Menno M. Schoonheim

YNICL Journal 2026 Journal Article

Longitudinal MRI-based changes in intracranial volume and skull thickness observed in both metachromatic leukodystrophy and multiple sclerosis

• Guus H.J. Vorst
• Nicole I. Wolf
• David R. van Nederpelt
• Frederik Barkhof
• Marjo S. van der Knaap
• Menno M. Schoonheim
• Eva M.M. Strijbis
• Petra J.W. Pouwels

Intracranial volume (ICV) is often used as normalization factor in volumetrics and considered to be stable in young adults. We noticed thick skulls on MRI scans of leukodystrophy patients, suggesting potentially changing skull morphology. In this study we aimed to quantify skull thickness and ICV in metachromatic leukodystrophy (MLD) patients and people with multiple sclerosis (pwMS). We retrospectively analyzed single-center cross-sectional and longitudinal MRI scans. Skull thickness and ICV were determined using automated segmentation techniques. Participants included MLD (n = 32, 11 male, scans = 136, median age first scan = 14.1 [IQR 7.9-25.7] years), MS (n = 232, 78 male, median age first scan = 47.3 [IQR 39.6-55.4] years, scans = 431), and controls (n = 139, 67 male, median age first scan = 30.7 [IQR 10.7-48.9] years, scans = 283). Both ICV and skull thickness showed natural growth in young controls. In young MLD participants, ICV decreased (-18.8 ± 22.4 mL/year, p < 0.001). Above age 20, ICV and skull thickness remained stable in controls. In comparison to controls, we observed ICV loss in MLD participants (-4.01 ± 8.29 mL/year, p < 0.001) and in pwMS (-2.99 ± 2.69 mL/year, p < 0.001), as well as skull thickening (MLD: 0.16 ± 0.14 mm/year, p < 0.001, pwMS: 0.04 ± 0.09 mm/year, p = 0.009). In adult patient groups, negative correlations were found between ICV and skull thickness (MLD: -19.24 mL/mm, p < 0.001, pwMS: -11.56 mL/mm, p < 0.001), but not in controls (p = 0.11). Despite limitations due to scanner variations, segmentation reliability and absence of validation against ground truth, these findings demonstrate a reduction in ICV in pathologies, already in young adulthood. Although the observed changes are small, they may lead to underestimations of atrophy when using ICV as a normalization factor.

YNICL Journal 2025 Journal Article

Regional cerebellar atrophy related to disability and cognitive progression in multiple sclerosis

• Myrte Strik
• Iris Dekker
• Aurélie Ruet
• Hanneke E. Hulst
• Mike P. Wattjes
• Frederik Barkhof
• Bernard M.J. Uitdehaag
• Joep Killestein

OBJECTIVE: The implications of cerebellar pathology on clinical disease progression in multiple sclerosis (MS) remain unclear. This study investigated regional cerebellar atrophy related to physical disability and cognitive impairment progression. METHODS: We included 331 MS patients and 95 controls (Amsterdam MS Cohort, 229 patients and 58 controls re-evaluated after 5 years). Assessments included baseline MRI, and disability and cognition at baseline and follow up. Cerebellar (sub)cortex was parcellated, volumetric data were determined and related to baseline disability and cognition. Longitudinal progression was explored only for regions with significant baseline correlations. RESULTS: At baseline, patients had mild disability (median EDSS 3.0) and 46% showing mild-to-severe cognitive impairment. At follow-up, 34.5% showed EDSS progression and 26.6% cognitive decline. All global and most regional volumes showed atrophy. Cross-sectionally, atrophy of several regions encompassing both anterior and posterior lobes correlated with both disability and cognition, while some correlated with EDSS only. Additionally, cerebellar nuclei only correlated with cognition. Cerebellar volumes were mainly related to information processing speed, working and verbal memory. Longitudinally, atrophy in the posterior lobe, lobule VI and VIIIb, and vermis VI, correlated with cognitive decline, while no variables correlated with disability progression. CONCLUSION: Regional cerebellar atrophy in both anterior and posterior lobes correlated with disability and cognitive impairment. Posterior regional atrophy was correlated with longitudinal cognitive decline, but none correlated with disability progression. Further research is required to elucidate these relationships.

