An imaging and blood biomarkers open dataset on alzheimer's disease vs. late onset bipolar disorder

1. Introduction

Bipolar disorder (BD) is a chronic mood disorder alternating maniac/depressive with euthymic episodes. Its onset is conditioned by the environment and genetic inheritance ( Bauer M. et al., 2014 ; Martinez-Cengotitabengoa et al., 2014 ; Bauer et al., 2015a , b ), often during youth producing cognitive, affective, and functional impairment ( Forcada et al., 2014 ). Late onset BD (LOBD) corresponds to ages above 50 years ( Depp and Jeste, 2004 ; Zanetti et al., 2007 ; Prabhakar and Balon, 2010 ; Besga et al., 2011 ; Carlino et al., 2013 ; Chou et al., 2015 ). At this period of life it can be difficult to differentiate LOBD from Alzheimer’s disease (AD) ( Zahodne et al., 2015 ). Recent studies have found shared biomarkers between LOBD and AD patients ( Berridge, 2013 ). Similar roles of inflammation and oxidative stress biomarkers have been found in AD ( Akiyama et al., 2000 ; Kamer et al., 2008 ; Sardi et al., 2011 ), LOBD ( Goldstein et al., 2009 ; Konradi et al., 2012 ; Leboyer et al., 2012 ; Lee et al., 2013 ; Bauer I et al., 2014 ; Hope et al., 2015 ), depression, and mania ( Brydon et al., 2009 ; Dickerson et al., 2013 ; Castanon et al., 2014 ; Singhal et al., 2014 ). Further details of shared LOBD and AD traits are given in Besga et al. (2015b). Specific common psychiatric symptoms are: agitation, euphoria, disinhibition, over-activity without agitation, aggression, affective liability, dysphoria, apathy, impaired self-regulation, and psychosis ( Albert and Blacker, 2006 ; Zahodne et al., 2015 ). The demographic, neuropsychological, clinical, imaging, and blood plasma analytics data used in our study, as well as the rules for eligibility and discarding of patients, appear in Graña et al. (2011) and Besga et al. (2012 , 2015a , 2016 , 2017), hence reproducing them here would be self-plagiarism according to journal rules. The content of this paper is as follows: first we refer the contents of the dataset and its location for downloading, then we summarize previously published results. We include the ethics statement and the trial registration reference.

2. Contents of the Dataset

The dataset has been published in the Zenodo public repository ( Besga et al., 2020 ). Its contents are as follows:

• The clinical data includes the following information

– Demographics data, such as age, sex, civil state, environment conditions, and others

– Results of neuropsychological examination: we have carried out tests for executive function, learning and memory, and attention. Details are given in Besga et al. (2015a).

– Clinical observation: we carried out the Neuropsychiatric Inventory and the functional assessment staging (FAST). Details are given in Besga et al. (2015a).

– Blood plasma biomarkers: Neurotrophins, inflammation biomarkers, and oxidative stress biomarkers. Details are given in Besga et al. (2017).

• Magnetic Resonance Imaging (MRI) data obtained on a 1. 5 Tesla scanner, that includes the following (details of data capture and preprocessing are given in Graña et al., 2011 ; Besga et al., 2012 , 2016 ):

– Diffusion weighted imaging (DWI) with high b and 30 gradient directions. The original data is provided in the dataset.

– Fractional Anisotropy (FA) volumes computed from the DWI data after noise correction, registration, and diffusion tensor (DTI) computation. The original FA data and the FA volumes co-registered to MNI template using the T1-weighted data non-linear registration parameters are provided.

– T1-weighted anatomical volumes at 1 mm resolution. Both original and non-linearly registered to MNI template data are provided in the published dataset.

• Results: we provide the following results that can be used as reference for checking the integrity of the data

– Voxel based morphometry (VBM) computed using Statistical Parametric Mapping (SPM) software. ¹

– Tract based spatial statistics (TBSS) intermediate and final results, as well as the correlations with blood sample biomarkers computed using the FSL software ( Smith et al., 2004 ) ² as published in Besga et al. (2017).

