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.
Abstract: Despite its great promise, neuroimaging has yet to substantially impact clinical practice and public health. However, a developing synergy between emerging analysis techniques and data-sharing initiatives has the potential to transform the role of neuroimaging in clinical applications. We review the state of translational neuroimaging and outline an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings. The approach rests on three pillars: (i) the use of multivariate pattern-recognition techniques to develop brain signatures for clinical outcomes and relevant mental processes; (ii) assessment and optimization of their diagnostic value; and (iii) a program of broad exploration followed by increasingly rigorous assessment of generalizability across samples, research contexts and populations. Increasingly sophisticated models based on these principles will help to overcome some of the obstacles on the road from basic neuroscience to better health and will ultimately serve both basic and applied goals.
Pub.: 23 Feb '17, Pinned: 31 Jul '17
Abstract: The brain functional connectome constitutes a unique fingerprint allowing identification of individuals among a pool of people. Here we establish that the connectome develops into a more stable, individual wiring pattern during adolescence and demonstrate that a delay in this network tuning process is associated with reduced mental health in the formative years of late neurodevelopment.
Pub.: 22 Feb '17, Pinned: 31 Jul '17
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
Abstract: Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.
Pub.: 14 Apr '16, Pinned: 31 Jul '17
Abstract: Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.
Pub.: 13 Oct '15, Pinned: 31 Jul '17
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