YNICL Journal 2024 Journal Article

Scanner-specific optimisation of automated lesion segmentation in MS

• David R. van Nederpelt
• Giuseppe Pontillo
• Mar Barrantes-Cepas
• Iman Brouwer
• Eva M.M. Strijbis
• Menno M. Schoonheim
• Bastiaan Moraal
• Bas Jasperse

BACKGROUND & OBJECTIVE: Automatic lesion segmentation techniques on MRI scans of people with multiple sclerosis (pwMS) could support lesion detection and segmentation in trials and clinical practice. However, knowledge on their reliability across scanners is limited, hampering clinical implementation. The aim of this study was to investigate the within-scanner repeatability and between-scanner reproducibility of lesion segmentation tools in pwMS across three different scanners and examine their accuracy compared to manual segmentations with and without optimization. METHODS: 30 pwMS underwent a scan and rescan on three MRI scanners. GE Discovery MR750 (3.0 T), Siemens Sola (1.5 T) and Siemens Vida (3.0 T)). 3D-FLuid Attenuated Inversion Recovery (3D-FLAIR) and 3D T1-weighted scans were acquired on each scanner. Lesion segmentation involved preprocessing and automatic segmentation using the Lesion Segmentation Toolbox (LST) and nicMSlesions (nicMS) as well as manual segmentation. Both automated segmentation techniques were used with default settings, and with settings optimized to match manual segmentations for each scanner specifically and combined for the three scanners. LST settings were optimized by adjusting the threshold to improve the Dice similarity coefficient (DSC) for each scanner separately and a combined threshold for all scanners. For nicMS the last layers were retrained, once with the multi-scanner data to represent a combined optimization and once separately for each scanner for scanner specific optimization. Volumes and counts were extracted. DSC was calculated for accuracy, and reliability was assessed using intra-class correlation coefficients (ICC). Differences in DSC between software was tested with a repeated measures ANOVA and when appropriate post-hoc paired t-tests using Bonferroni correction. RESULTS: Scanner-specific optimization significantly improved DSC for LST compared to default and combined settings, except for the GE scanner. NicMS showed significantly higher DSC for both the scanner-specific and combined optimization than default. Within-scanner repeatability was excellent (ICC>0.9) for volume and counts. Between-scanner ICC for volume between Vida and Sola was higher (0.94-0.99) than between GE MR750 and Vida or Sola (0.18-0.93), with improved ICCs for nicMS scanner-specific (0.87-0.93) compared to others (0.18-0.79). This was not present for Sola vs. Vida where all ICCs were excellent (>0.94). CONCLUSION: Scanner-specific optimization strategies proved effective in mitigating inter-scanner variability, addressing the issue of insufficient reproducibility and accuracy found with default settings.

YNICL Journal 2024 Journal Article

Thalamic atrophy and dysconnectivity are associated with cognitive impairment in a multi-center, clinical routine, real-word study of people with relapsing-remitting multiple sclerosis

• Robert Zivadinov
• Niels Bergsland
• Dejan Jakimovski
• Bianca Weinstock-Guttman
• Lorena Lorefice
• Menno M. Schoonheim
• Sarah A. Morrow
• Mary Ann Picone

