Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.

There is significant clinical and prognostic heterogeneity in the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), despite a common immunohistological signature. Consistent extra-motor as well as motor cerebral, spinal anterior horn and distal neuromuscular junction pathology supports...

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Autores principales:	Tomer Fekete, Neta Zach, Lilianne R Mujica-Parodi, Martin R Turner
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b13a3c7252914c23a047d94c396f89c6
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spelling	oai:doaj.org-article:b13a3c7252914c23a047d94c396f89c62021-11-18T08:39:15ZMultiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.1932-620310.1371/journal.pone.0085190https://doaj.org/article/b13a3c7252914c23a047d94c396f89c62013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24391997/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203There is significant clinical and prognostic heterogeneity in the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), despite a common immunohistological signature. Consistent extra-motor as well as motor cerebral, spinal anterior horn and distal neuromuscular junction pathology supports the notion of ALS a system failure. Establishing a disease biomarker is a priority but a simplistic, coordinate-based approach to brain dysfunction using MRI is not tenable. Resting-state functional MRI reflects the organization of brain networks at the systems-level, and so changes in of motor functional connectivity were explored to determine their potential as the substrate for a biomarker signature. Intra- as well as inter-motor functional networks in the 0.03-0.06 Hz frequency band were derived from 40 patients and 30 healthy controls of similar age, and used as features for pattern detection, employing multiple kernel learning. This approach enabled an accurate classification of a group of patients that included a range of clinical sub-types. An average of 13 regions-of-interest were needed to reach peak discrimination. Subsequent analysis revealed that the alterations in motor functional connectivity were widespread, including regions not obviously clinically affected such as the cerebellum and basal ganglia. Complex network analysis showed that functional networks in ALS differ markedly in their topology, reflecting the underlying altered functional connectivity pattern seen in patients: 1) reduced connectivity of both the cortical and sub-cortical motor areas with non motor areas 2)reduced subcortical-cortical motor connectivity and 3) increased connectivity observed within sub-cortical motor networks. This type of analysis has potential to non-invasively define a biomarker signature at the systems-level. As the understanding of neurodegenerative disorders moves towards studying pre-symptomatic changes, there is potential for this type of approach to generate biomarkers for the testing of neuroprotective strategies.Tomer FeketeNeta ZachLilianne R Mujica-ParodiMartin R TurnerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e85190 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tomer Fekete Neta Zach Lilianne R Mujica-Parodi Martin R Turner Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.
description	There is significant clinical and prognostic heterogeneity in the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), despite a common immunohistological signature. Consistent extra-motor as well as motor cerebral, spinal anterior horn and distal neuromuscular junction pathology supports the notion of ALS a system failure. Establishing a disease biomarker is a priority but a simplistic, coordinate-based approach to brain dysfunction using MRI is not tenable. Resting-state functional MRI reflects the organization of brain networks at the systems-level, and so changes in of motor functional connectivity were explored to determine their potential as the substrate for a biomarker signature. Intra- as well as inter-motor functional networks in the 0.03-0.06 Hz frequency band were derived from 40 patients and 30 healthy controls of similar age, and used as features for pattern detection, employing multiple kernel learning. This approach enabled an accurate classification of a group of patients that included a range of clinical sub-types. An average of 13 regions-of-interest were needed to reach peak discrimination. Subsequent analysis revealed that the alterations in motor functional connectivity were widespread, including regions not obviously clinically affected such as the cerebellum and basal ganglia. Complex network analysis showed that functional networks in ALS differ markedly in their topology, reflecting the underlying altered functional connectivity pattern seen in patients: 1) reduced connectivity of both the cortical and sub-cortical motor areas with non motor areas 2)reduced subcortical-cortical motor connectivity and 3) increased connectivity observed within sub-cortical motor networks. This type of analysis has potential to non-invasively define a biomarker signature at the systems-level. As the understanding of neurodegenerative disorders moves towards studying pre-symptomatic changes, there is potential for this type of approach to generate biomarkers for the testing of neuroprotective strategies.
format	article
author	Tomer Fekete Neta Zach Lilianne R Mujica-Parodi Martin R Turner
author_facet	Tomer Fekete Neta Zach Lilianne R Mujica-Parodi Martin R Turner
author_sort	Tomer Fekete
title	Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.
title_short	Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.
title_full	Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.
title_fullStr	Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.
title_full_unstemmed	Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.
title_sort	multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/b13a3c7252914c23a047d94c396f89c6
work_keys_str_mv	AT tomerfekete multiplekernellearningcapturesasystemslevelfunctionalconnectivitybiomarkersignatureinamyotrophiclateralsclerosis AT netazach multiplekernellearningcapturesasystemslevelfunctionalconnectivitybiomarkersignatureinamyotrophiclateralsclerosis AT liliannermujicaparodi multiplekernellearningcapturesasystemslevelfunctionalconnectivitybiomarkersignatureinamyotrophiclateralsclerosis AT martinrturner multiplekernellearningcapturesasystemslevelfunctionalconnectivitybiomarkersignatureinamyotrophiclateralsclerosis
_version_	1718421522443403264

Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.

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