.582, p = 0.078). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.642 (p = 0.032). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.684, p = 0.004). Conclusions: FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.

YNIMG Journal 2018 Journal Article

A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease

• Frank de Vos
• Marisa Koini
• Tijn M. Schouten
• Stephan Seiler
• Jeroen van der Grond
• Anita Lechner
• Reinhold Schmidt
• Mark de Rooij

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 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.

YNIMG Journal 2018 Journal Article

Serotonergic and cholinergic modulation of functional brain connectivity: A comparison between young and older adults

• Bernadet L. Klaassens
• Joop M.A. van Gerven
• Erica S. Klaassen
• Jeroen van der Grond
• Serge A.R.B. Rombouts

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 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 × 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.

YNICL Journal 2018 Journal Article

Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI

• Rogier A. Feis
• Mark J.R.J. Bouts
• Jessica L. Panman
• Lize C. Jiskoot
• Elise G.P. Dopper
• Tijn M. Schouten
• Frank de Vos
• Jeroen van der Grond

BACKGROUND: Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. METHODS: Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). RESULTS: The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.582, p = 0.078). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.642 (p = 0.032). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.684, p = 0.004). CONCLUSIONS: FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.

YNIMG Journal 2017 Journal Article

Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging

• Tijn M. Schouten
• Marisa Koini
• Frank de Vos
• Stephan Seiler
• Mark de Rooij
• Anita Lechner
• Reinhold Schmidt
• Martijn van den Heuvel

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0. 888, and 0. 902. The ICA-clustered measures ranged between an AUC of 0. 893, and 0. 920. The AUC for the structural connectivity graph was 0. 900, while graph measures based upon this graph ranged between an AUC of 0. 531, and 0. 840. All measures combined with a sparse group lasso resulted in an AUC of 0. 896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI.

YNICL Journal 2017 Journal Article

Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson's disease

• Laura J. de Schipper
• Jeroen van der Grond
• Johan Marinus
• Johanna M.L. Henselmans
• Jacobus J. van Hilten

BACKGROUND: In Parkinson's disease (PD), the relation between cortical brain atrophy on MRI and clinical progression is not straightforward. Determination of changes in structural covariance networks - patterns of covariance in grey matter density - has shown to be a valuable technique to detect subtle grey matter variations. We evaluated how structural network integrity in PD is related to clinical data. METHODS: 3 Tesla MRI was performed in 159 PD patients. We used nine standardized structural covariance networks identified in 370 healthy subjects as a template in the analysis of the PD data. Clinical assessment comprised motor features (Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MDS-UPDRS motor scale) and predominantly non-dopaminergic features (SEverity of Non-dopaminergic Symptoms in Parkinson's Disease; SENS-PD scale: postural instability and gait difficulty, psychotic symptoms, excessive daytime sleepiness, autonomic dysfunction, cognitive impairment and depressive symptoms). Voxel-based analyses were performed within networks significantly associated with PD. RESULTS: = 0.074), respectively. Of the components of the SENS-PD score, cognitive impairment and excessive daytime sleepiness were associated with atrophy within both networks. CONCLUSIONS: We identified loss of integrity and atrophy in the anterior and posterior cingulate networks in PD patients. Abnormalities of both networks were associated with predominantly non-dopaminergic features, specifically cognition and excessive daytime sleepiness. Our findings suggest that (components of) the cingulate networks display a specific vulnerability to the pathobiology of PD and may operate as interfaces between networks involved in cognition and alertness.

YNICL Journal 2016 Journal Article

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease

• Tijn M. Schouten
• Marisa Koini
• Frank de Vos
• Stephan Seiler
• Jeroen van der Grond
• Anita Lechner
• Anne Hafkemeijer
• Christiane Möller

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

YNICL Journal 2016 Journal Article

Early grey matter changes in structural covariance networks in Huntington's disease

• Emma M. Coppen
• Jeroen van der Grond
• Anne Hafkemeijer
• Serge A.R.B. Rombouts
• Raymund A.C Roos

BACKGROUND: Progressive subcortical changes are known to occur in Huntington's disease (HD), a hereditary neurodegenerative disorder. Less is known about the occurrence and cohesion of whole brain grey matter changes in HD. OBJECTIVES: We aimed to detect network integrity changes in grey matter structural covariance networks and examined relationships with clinical assessments. METHODS: = 30), HD patients (n = 30) and controls (n = 30) was used to identify ten structural covariance networks based on a novel technique using the co-variation of grey matter with independent component analysis in FSL. Group differences were studied controlling for age and gender. To explore whether our approach is effective in examining grey matter changes, regional voxel-based analysis was additionally performed. RESULTS: = 0.032). Changes in network integrity were significantly associated with scores of motor and neuropsychological assessments. In premanifest HD, voxel-based analyses showed pronounced volume loss in the basal ganglia, but less prominent in cortical regions. CONCLUSION: Our results suggest that structural covariance might be a sensitive approach to reveal early grey matter changes, especially for premanifest HD.