BACKGROUND: Prior research has established a link between thalamic pathology and cognitive impairment (CI) in people with multiple sclerosis (pwMS). However, the translation of these findings to pwMS in everyday clinical settings has been insufficient. OBJECTIVE: To assess which global and/or thalamic imaging biomarkers can be used to identify pwMS at risk for CI and cognitive worsening (CW) in a real-world setting. METHODS: This was an international, multi-center (11 centers), longitudinal, retrospective, real-word study of people with relapsing-remitting MS (pwRRMS). Brain MRI exams acquired at baseline and follow-up were collected. Cognitive status was evaluated using the Symbol Digit Modalities Test (SDMT). Thalamic volume (TV) measurement was performed on T2-FLAIR, as well as on T1-WI, when available. Thalamic dysconnectivity, T2-lesion volume (T2-LV), and volumes of gray matter (GM), whole brain (WB) and lateral ventricles (LVV) were also assessed. RESULTS: 332 pwMS were followed for an average of 2.8 years. At baseline, T2-LV, LVV, TV and thalamic dysconnectivity on T2-FLAIR (p < 0.016), and WB, GM and TV volumes on T1-WI (p < 0.039) were significantly worse in 90 (27.1 %) CI vs. 242 (62.9 %) non-CI pwRRMS. Greater SDMT decline over the follow-up was associated with lower baseline TV on T2-FLAIR (standardized β = 0.203, p = 0.002) and greater thalamic dysconnectivity (standardized β = -0.14, p = 0.028) in a linear regression model. CONCLUSIONS: PwRRMS with thalamic atrophy and worse thalamic dysconnectivity present more frequently with CI and experience greater CW over mid-term follow-up in a real-world setting.

YNICL Journal 2022 Journal Article

Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective

• Maria A. Rocca
• Menno M. Schoonheim
• Paola Valsasina
• Jeroen J.G. Geurts
• Massimo Filippi

Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research.

YNICL Journal 2022 Journal Article

The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics

• Menno M. Schoonheim
• Tommy A.A. Broeders
• Jeroen J.G. Geurts

Multiple sclerosis is a neuroinflammatory and neurodegenerative disorder of the central nervous system that can be considered a network disorder. In MS, lesional pathology continuously disconnects structural pathways in the brain, forming a disconnection syndrome. Complex functional network changes then occur that are poorly understood but closely follow clinical status. Studying these structural and functional network changes has been and remains crucial to further decipher complex symptoms like cognitive impairment and physical disability. Recent insights especially implicate the importance of monitoring network hubs in MS, like the thalamus and default-mode network which seem especially hit hard. Such network insights in MS have led to the hypothesis that as the network continues to become disconnected and dysfunctional, exceeding a certain threshold of network efficiency loss leads to a "network collapse". After this collapse, crucial network hubs become rigid and overloaded, and at the same time a faster neurodegeneration and accelerated clinical (and cognitive) progression can be seen. As network neuroscience has evolved, the MS field can now move towards a clearer classification of the network collapse itself and specific milestone events leading up to it. Such an updated network-focused conceptual framework of MS could directly impact clinical decision making as well as the design of network-tailored rehabilitation strategies. This review therefore provides an overview of recent network concepts that have enhanced our understanding of clinical progression in MS, especially focusing on cognition, as well as new concepts that will likely move the field forward in the near future.

YNICL Journal 2021 Journal Article

Development and evaluation of a manual segmentation protocol for deep grey matter in multiple sclerosis: Towards accelerated semi-automated references

• Alexandra de Sitter
• Jessica Burggraaff
• Fabian Bartel
• Miklos Palotai
• Yaou Liu
• Jorge Simoes
• Serena Ruggieri
• Katharina Schregel

BACKGROUND: Deep grey matter (dGM) structures, particularly the thalamus, are clinically relevant in multiple sclerosis (MS). However, segmentation of dGM in MS is challenging; labeled MS-specific reference sets are needed for objective evaluation and training of new methods. OBJECTIVES: This study aimed to (i) create a standardized protocol for manual delineations of dGM; (ii) evaluate the reliability of the protocol with multiple raters; and (iii) evaluate the accuracy of a fast-semi-automated segmentation approach (FASTSURF). METHODS: A standardized manual segmentation protocol for caudate nucleus, putamen, and thalamus was created, and applied by three raters on multi-center 3D T1-weighted MRI scans of 23 MS patients and 12 controls. Intra- and inter-rater agreement was assessed through intra-class correlation coefficient (ICC); spatial overlap through Jaccard Index (JI) and generalized conformity index (CIgen). From sparse delineations, FASTSURF reconstructed full segmentations; accuracy was assessed both volumetrically and spatially. RESULTS: All structures showed excellent agreement on expert manual outlines: intra-rater JI > 0.83; inter-rater ICC ≥ 0.76 and CIgen ≥ 0.74. FASTSURF reproduced manual references excellently, with ICC ≥ 0.97 and JI ≥ 0.92. CONCLUSIONS: The manual dGM segmentation protocol showed excellent reproducibility within and between raters. Moreover, combined with FASTSURF a reliable reference set of dGM segmentations can be produced with lower workload.