3. Published Results

Previously reported results of this study are as follows:

1. Machine learning based computer aided diagnosis (CAD) ( Sigut et al., 2007 ; Salas-Gonzalez et al., 2009 ; Ramirez et al., 2010 ; Savio et al., 2011 ; Westman et al., 2011 ; Termenon et al., 2013 ) achieved high accuracy discrimination between AD and LOBD populations using whole brain FA ( Graña et al., 2011 ; Besga et al., 2012 ). We discuss there that the good classification performance was not enough due to poor brain localization results causes by the feature extraction process.

2. Again, machine learning techniques applied on the clinical, neuropsychological test, and blood plasma biomarkers give good LOBD vs. AD classification performance ( Besga et al., 2015a ). Clinical variables reported the highest discriminant power, while blood plasma biomarkers reported low discriminant power. Combinations of both, improved the classification results, showing some indirect effects of blood plasma biomarkers.

3. Using eigenanatomy tools ( Avants et al., 2012 , 2014 ) we found an optimal decomposition of the FA volumes that show maximal correlation with plasma biomarkers ( Besga et al., 2016 ). This decomposition provides the features for classifier model building to discriminate AD vs. LOBD classification, providing anatomical localization of the effects corresponding to the classification features, which are consistent with differential diagnostics.

4. The Tract-Based Spatial Statistics (TBSS) ( Smith et al., 2006 ; Bach et al., 2014 ) allows us to identify strongly significant clusters in behavioral impairment relevant tracts. These clusters show specific correlation with neurotrophins biomarkers in an AD population but none with a LOBD population. We also found a strong positive correlation of inflammation biomarkers with the LOBD population ( Besga et al., 2017 ).

4. Limitations

The major limitation for the data is the small sample size. Recruiting was a long process that took several years due to the advanced age of the participants. Another limitation to keep in mind is that the plasma biomarkers were not extracted specifically from the central nervous system (CNS) tissues, so they are biomarkers of the general state of the body not of specific locations in the CNS.

5. Conclusions

We introduce a public dataset that can be exploited for the identification of biomarkers that allow enhanced differential diagnosis of AD vs. LOBD. We summarize our findings, already published in the literature, in order to encourage innovative computational approaches to be tested on this dataset.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: Zenodo, doi: 10. 5281/zenodo. 3935636 .

Ethics Statement

The ethics committee of the Alava University Hospital, Spain, approved this study. All patients provided written consent to participate in the study, which was conducted according to the provisions of the Helsinki declaration. The study has been registered as an observation trial ³ in the ISRCTN registry.

Author Contributions

AB and AG-P have made a substantial contributions to the conception and design of the work. DC and MG contributed to the acquisition, analysis, and interpretation of data for the work. All authors contributed to drafting the work and revising it critically for important intellectual content, provided the final approval of the version to be published, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding

This research has been partially funded by the Basque Government grant IT1284-19 for the Computational Intelligence research group, and supported by FEDER funds through MINECO project TIN2017-85827-P. DC has been supported by Basque Government post-doctoral grant No Ref.: POS-2014-1-2, MOD: POSDOC. This work was supported by health research grant 2013111162 from the Department of Education, Linguistic Policy and Culture of the Basque Country Government. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777720.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer FS declared a past co-authorship with one of the authors MG to the handling Editor.

Footnotes

1.^ https://www. fil. ion. ucl. ac. uk/spm/

2.^ http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/TBSS

3.^ http://www. controlled-trials. com/search? q= HS%2FPI2010001

References

Akiyama, H., Barger, S., Barnum, S., Bradt, B., Bauer, J., Cole, G., et al. (2000). Inflammation and Alzheimer’s disease. Neurobiol. Aging 21, 383–421. doi: 10. 1016/S0197-4580(00)00124-X

Albert, M., and Blacker, D. (2006). Mild cognitive impairment and dementia. Annu. Rev. Clin. Psychol . 2, 379–388. doi: 10. 1146/annurev. clinpsy. 1. 102803. 144039

Avants, B., Dhillon, P., Kandel, B., Cook, P., McMillan, C., Grossman, M., et al. (2012). Eigenanatomy improves detection power for longitudinal cortical change. Med. Image Comput. Comput. Assist. Interv . 15, 206–213. doi: 10. 1007/978-3-642-33454-2_26

