@article {923,
	title = {Neurobiological underpinnings of cognitive subtypes in psychoses: A cross-diagnostic cluster analysis},
	journal = {Schizophrenia Research},
	volume = {229},
	year = {2021},
	pages = {102-111},
	abstract = {<p>Schizophrenia and bipolar disorder include patients with different characteristics, which may hamper the definition of biomarkers. One of the dimensions with greater heterogeneity among these patients is cognition. Recent studies support the identification of different patients{\textquoteright} subgroups along the cognitive domain using cluster analysis. Our aim was to validate clusters defined on the basis of patients{\textquoteright} cognitive status and to assess its relation with demographic, clinical and biological measurements. We hypothesized that subgroups characterized by different cognitive profiles would show differences in an array of biological data. Cognitive data from 198 patients (127 with chronic schizophrenia, 42 first episodes of schizophrenia and 29 bipolar patients) were analyzed by a K-means cluster approach and were compared on several clinical and biological variables. We also included 155 healthy controls for further comparisons. A two-cluster solution was selected, including a severely impaired group and a moderately impaired group. The severely impaired group was associated with higher illness duration and symptoms scores, lower thalamus and hippocampus volume, lower frontal connectivity and basal hypersynchrony in comparison to controls and the moderately impaired group. Moreover, both patients{\textquoteright} groups showed lower cortical thickness and smaller functional connectivity modulation than healthy controls. This study supports the existence of different cognitive subgroups within the psychoses with different neurobiological underpinnings.</p>
},
	keywords = {Cognition, Connectivity, Modulation, Volume, bipolar disorder, schizophrenia},
	issn = {0920-9964},
	doi = {https://doi.org/10.1016/j.schres.2020.11.013},
	url = {https://www.sciencedirect.com/science/article/pii/S0920996420305521},
	author = {Fern{\'a}ndez-Linsenbarth, In{\'e}s and {\'A}lvaro Planchuelo-G{\'o}mez and D{\'\i}ez, {\'A}lvaro and Arjona-Valladares, Antonio and Rodrigo de Luis-Garc{\'\i}a and Mart{\'\i}n-Santiago, {\'O}scar and Benito-S{\'a}nchez, Jos{\'e} Antonio and P{\'e}rez-Laureano, {\'A}ngela and Gonz{\'a}lez-Parra, David and Montes-Gonzalo, Carmen and Melero-Lerma, Raquel and Fern{\'a}ndez Morante, Sonia and Sanz-Fuentenebro, Javier and G{\'o}mez-Pilar, Javier and N{\'u}{\~n}ez-Novo, Pablo and Molina, Vicente}
}
@conference {aja2013noise,
	title = {Noise estimation in magnetic resonance SENSE reconstructed data},
	booktitle = {Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE},
	year = {2013},
	pages = {1104{\textendash}1107},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Santiago Aja-Fern{\'a}ndez and Gonzalo Vegas-S{\'a}nchez-Ferrero and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega}
}
@proceedings {de2002neural,
	title = {A neural architecture for bone age assessment},
	year = {2002},
	pages = {161{\textendash}166},
	author = {Rodrigo de Luis-Garc{\'\i}a and J I Arribas and Santiago Aja-Fern{\'a}ndez and Lopez, C Alberola}
}
