A pinboard by
Vicente Pallarés

PhD student, Pompeu Fabra University


Connectivity measure as a biomarker of conditions and pathology

Neuroscience traditionally tackled the study of the brain by trying to infer the mechanisms that explain the cognitive or pathological processes common to a particular population. It has been only recently, that a more subject specific perspective has been adopted, taking into account the particular characteristics of each individual. Some studies have demonstrated the discrimination power between subjects of brain functional connectivity, whereas some others have emphasized the underlying functional structure common to a group of subjects that perform the same task. Our work is the first one that addresses these two points simultaneously: the connectivity patterns that differ from one condition to another and the individual differences between subjects. To do that, we estimate the effective connectivity between brain regions through a generative model and extract those causal interactions between areas specific to a task or condition. Moreover, the identity of each subject is preserved, which allows for a more subject-specific study of both cognition and pathology. We claim that effective connectivity is the best space to perform this bidimensional discrimination of both condition and subjects and we prove it by using identification of subjects on different datasets, as well as on discriminating between resting state and free viewing conditions. This promising results are the first steps in a new direction, applying neuroimaging to a more personalized medicine and cognitive neuroscience.


Resting-state connectivity biomarkers define neurophysiological subtypes of depression.

Abstract: Biomarkers have transformed modern medicine but remain largely elusive in psychiatry, partly because there is a weak correspondence between diagnostic labels and their neurobiological substrates. Like to other neuropsychiatric disorders, depression is not a unitary disease, but rather a heterogeneous syndrome that encompasses varied, co-occurring symptoms and divergent responses to treatment. By using functional magnetic resonance imaging (fMRI) in a large multisite sample (n = 1,188), we show here that patients with depression can be subdivided into four neurophysiological subtypes ('biotypes') defined by distinct patterns of dysfunctional connectivity in limbic and frontostriatal networks. Clustering patients on this basis enabled the development of diagnostic classifiers (biomarkers) with high (82-93%) sensitivity and specificity for depression subtypes in multisite validation (n = 711) and out-of-sample replication (n = 477) data sets. These biotypes cannot be differentiated solely on the basis of clinical features, but they are associated with differing clinical-symptom profiles. They also predict responsiveness to transcranial-magnetic-stimulation therapy (n = 154). Our results define novel subtypes of depression that transcend current diagnostic boundaries and may be useful for identifying the individuals who are most likely to benefit from targeted neurostimulation therapies.

Pub.: 06 Dec '16, Pinned: 31 Jul '17