Brain Connectivity Analysis of Parkinson's Disease and “Scans Without Evidence for Dopaminergic Deficit" Patients
Tiago Constantino1,2,3, André Santos Ribeiro4, Ricardo Maximiano3, John Mcgonigle4, David Nutt4, and Hugo Alexandre Ferreira3

1Lisbon School of Health Technology-ESTeSL, Lisbon, Portugal, 2Spitalzentrum Biel, Biel, Switzerland, 3Institute of Biophysics and Biomedical Engineering, Faculty of Sciences of the University of Lisbon, Lisbon, Portugal, 4Centre for Neuropsychopharmacology, Imperial College London, London, United Kingdom

Synopsis

In this work we propose a comparison study between “Scans Without Evidence for Dopaminergic Deficit" (SWEDD) and Parkinson’s Disease (PD) patients against healthy subjects using the MIBCA toolbox. Here, we studied the difference in imaging and connectivity metrics obtained from anatomical (T1-weighted) and structural (Diffusion Tensor Imaging) data between the three groups. Results showed increased mean diffusivity in the frontal pole, rostral middle frontal gyrus and superior frontal gyrus between SWEDD and PD patients, which can be related with the dopaminergic mesocortical pathway degeneration in PD. These preliminary results help clarify the differences between SWEDD and PD patients.

Purpose

Patients with “Scans Without Evidence for Dopaminergic Deficit" (SWEDD) are those that do not show dopamine deficiency nor any imaging irregularity that would diagnose them as actually having Parkinson’s Disease (PD). Presently, there is an on-going controversy about SWEDD being a PD look-alike disease or a benign subtype of PD.1 In order to shed some light on the topic, in this study we investigated the changes in structural connectivity (SC) of patients diagnosed with PD or SWEDD, which to our best knowledge has not been done yet.

Methods

We studied 89 subjects (30 healthy subjects, 29 patients with SWEDD and 30 PD patients) obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (https://ida.loni.usc.edu/). The MRI sequence protocol included T1-weighted (T1-w) and Diffusion Tensor Imaging (DTI) data acquisition using a 3T scanner (TrioTim, SIEMENS, Erlangen, Germany) and an 8-channel head coil. T1-w sequence (3D MP-RAGE) parameters included: acquisition in the sagittal plane; 240 Slices; TR/TE/TI=2300/2.98/900 ms; Flip angle=90 degrees; Matrix=240x256; Voxel size=1x1x1.2 mm3. The DTI sequence (2D Echo Planar Imaging) parameters included acquisition in the coronal plane; 116 Slices; TR/TE=890/88 ms; Flip angle=90 degrees; 64 gradients directions; b=0,1000 s/mm2; Matrix =176 x 176; Voxel size=2x2x2 mm3. All data were automatically processed and analysed using the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox2. Imaging metrics such as cortical thickness (CThk), cortical area (CAr), and volume were obtained from T1-w data for all 96 regions-of-interest (ROIs), parcellated using Freesurfer atlases, as well as Mean Diffusibility (MD), Fractional Anisotropy (FA) and number of fibers between ROIs (FiberConn) from DTI-data. Additionally, SC matrices were computed from FiberConn data, as well as derived connectivity metrics such as node degree (Deg), clustering coefficient (ClusC), edge betweenness centrality (EdgeBetw) and distance.3 Demographics data such as gender, age, years of education and Unified Parkinson Disease Rating Scale (UPDRS) scores were compared between groups using parametric or non-parametric tests, as appropriate, in IBM SPSS. Differences between groups were also evaluated regarding imaging and connectivity metrics, and FiberConn, using MIBCA’s statistical functions, and differences were visualised in connectograms. A significance of p<0.05 was used for all the tests.