YNICL Journal 2015 Journal Article

Joint assessment of white matter integrity, cortical and subcortical atrophy to distinguish AD from behavioral variant FTD: A two-center study

• Christiane Möller
• Anne Hafkemeijer
• Yolande A.L. Pijnenburg
• Serge A.R.B. Rombouts
• Jeroen van der Grond
• Elise Dopper
• John van Swieten
• Adriaan Versteeg

We investigated the ability of cortical and subcortical gray matter (GM) atrophy in combination with white matter (WM) integrity to distinguish behavioral variant frontotemporal dementia (bvFTD) from Alzheimer's disease (AD) and from controls using voxel-based morphometry, subcortical structure segmentation, and tract-based spatial statistics. To determine which combination of MR markers differentiated the three groups with the highest accuracy, we conducted discriminant function analyses. Adjusted for age, sex and center, both types of dementia had more GM atrophy, lower fractional anisotropy (FA) and higher mean (MD), axial (L1) and radial diffusivity (L23) values than controls. BvFTD patients had more GM atrophy in orbitofrontal and inferior frontal areas than AD patients. In addition, caudate nucleus and nucleus accumbens were smaller in bvFTD than in AD. FA values were lower; MD, L1 and L23 values were higher, especially in frontal areas of the brain for bvFTD compared to AD patients. The combination of cortical GM, hippocampal volume and WM integrity measurements, classified 97-100% of controls, 81-100% of AD and 67-75% of bvFTD patients correctly. Our results suggest that WM integrity measures add complementary information to measures of GM atrophy, thereby improving the classification between AD and bvFTD.

YNIMG Journal 2015 Journal Article

Single-dose serotonergic stimulation shows widespread effects on functional brain connectivity

• Bernadet L. Klaassens
• Helene C. van Gorsel
• Najmeh Khalili-Mahani
• Jeroen van der Grond
• Bradley T. Wyman
• Brandon Whitcher
• Serge A.R.B. Rombouts
• Joop M.A. van Gerven

The serotonergic system is widely distributed throughout the central nervous system. It is well known as a mood regulating system, although it also contributes to many other functions. With resting state functional magnetic resonance imaging (RS-fMRI) it is possible to investigate whole brain functional connectivity. We used this non-invasive neuroimaging technique to measure acute pharmacological effects of the selective serotonin reuptake inhibitor sertraline (75mg) in 12 healthy volunteers. In this randomized, double blind, placebo-controlled, crossover study, RS-fMRI scans were repeatedly acquired during both visits (at baseline and 3, 5, 7 and 9h after administering sertraline or placebo). Within-group comparisons of voxelwise functional connectivity with ten functional networks were examined (p <0. 005, corrected) using a mixed effects model with cerebrospinal fluid, white matter, motion parameters, heart rate and respiration as covariates. Sertraline induced widespread effects on functional connectivity with multiple networks; the default mode network, the executive control network, visual networks, the sensorimotor network and the auditory network. A common factor among these networks was the involvement of the precuneus and posterior cingulate cortex. Cognitive and subjective measures were taken as well, but yielded no significant treatment effects, emphasizing the sensitivity of RS-fMRI to pharmacological challenges. The results are consistent with the existence of an extensive serotonergic system relating to multiple brain functions with a possible key role for the precuneus and cingulate.

YNICL Journal 2013 Journal Article

Reduced functional brain connectivity prior to and after disease onset in Huntington's disease

• Eve M. Dumas
• Simon J.A. van den Bogaard
• Ellen P. Hart
• Roelof P. Soeter
• Mark A. van Buchem
• Jeroen van der Grond
• Serge A.R.B. Rombouts
• Raymund A.C. Roos

