title: Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Stony Brook University, New York, New York, United States of America.
authors.forename: Tomer
authors.initials: T
authors.lastname: Fekete
authors.affiliation: Biological Basis of Behavior Program, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Neta
authors.initials: N
authors.lastname: Zach
authors.affiliation: Department of Biomedical Engineering, Stony Brook University, New York, New York, United States of America.
authors.forename: Lilianne R
authors.initials: LR
authors.lastname: Mujica-Parodi
authors.affiliation: Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
authors.forename: Martin R
authors.initials: MR
authors.lastname: Turner
mesh_terms: D000690:Amyotrophic Lateral Sclerosis / Q000175:diagnosis* / Q000503:physiopathology*; D015415:Biomarkers; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping; D016022:Case-Control Studies; D063132:Connectome / Q000379:methods*; D004525:Efferent Pathways / Q000502:physiology*; D004739:England; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D049490:Systems Biology
chemical_list: D015415:Biomarkers