YNICL Journal 2021 Journal Article

Dynamic functional connectivity as a neural correlate of fatigue in multiple sclerosis

• Floris B. Tijhuis
• Tommy A.A. Broeders
• Fernando A.N. Santos
• Menno M. Schoonheim
• Joep Killestein
• Cyra E. Leurs
• Quinten van Geest
• Martijn D. Steenwijk

BACKGROUND: More than 80% of multiple sclerosis (MS) patients experience symptoms of fatigue. MS-related fatigue is only partly explained by structural (lesions and atrophy) and functional (brain activation and conventional static functional connectivity) brain properties. OBJECTIVES: To investigate the relationship of dynamic functional connectivity (dFC) with fatigue in MS patients and to compare dFC with commonly used clinical and MRI parameters. METHODS: In 35 relapsing-remitting MS patients (age: 42.83 years, female/male: 20/15, disease duration: 11 years) and 19 healthy controls (HCs) (age: 41.38 years, female/male: 11/8), fatigue was measured using the CIS-20r questionnaire at baseline and at 6-month follow-up. All subjects underwent structural and resting-state functional MRI at baseline. Global static functional connectivity (sFC) and dynamic functional connectivity (dFC) were calculated. dFC was assessed using a sliding-window approach by calculating the summed difference (diff) and coefficient of variation (cv) across windows. Moreover, regional connectivity between regions previously associated with fatigue in MS was estimated (i.e. basal ganglia and regions of the Default Mode Network (DMN): medial prefrontal, posterior cingulate and precuneal cortices). Hierarchical regression analyses were performed with forward selection to identify the most important correlates of fatigue at baseline. Results were not corrected for multiple testing due to the exploratory nature of the study. RESULTS: Patients were more fatigued than HCs at baseline (p = 0.001) and follow-up (p = 0.002) and fatigue in patients was stable over time (p = 0.213). Patients had significantly higher baseline global dFC than HCs, but no difference in basal ganglia-DMN dFC. In the regression model for baseline fatigue in patients, basal ganglia-DMN dFC-cv (standardized β = -0.353) explained 12.5% additional variance on top of EDSS (p = 0.032). Post-hoc analysis revealed higher basal ganglia-DMN dFC-cv in non-fatigued patients compared to healthy controls (p = 0.013), whereas fatigued patients and healthy controls showed similar basal ganglia-DMN dFC. CONCLUSIONS: Less dynamic connectivity between the basal ganglia and the cortex is associated with greater fatigue in MS patients, independent of disability status. Within patients, lower dynamics of these connections could relate to lower efficiency and increased fatigue. Increased dynamics in non-fatigued patients compared to healthy controls might represent a network organization that protects against fatigue or signal early network dysfunction.

YNICL Journal 2021 Journal Article

The sequence of structural, functional and cognitive changes in multiple sclerosis

• Iris Dekker
• Menno M. Schoonheim
• Vikram Venkatraghavan
• Anand J.C. Eijlers
• Iman Brouwer
• Esther E. Bron
• Stefan Klein
• Mike P. Wattjes