BACKGROUND: Huntington's disease (HD) is characterised by both regional and generalised neuronal cell loss in the brain. Investigating functional brain connectivity patterns in rest in HD has the potential to broaden the understanding of brain functionality in relation to disease progression. This study aims to establish whether brain connectivity during rest is different in premanifest and manifest HD as compared to controls. METHODS: At the Leiden University Medical Centre study site of the TRACK-HD study, 20 early HD patients (disease stages 1 and 2), 28 premanifest gene carriers and 28 healthy controls underwent 3 T MRI scanning. Standard and high-resolution T1-weighted images and a resting state fMRI scan were acquired. Using FSL, group differences in resting state connectivity were examined for eight networks of interest using a dual regression method. With a voxelwise correction for localised atrophy, group differences in functional connectivity were examined. RESULTS: Brain connectivity of the left middle frontal and pre-central gyrus, and right post central gyrus with the medial visual network was reduced in premanifest and manifest HD as compared to controls (0.05 > p > 0.0001). In manifest HD connectivity of numerous widespread brain regions with the default mode network and the executive control network were reduced (0.05 > p > 0.0001). DISCUSSION: Brain regions that show reduced intrinsic functional connectivity are present in premanifest gene carriers and to a much larger extent in manifest HD patients. These differences are present even when the potential influence of atrophy is taken into account. Resting state fMRI could potentially be used for early disease detection in the premanifest phase of HD and for monitoring of disease modifying compounds.

YNIMG Journal 2012 Journal Article

Elevated brain iron is independent from atrophy in Huntington's Disease

• Eve M. Dumas
• Maarten J. Versluis
• Simon J.A. van den Bogaard
• Matthias J.P. van Osch
• Ellen P. Hart
• Willeke M.C. van Roon-Mom
• Mark A. van Buchem
• Andrew G. Webb

Increased iron in subcortical structures in patients with Huntington's Disease (HD) has been suggested as a causal factor of neuronal degeneration. The present study examines iron accumulation, measured using magnetic resonance imaging (MRI), in premanifest gene carriers and in early HD patients as compared to healthy controls. In total 27 early HD patients, 22 premanifest gene carriers and 25 healthy controls, from the Leiden site of the TRACK-HD study, underwent 3T MRI including high resolution 3D T1- and T2-weighted and asymmetric spin echo (ASE) sequences. Magnetic Field Correlation (MFC) maps of iron levels were constructed to assess magnetic field inhomogeneities and compared between groups in the caudate nucleus, putamen, globus pallidus, hippocampus, amygdala, accumbens nucleus, and thalamus. Subsequently the relationship of MFC value to volumetric data and disease state was examined. Higher MFC values were found in the caudate nucleus (p <0. 05) and putamen (p <0. 005) of early HD compared to controls and premanifest gene carriers. No differences in MFC were found between premanifest gene carriers and controls. MFC in the caudate nucleus and putamen is a predictor of disease state in HD. No correlation was found between the MFC value and volume of these subcortical structures. We conclude that Huntington's disease patients in the early stages of the disease, but not premanifest gene carriers, have higher iron concentrations in the caudate nucleus and putamen. We have demonstrated that the iron content of these structures relates to disease state in gene carriers, independently of the measured volume of these structures.

YNIMG Journal 2006 Journal Article

In vivo flow territory mapping of major brain feeding arteries

• Peter Jan van Laar
• Jeroen Hendrikse
• Xavier Golay
• Hanzhang Lu
• Matthias J.P. van Osch
• Jeroen van der Grond

The ability to visualize the perfusion territories of major feeding arteries to the brain is important for many clinical applications. Since the work of Duret in 1874 on vascularization of the brain, many textbooks and atlases have shown schematic drawings of the supply areas of the major cerebral arteries. Recent postmortem studies demonstrated that the variability of the cerebral vascular territories is significantly greater than previously assumed. The aim of the present study was to investigate in vivo, the variability of flow territories of major brain feeding arteries. Flow territory mapping of the anterior (internal carotid arteries) and posterior (basilar artery) circulation was performed in 115 (58 ± 9 years of age) subjects with selective arterial spin labeling MRI. Flow territory maps for the entire population indicated significant variation in flow territories. However, when the subjects are further categorized into groups with a complete circle of Willis, with a missing A1 segment and with a unilateral or bilateral fetal-type posterior cerebral artery, the results showed considerably lower variation within groups. It is therefore concluded that, the variation observed from the entire population is mainly caused by anatomical variants of the circle of Willis. To relate focal brain lesions to underlying flow territories in individual cases, knowledge of the anatomy of the circle of Willis is essential.

YNIMG Journal 2005 Journal Article

Association between supine cerebral perfusion and symptomatic orthostatic hypotension

• Matthias J.P. van Osch
• Paul A.F. Jansen
• Ralf W. Vingerhoets
• Jeroen van der Grond