Avants, B. B., Libon, D. J., Rascovsky, K., Boller, A., McMillan, C. T., Massimo, L., et al. (2014). Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population. Neuroimage 84, 698–711. doi: 10. 1016/j. neuroimage. 2013. 09. 048

Bach, M., Laun, F. B., Leemans, A., Tax, C. M., Biessels, G. J., Stieltjes, B., et al. (2014). Methodological considerations on tract-based spatial statistics (tBSS). Neuroimage 100, 358–369. doi: 10. 1016/j. neuroimage. 2014. 06. 021

Bauer, I., Pascoe, M., Wollenhaupt-Aguiar, B., Kapczinski, F., and Soares, J. C. (2014). Inflammatory mediators of cognitive impairment in bipolar disorder. J. Psychiatr. Res . 56, 18–27. doi: 10. 1016/j. jpsychires. 2014. 04. 017

Bauer, M., Glenn, T., Alda, M., Andreassen, O., Angelopoulos, E., Ardau, R., et al. (2015a). Influence of birth cohort on age of onset cluster analysis in bipolar I disorder. Eur. Psychiatry 30, 99–105. doi: 10. 1016/j. eurpsy. 2014. 10. 005

Bauer, M., Glenn, T., Alda, M., Andreassen, O. A., Angelopoulos, E., Ardau, R., et al. (2014). Relationship between sunlight and the age of onset of bipolar disorder: an international multisite study. J. Affect. Disord . 167, 104–111. doi: 10. 1016/j. jad. 2014. 05. 032

Bauer, M., Glenn, T., Alda, M., Andreassen, O. A., Angelopoulos, E., Ardau, R., et al. (2015b). Influence of light exposure during early life on the age of onset of bipolar disorder. J. Psychiatr. Res . 64, 1–8. doi: 10. 1016/j. jpsychires. 2015. 03. 013

Berridge, M. J. (2013). Dysregulation of neural calcium signaling in alzheimer disease, bipolar disorder and schizophrenia. Prion 7, 2–13. doi: 10. 4161/pri. 21767

Besga, A., Chyzhyk, D., Gonzalez-Ortega, I., Echeveste, J., Graña-Lecuona, M., Graña, M., et al. (2017). White matter tract integrity in Alzheimer’s disease vs. late onset bipolar disorder and its correlation with systemic inflammation and oxidative stress biomarkers. Front. Aging Neurosci . 9: 179. doi: 10. 3389/fnagi. 2017. 00179

Besga, A., Chyzhyk, D., Gonzalez-Ortega, I., Savio, A., Ayerdi, B., Echeveste, J., et al. (2016). Eigenanatomy on fractional anisotropy imaging provides white matter anatomical features discriminating between Alzheimer’s disease and late onset bipolar disorder. Curr. Alzheimer Res . 13, 557–565. doi: 10. 2174/1567205013666151116125349

Besga, A., Gonzalez, I., Echeburua, E., Savio, A., Ayerdi, B., Chyzhyk, D., et al. (2015a). Discrimination between Alzheimer’s disease and late onset bipolar disorder using multivariate analysis. Front. Aging Neurosci . 7: 231. doi: 10. 3389/fnagi. 2015. 00231

Besga, A., Gonzalez-Ortega, I., Echeburua, E., Savio, A., Ayerdi, B., Chyzhyk, D., et al. (2015b). Discrimination between Alzheimer’s disease and late onset bipolar disorder using multivariate analysis. Front. Aging Neurosci . 7: 231.

Besga, A., Graña, M., and Chyzhyk, D. (2020). Alzheimer’s Disease Versus Bipolar Disorder Versus Health Control MRI Data and Processed Results . Zenodo. doi: 10. 5281/zenodo. 3935636

Besga, A., Martinez-Cengotitabengoa, M., Gonzalez-Ortega, I., Gutierrez, M., Barbeito, S., and Gonzalez-Pinto, A. (2011). The role of white matter damage in late onset bipolar disorder. Maturitas 70, 160–163. doi: 10. 1016/j. maturitas. 2011. 07. 005