BACKGROUND: As disease progression remains poorly understood in multiple sclerosis (MS), we aim to investigate the sequence in which different disease milestones occur using a novel data-driven approach. METHODS: We analysed a cohort of 295 relapse-onset MS patients and 96 healthy controls, and considered 28 features, capturing information on T2-lesion load, regional brain and spinal cord volumes, resting-state functional centrality ("hubness"), microstructural tissue integrity of major white matter (WM) tracts and performance on multiple cognitive tests. We used a discriminative event-based model to estimate the sequence of biomarker abnormality in MS progression in general, as well as specific models for worsening physical disability and cognitive impairment. RESULTS: We demonstrated that grey matter (GM) atrophy of the cerebellum, thalamus, and changes in corticospinal tracts are early events in MS pathology, whereas other WM tracts as well as the cognitive domains of working memory, attention, and executive function are consistently late events. The models for disability and cognition show early functional changes of the default-mode network and earlier changes in spinal cord volume compared to the general MS population. Overall, GM atrophy seems crucial due to its early involvement in the disease course, whereas WM tract integrity appears to be affected relatively late despite the early onset of WM lesions. CONCLUSION: Data-driven modelling revealed the relative occurrence of both imaging and non-imaging events as MS progresses, providing insights into disease propagation mechanisms, and allowing fine-grained staging of patients for monitoring purposes.

YNICL Journal 2020 Journal Article

Anterior insular network disconnection and cognitive impairment in Parkinson’s disease

• Yasmine Y. Fathy
• Dagmar H. Hepp
• Frank J. de Jong
• Jeroen J.G. Geurts
• Elisabeth M.J. Foncke
• Henk W. Berendse
• Wilma D.J. van de Berg
• Menno M. Schoonheim

BACKGROUND: The insula is a central brain hub involved in cognition and affected in Parkinson's disease (PD). The aim of this study was to assess functional connectivity (FC) and betweenness centrality (BC) of insular sub-regions and their relationship with cognitive impairment in PD. METHODS: Whole-brain 3D-T1, resting-state functional MRI and a battery of cognitive tests (CAMCOG) were included for 53 PD patients and 15 controls. The insular cortex was segmented into ventral (vAI) and dorsal (dAI) anterior and posterior sub-regions. Connectivity between insular sub-regions and resting-state networks was assessed and related to cognition; BC was used to further explore nodes associated with cognition. RESULTS: = -0.44, p = 0.001 respectively). CONCLUSIONS: These results highlight the relevance of the insula in cognitive dysfunction in PD. Disconnection of the dAI with ACC was related to altered centrality in the DMN and FPN only in patients. Disturbance in this network triad appears to be particularly relevant for cognitive impairment in PD.

YNICL Journal 2020 Journal Article

Functional connectivity between resting-state networks reflects decline in executive function in Parkinson’s disease: A longitudinal fMRI study

• Lennard I. Boon
• Dagmar H. Hepp
• Linda Douw
• Noëlle van Geenen
• Tommy A.A. Broeders
• Jeroen J.G. Geurts
• Henk W. Berendse
• Menno M. Schoonheim

Deficits in cognitive functioning are a common yet poorly understood symptom in Parkinson's disease (PD). Recent studies have highlighted the importance of (dynamic) interactions between resting-state networks for cognition, which remains understudied in PD. We investigated how altered (dynamic) functional interactions between brain networks relate to cognitive dysfunction in PD patients. In this fMRI study, 50 PD patients (mean age 65.5 years ± 6.27) on dopaminergic medication were studied cross-sectionally, and of this cohort 31 PD patients were studied longitudinally. MRI imaging and neuropsychological testing was performed at two time points, with a follow-up duration of approximately three years. Functional connectivity within and between seven resting-state networks was calculated (both statically and dynamically) and correlated with four neuropsychological test scores; a combined score of (four) executive tasks, a motor perseveration, memory, and category fluency task. Cognitive dysfunction was determined based on a longitudinal sample of age-matched healthy controls (n = 13). PD patients showed dysfunction on six out of seven cognitive tasks when compared to healthy controls. Severity of executive dysfunction was correlated with higher static and lower dynamic functional connectivity between deep gray matter regions and the frontoparietal network (DGM-FPN). Over time, declining executive function was related to increasing static DGM-FPN connectivity, together with changes of connectivity involving the dorsal attention network (amongst others with the ventral attention network). Static functional connectivity between the ventral and dorsal attention network correlated with motor perseveration. Our findings demonstrate that in PD patients, dysfunctional communication between (i) subcortical, fronto-parietal and attention networks mostly underlies worsening of executive functioning, (ii) attention networks are involved in motor perseveration.