Results and Discussion

Regarding demographics data the groups were gender, age, and years of education matched, and showed statistical differences (Mann-Whitney U-test) in UPDRS between healthy subjects (Control) group and PD (p=0.000) and SWEDD (p=0.000) groups. This implies that the differences found in subsequent analysis are most probably related to pathology rather than other confounding variables. Figure 1 and 2 show the statistical differences between the various groups: Control vs PD, Control vs SWEDD and PD vs SWEDD. Control vs PD. Several differences were observed regarding various imaging and connectivity metrics, particularly in the basal ganglia of both hemispheres. In the right hemisphere, the Nucleus Accumbens showed decreased MD and increased FA, FiberConn and EdgeBetw. These changes were similarly observed for the rostral middle frontal Gyrus (rMFG) of both hemispheres. These findings may be related with known degeneration of dopaminergic pathways, including the nigrostriatal, mesocortical and mesolimbic pathways, in PD.4,5 Control vs SWEDD. The splenium of the corpus callosum showed decreased MD and Deg and increased FA and FiberConn. Regions of the frontal and parietal lobes showed many connectivity metrics changes, particularly the superior marginal gyrus and superior parietal gyrus of both hemispheres and the pars orbitallis of the left hemisphere. These results show changes in distinct regions than the ones observed for PD, supporting the idea that SWEDD to be a distinct nosological entity or entities.1,6 PD vs SWEDD. In the frontal lobe of both hemispheres, various DTI-based imaging and connectivity metrics changes were observed, particularly in the frontal pole, rMFG and superior frontal gyrus, regions of the mesocortical pathway. In the limbic lobe changes were observed in the isthmus of the cingulate gyrus and parahippocampal gyrus of both hemispheres, which may be related to memory impairment.7 In the insular cortex of both hemispheres a decreased FA decrease and increased MD and ClusC were observed. These findings could be related to cognitive decline, behavioural abnormalities and somatosensory disturbances.8

Conclusions

All results observed in this study are in agreement with the literature regarding observed changes in regions related to the nigrostriatal, mesocortical and mesolimbic pathways. These findings suggest that the study of SC an important method to distinguish SWEDD and PD.

Acknowledgements

Data used in the preparation of this abstract were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI—a public-private partnership—is funded by the Michael J. Fox Foundation for Parkinson’s Research and its funding partners, which include Abbvie, Avid, Biogen, Bristol-Myers Squibb, Covance, GE Healthcare, Genentech, GalaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Roche, Servier and UCB—for a current list see http://www.ppmi-info.org/about-ppmi/who-we-are/study-sponsors/.

Research supported by the Edmond J. Safra Philanthropic Foundation, and Fundação para a Ciência e Tecnologia (FCT) and Ministério da Ciência e Educação (MCE) Portugal (PIDDAC) under grants UID/BIO/00645/2013 and PTDC/SAU-ENB/120718/2010.

References

1. Erro R, Schneider SA, Quinn NP, et al. What do patients with scans without evidence of dopaminergic deficit (SWEDD) have? New evidence and continuing controversies. Journal of Neurology, Neurosurgery & Psychiatry. 2015. doi:10.1136/jnnp-2014-310256.

2. Santos-Ribeiro A, Lacerda LM, Ferreira HA. Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox. PeerJ. 2015;3:e1078.

3. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage. 2010;52(3):1059–1069.

4. Lewis SJG, Dove A, Robbins TW, et al. Cognitive impairments in early Parkinson's disease are accompanied by reductions in activity in frontostriatal neural circuitry. The Journal of Neuroscience. 2003;23(15):6351-6356.

5. Carriere N, Besson P, Dujardin K, et al. Apathy in Parkinson's disease is associated with nucleus accumbens atrophy: A magnetic resonance imaging shape analysis. Movement Disorders. 2014;29(7): 897-903.

6. Marek K, Seibyl J, Eberly S, et al. Longitudinal follow-up of SWEDD subjects in the PRECEPT Study. Neurology. 2014;82(20):1791-1797.

7. Carlesimo GA, Piras F, Assogna F, et al. Hippocampal abnormalities and memory deficits in Parkinson disease - A multimodal imaging study. Neurology. 2012;78(24):1939-1945.

8. Christopher L, Koshimori Y, Lang AE, et al. Uncovering the role of the insula in non-motor symptoms of Parkinson’s disease. Brain. 2014;137(8):2143-2154.

Figures

The table (left-side) demonstrates main significant regional increases and decreases in T1-weighted metrics between the second and first groups. Regions with 2 and 3 changes are exhibited in bold and italic, correspondingly. The table (right-side) shows the acronym and designation words. No CAr decreases were observed.

The table (left-side) demonstrates main significant regional increases and decreases in DTI and connectivity metrics between the second and first groups. Regions with 2 and 3 changes are exhibited in bold and italic, correspondingly. The table (right-side) shows the acronym and designation words. No CAr decreases were observed.

Connectogram for Control-PD.

Connectogram for Control-SWEDD.

Connectogram for PD-SWEDD.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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