The goal of this study was to investigate whether the supine resting perfusion of brain tissue in symptomatic patients suffering from orthostatic hypotension (OH) is changed compared to control subjects and whether an association exists between the resting perfusion and the severity of OH. Ten symptomatic OH patients and 8 control subjects were included in this study. One patient was retrospectively excluded because he suffered from multiple system atrophy. Systolic and diastolic blood pressure changes were measured during a tilting bed procedure. Cerebral blood flow, cerebral blood volume and mean transit time were determined by bolus-tracking perfusion MRI and correlated with blood pressure changes. Cerebral blood volume was significantly increased in OH patients compared with control subjects for white matter (P = 0. 019) and the mean transit time was significantly increased for gray (P = 0. 010) and white matter (P = 0. 015). The cerebral blood flow of the gray (r = 0. 74, P = 0. 022) and white matter (r = 0. 75, P = 0. 020) was significantly, positively correlated with systolic blood pressure changes. The mean transit time in white matter was significantly, negatively correlated with systolic blood pressure changes (r = −0. 68, P = 0. 045). This study suggests that in symptomatic patients with OH the cerebral perfusion of the brain in the resting, supine position correlates with the severity of OH as measured by postural changes in blood pressure.

YNIMG Journal 2005 Journal Article

Functional MRI of human hypothalamic responses following glucose ingestion

• Paul A.M. Smeets
• Cees de Graaf
• Annette Stafleu
• Matthias J.P. van Osch
• Jeroen van der Grond

The hypothalamus is intimately involved in the regulation of food intake, integrating multiple neural and hormonal signals. Several hypothalamic nuclei contain glucose-sensitive neurons, which play a crucial role in energy homeostasis. Although a few functional magnetic resonance imaging (fMRI) studies have indicated that glucose consumption has some effect on the neuronal activity levels in the hypothalamus, this matter has not been investigated extensively yet. For instance, dose-dependency of the hypothalamic responses to glucose ingestion has not been addressed. We measured the effects of two different glucose loads on neuronal activity levels in the human hypothalamus using fMRI. After an overnight fast, the hypothalamus of 15 normal weight men was scanned continuously for 37 min. After 7 min, subjects ingested either water or a glucose solution containing 25 or 75 g of glucose. We observed a prolonged decrease of the fMRI signal in the hypothalamus, which started shortly after subjects began drinking the glucose solution and lasted for at least 30 min. Moreover, the observed response was dose-dependent: a larger glucose load resulted in a larger signal decrease. This effect was most pronounced in the upper anterior hypothalamus. In the upper posterior hypothalamus, the signal decrease was similar for both glucose loads. No effect was found in the lower hypothalamus. We suggest a possible relation between the observed hypothalamic response and changes in the blood insulin concentration.

YNIMG Journal 2005 Journal Article

Probabilistic segmentation of brain tissue in MR imaging

• Petronella Anbeek
• Koen L. Vincken
• Glenda S. van Bochove
• Matthias J.P. van Osch
• Jeroen van der Grond

A new method has been developed for probabilistic segmentation of five different types of brain structures: white matter, gray matter, cerebro-spinal fluid without ventricles, ventricles and white matter lesion in cranial MR imaging. The algorithm is based on information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It uses the K-Nearest Neighbor classification technique that builds a feature space from spatial information and voxel intensities. The technique generates for each tissue type an image representing the probability per voxel being part of it. By application of thresholds on these probability maps, binary segmentations can be obtained. A similarity index (SI) and a probabilistic SI (PSI) were calculated for quantitative evaluation of the results. The influence of each image type on the performance was investigated by alternately leaving out one of the five scan types. This procedure showed that the incorporation of the T1-w, PD or T2-w did not significantly improve the segmentation results. Further investigation indicated that the combination of IR and FLAIR was optimal for segmentation of the five brain tissue types. Evaluation with respect to the gold standard showed that the SI-values for all tissues exceeded 0. 8 and all PSI-values exceeded 0. 7, implying an excellent agreement.

YNIMG Journal 2004 Journal Article

Probabilistic segmentation of white matter lesions in MR imaging

• Petronella Anbeek
• Koen L. Vincken
• Matthias J.P. van Osch
• Robertus H.C. Bisschops
• Jeroen van der Grond

A new method has been developed for fully automated segmentation of white matter lesions (WMLs) in cranial MR imaging. The algorithm uses information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It is based on the K-Nearest Neighbor (KNN) classification technique that builds a feature space from voxel intensities and spatial information. The technique generates images representing the probability per voxel being part of a WML. By application of thresholds on these probability maps, binary segmentations can be obtained. ROC curves show that the segmentations achieve both high sensitivity and specificity. A similarity index (SI), overlap fraction (OF) and extra fraction (EF) are calculated for additional quantitative analysis of the result. The SI is also used for determination of the optimal probability threshold for generation of the binary segmentation. Using probabilistic equivalents of the SI, OF and EF, the probability maps can be evaluated directly, providing a powerful tool for comparison of different classification results. This method for automated WML segmentation reaches an accuracy that is comparable to methods for multiple sclerosis (MS) lesion segmentation and is suitable for detection of WMLs in large and longitudinal population studies.