YNIMG Journal 2020 Journal Article

Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches

• Prejaas Tewarie
• Bastian Prasse
• Jil M. Meier
• Fernando A.N. Santos
• Linda Douw
• Menno M. Schoonheim
• Cornelis J. Stam
• Piet Van Mieghem

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i. e. , walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. ​Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks.

YNICL Journal 2017 Journal Article

Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy

• Martijn D. Steenwijk
• Houshang Amiri
• Menno M. Schoonheim
• Alexandra de Sitter
• Frederik Barkhof
• Petra J.W. Pouwels
• Hugo Vrenken

INTRODUCTION: MRI data. METHODS: data from 100 MS patients and 20 matched healthy controls. In fifty of the MS patients a second time point was available. In this subgroup, we additionally analyzed the whole-brain and GM volume change using the longitudinal pipeline of MSmetrix and compared the results with those of FreeSurfer (longitudinal pipeline) and SIENA. RESULTS: . Among the cross-sectional methods, Deming regression analyses revealed proportional errors particularly in MSmetrix and SPM. The mean difference percentage brain volume change (PBVC) was lowest between longitudinal MSmetrix and SIENA (+ 0.16 ± 0.91%). A strong proportional error was present between longitudinal percentage gray matter volume change (PGVC) measures of MSmetrix and FreeSurfer (slope = 2.48). All longitudinal methods were sensitive to the MRI hardware upgrade that occurred during the time of the study. CONCLUSION: MSmetrix, FreeSurfer, FSL and SPM show differences in atrophy measurements, even at the whole-brain level, that are large compared to typical atrophy rates observed in MS. Especially striking are the proportional errors between methods. Cross-sectional MSmetrix behaved similarly to SPM, both in terms of mean volume difference as well as proportional error. Longitudinal MSmetrix behaved most similar to SIENA. Our results indicate that brain volume measurement and normalization from T1-weighted images remains an unsolved problem that requires much more attention.

YNIMG Journal 2013 Journal Article

Corrigendum to “Functional segmentation of the hippocampus in the healthy human brain and in Alzheimer's disease” [Neuroimage 66 (2013) 28–35]

• Mojtaba Zarei
• Christian F. Beckmann
• Maja A.A. Binnewijzend
• Menno M. Schoonheim
• Mohammad Ali Oghabian
• Ernesto J. Sanz-Arigita
• Philip Scheltens
• Paul M. Matthews

YNIMG Journal 2013 Journal Article

Functional segmentation of the hippocampus in the healthy human brain and in Alzheimer's disease

• Mojtaba Zarei
• Christian F. Beckmann
• Maja A.A. Binnewijzend
• Menno M. Schoonheim
• Mohammad Ali Oghabian
• Ernesto J. Sanz-Arigita
• Philip Scheltens
• Paul M. Matthews

In this study we segment the hippocampus according to functional connectivity assessed from resting state functional magnetic resonance images in healthy subjects and in patients with Alzheimer's disease (AD). We recorded the resting FMRI signal from 16 patients and 22 controls. We used seed-based functional correlation analyses to calculate partial correlations of all voxels in the hippocampus relative to characteristic regional signal changes in the thalamus, the prefrontal cortex (PFC) and the posterior cingulate cortex (PCC), while controlling for ventricular CSF and white matter signals. Group comparisons were carried out controlling for age, gender, hippocampal volume and brain volume. The strength of functional connectivity in each region also was correlated with neuropsychological measures. We found that the hippocampus can be segmented into three distinct functional subregions (head, body, and tail), according to the relative connectivity with PFC, PCC and thalamus, respectively. The AD group showed stronger hippocampus–PFC and weaker hippocampus–PCC functional connectivity, the magnitudes of which correlated with MMSE in both cases. The results are consistent with an adaptive role of the PFC in the context of progression of dysfunction in PCC during earlier stages of AD. Extension of our approach could integrate regional volume measures for the hippocampus with their functional connectivity patterns in ways that should increase sensitivity for assessment of AD onset and progression.