Besga, A., Termenon, M., Graña, M., Echeveste, J., Perez, J., and Gonzalez-Pinto, A. (2012). Discovering Alzheimer’s disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features. Neurosci. Lett . 520, 71–76. doi: 10. 1016/j. neulet. 2012. 05. 033

Brydon, L., Walker, C., Wawrzyniak, A., Whitehead, D., Okamura, H., Yajima, J., et al. (2009). Synergistic effects of psychological and immune stressors on inflammatory cytokine and sickness responses in humans. Brain Behav. Immun . 23, 217–224. doi: 10. 1016/j. bbi. 2008. 09. 007

Carlino, A., Stinnett, J., and Kim, D. (2013). New onset of bipolar disorder in late life. Psychosomatics 54, 94–97. doi: 10. 1016/j. psym. 2012. 01. 006

Castanon, N., Lasselin, J., and Capuron, L. (2014). Neuropsychiatric comorbidity in obesity: role of inflammatory processes. Neuroendocr. Sci . 5: 74. doi: 10. 3389/fendo. 2014. 00074

Chou, P.-H., Tseng, W.-J., Chen, L.-M., Lin, C.-C., Lan, T.-H., and Chan, C.-H. (2015). Late onset bipolar disorder: a case report and review of the literature. J. Clin. Gerontol. Geriatr . 6, 27–29. doi: 10. 1016/j. jcgg. 2014. 05. 002

Depp, C., and Jeste, D. (2004). Bipolar disorder in older adults: a critical review. Bipolar Disord . 6, 343–367. doi: 10. 1111/j. 1399-5618. 2004. 00139. x

Dickerson, F., Stallings, C., Origoni, A., Vaughan, C., Katsafanas, E., Khushalani, S., et al. (2013). A combined marker of inflammation in individuals with mania. PLoS ONE 8: e73520. doi: 10. 1371/journal. pone. 0073520

Forcada, I., Mur, M., Mora, E., Vieta, E., Bartras-Faz, D., and Portella, M. (2014). The influence of cognitive reserve on psychosocial and neuropsychological functioning in bipolar disorder. Eur. Neuropsychopharmacol . 25, 214–222. doi: 10. 1016/j. euroneuro. 2014. 07. 018

Goldstein, B., Kemp, D., Soczynska, J., and McIntyre, R. (2009). Inflammation and the phenomenology, pathophysiology, comorbidity, and treatment of bipolar disorder: a systematic review of the literature. J. Clin. Psychiatry 70, 1078–1090. doi: 10. 4088/JCP. 08r04505

Graña, M., Termenon, M., Savio, A., Gonzalez-Pinto, A., Echeveste, J., Perez, J., et al. (2011). Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by pearson’s correlation. Neurosci. Lett . 502, 225–229. doi: 10. 1016/j. neulet. 2011. 07. 049

Hope, S., Hoseth, E., Dieset, I., Mørch, R. H., Aas, M., Aukrust, P., et al. (2015). Inflammatory markers are associated with general cognitive abilities in schizophrenia and bipolar disorder patients and healthy controls. Schizophr. Res . 165, 188–194. doi: 10. 1016/j. schres. 2015. 04. 004

Kamer, A., Craig, R., Dasanayake, A., Brys, M., Glodzik-Sobanska, L., and de Leon, M. (2008). Inflammation and Alzheimer’s disease: possible role of periodontal diseases. Alzheimers Dement . 4, 242–250. doi: 10. 1016/j. jalz. 2007. 08. 004

Konradi, C., Sillivan, S., and Clay, H. (2012). Mitochondria, oligodendrocytes and inflammation in bipolar disorder: evidence from transcriptome studies points to intriguing parallels with multiple sclerosis. Neurobiol. Dis . 45, 37–47. doi: 10. 1016/j. nbd. 2011. 01. 025

Leboyer, M., Soreca, I., Scott, J., Frye, M., Henry, C., Tamouza, R., et al. (2012). Can bipolar disorder be viewed as a multi-system inflammatory disease? J. Affect. Disord . 141, 1–10. doi: 10. 1016/j. jad. 2011. 12. 049

Lee, S.-Y., Chen, S.-L., Chang, Y.-H., Chen, P., and Huang, S.-Y. (2013). Inflammation’s association with metabolic profiles before and after a twelve-week clinical trial in drug-naive patients with bipolar II disorder. PLoS ONE 8: e66847. doi: 10. 1371/journal. pone. 0066847

Martinez-Cengotitabengoa, M., Mico, J., Arango, C., Castro-Fornieles, J., Graell, M., Paya, B., et al. (2014). Basal low antioxidant capacity correlates with cognitive deficits in early onset psychosis. A 2-year follow-up study. Schizophr. Res . 156, 23–29. doi: 10. 1016/j. schres. 2014. 03. 025

Prabhakar, D., and Balon, R. (2010). Late-onset bipolar disorder a case for careful appraisal. Psychiatry 7, 34–37.

Ramirez, J., Gorriz, J., Segovia, F., Chaves, R., Salas-Gonzalez, D., Lopez, M., et al. (2010). “ Early Alzheimer’s disease diagnosis using partial least squares and random forests,” in 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (Rotterdam), 81–84. doi: 10. 1109/ISBI. 2010. 5490408

Salas-Gonzalez, D., Górriz, J. M., Ramírez, J., López, M., Illan, I. A., Segovia, F., et al. (2009). Analysis of SPECT brain images for the diagnosis of Alzheimer’s disease using moments and support vector machines. Neurosci. Lett . 461, 60–64. doi: 10. 1016/j. neulet. 2009. 05. 056

Sardi, F., Fassina, L., Venturini, L., Inguscio, M., Guerriero, F., Rolfo, E., et al. (2011). Alzheimer’s disease, autoimmunity and inflammation. The good, the bad and the ugly. Autoimmun. Rev . 11, 149–153. doi: 10. 1016/j. autrev. 2011. 09. 005

Savio, A., Garcia-Sebastian, M., Chyzyk, D., Hernandez, C., Graña, M., Sistiaga, A., et al. (2011). Neurocognitive disorder detection based on feature vectors extracted from vbm analysis of structural MRI. Comput. Biol. Med . 41, 600–610. doi: 10. 1016/j. compbiomed. 2011. 05. 010

Sigut, J., Piñeiro, J., Gonzalez, E., and Torres, J. (2007). An expert system for supervised classifier design: application to alzheimer diagnosis. Expert Syst. Appl . 32, 927–938. doi: 10. 1016/j. eswa. 2006. 01. 026

Singhal, G., Jaehne, E., Corrigan, F., Toben, C., and Baune, B. (2014). Inflammasomes in neuroinflammation and changes in brain function: a focused review. Neuroendocr. Sci . 8: 315. doi: 10. 3389/fnins. 2014. 00315

Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., et al. (2006). Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487–1505. doi: 10. 1016/j. neuroimage. 2006. 02. 024

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208–S219. doi: 10. 1016/j. neuroimage. 2004. 07. 051

Termenon, M., Graña, M., Besga, A., Echeveste, J., and Gonzalez-Pinto, A. (2013). Lattice independent component analysis feature selection on diffusion weighted imaging for Alzheimer’s disease classification. Neurocomputing 114, 132–141. doi: 10. 1016/j. neucom. 2012. 08. 044

Westman, E., Simmons, A., Zhang, Y., Muehlboeck, J., Tunnard, C., Liu, Y., et al. (2011). Multivariate analysis of MRI data for Alzheimer’s disease, mild cognitive impairment and healthy controls. Neuroimage 54, 1178–1187. doi: 10. 1016/j. neuroimage. 2010. 08. 044

Zahodne, L. B., Ornstein, K., Cosentino, S., Devanand, D., and Stern, Y. (2015). Longitudinal relationships between Alzheimer disease progression and psychosis, depressed mood, and agitation/aggression. Am. J. Geriatr. Psychiatry 23, 130–140. doi: 10. 1016/j. jagp. 2013. 03. 014

Zanetti, M. V., Cordeiro, Q., and Busatto, G. F. (2007). Late onset bipolar disorder associated with white matter hyperintensities: a pathophysiological hypothesis. Prog. Neuropsychopharmacol. Biol. Psychiatry 31, 551–556. doi: 10. 1016/j. pnpbp. 2006. 10. 004