Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 4 de 4
Filter
1.
Psychol Med ; 44(3): 519-32, 2014 Feb.
Article in English | MEDLINE | ID: mdl-23734914

ABSTRACT

BACKGROUND: Bipolar disorder (BD) is one of the leading causes of disability worldwide. Patients are further disadvantaged by delays in accurate diagnosis ranging between 5 and 10 years. We applied Gaussian process classifiers (GPCs) to structural magnetic resonance imaging (sMRI) data to evaluate the feasibility of using pattern recognition techniques for the diagnostic classification of patients with BD. METHOD: GPCs were applied to gray (GM) and white matter (WM) sMRI data derived from two independent samples of patients with BD (cohort 1: n = 26; cohort 2: n = 14). Within each cohort patients were matched on age, sex and IQ to an equal number of healthy controls. RESULTS: The diagnostic accuracy of the GPC for GM was 73% in cohort 1 and 72% in cohort 2; the sensitivity and specificity of the GM classification were respectively 69% and 77% in cohort 1 and 64% and 99% in cohort 2. The diagnostic accuracy of the GPC for WM was 69% in cohort 1 and 78% in cohort 2; the sensitivity and specificity of the WM classification were both 69% in cohort 1 and 71% and 86% respectively in cohort 2. In both samples, GM and WM clusters discriminating between patients and controls were localized within cortical and subcortical structures implicated in BD. CONCLUSIONS: Our results demonstrate the predictive value of neuroanatomical data in discriminating patients with BD from healthy individuals. The overlap between discriminative networks and regions implicated in the pathophysiology of BD supports the biological plausibility of the classifiers.


Subject(s)
Bipolar Disorder/diagnosis , Brain/pathology , Magnetic Resonance Imaging/methods , Pattern Recognition, Automated/methods , Predictive Value of Tests , Adult , Algorithms , Bipolar Disorder/drug therapy , Bipolar Disorder/pathology , Case-Control Studies , Delayed Diagnosis/adverse effects , Diagnosis, Differential , Diagnostic and Statistical Manual of Mental Disorders , Feasibility Studies , Female , Humans , Image Processing, Computer-Assisted/methods , Magnetic Resonance Imaging/classification , Male , Normal Distribution , Pattern Recognition, Automated/classification
2.
Psychol Med ; 42(5): 1037-47, 2012 May.
Article in English | MEDLINE | ID: mdl-22059690

ABSTRACT

BACKGROUND: To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode. METHOD: One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls. RESULTS: At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035). CONCLUSIONS: We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data.


Subject(s)
Individuality , Magnetic Resonance Imaging/methods , Psychotic Disorders/diagnosis , Psychotic Disorders/physiopathology , Support Vector Machine , Adult , Brain/physiopathology , Brain Mapping/methods , Cohort Studies , Disease Progression , Female , Follow-Up Studies , Humans , Image Processing, Computer-Assisted/methods , Male , Observer Variation , Predictive Value of Tests , Reproducibility of Results
3.
Neurosci Biobehav Rev ; 76(Pt A): 29-38, 2017 05.
Article in English | MEDLINE | ID: mdl-28131873

ABSTRACT

Violence exacts a burden on public health. Gun violence is a major trigger for motor defensive reactions in humans and post-traumatic stress disorder (PTSD) is its main psychiatric sequela. However, studies of the human defensive cascade, especially the motor reactions, are at an early stage. This review focuses on studies that employ stabilometry, a methodology that assesses whole body motor reactions, to address defensive behaviors to violence-related threats. Special attention is given to three reactions: "attentive immobility", "immobility under attack" and "tonic immobility", with emphasis on the latter - a peritraumatic reaction which has been strongly associated with the severity of PTSD. These reactions are characterized by reduced body sway and bradycardia, except tonic immobility that presents robust tachycardia. The advances made by investigations into the immobility reactions of the human defensive cascade contribute to helping to bridge the gap between human and non-human species. Furthermore, progresses in basic research to objectively monitor motor defensive reactions under threat can help to develop a dimensional, trans-diagnostic approach to PTSD.


Subject(s)
Defense Mechanisms , Stress Disorders, Post-Traumatic , Humans , Immobility Response, Tonic
4.
Brain Res Bull ; 76(1-2): 26-35, 2008 May 15.
Article in English | MEDLINE | ID: mdl-18395607

ABSTRACT

The architecture of the amygdaloid complex of a marsupial, the opossum Didelphis aurita, was analyzed using classical stains like Nissl staining and myelin (Gallyas) staining, and enzyme histochemistry for acetylcholinesterase and NADPH-diaphorase. Most of the subdivisions of the amygdaloid complex described in eutherian mammals were identified in the opossum brain. NADPH-diaphorase revealed reactivity in the neuropil of nearly all amygdaloid subdivisions with different intensities, allowing the identification of the medial and lateral subdivisions of the cortical posterior nucleus and the lateral subdivision of the lateral nucleus. The lateral, central, basolateral and basomedial nuclei exhibited acetylcholinesterase positivity, which provided a useful chemoarchitectural criterion for the identification of the anterior basolateral nucleus. Myelin stain allowed the identification of the medial subdivision of the lateral nucleus, and resulted in intense staining of the medial subdivisions of the central nucleus. The medial, posterior, and cortical nuclei, as well as the amygdalopiriform area did not exhibit positivity for myelin staining. On the basis of cyto- and chemoarchitectural criteria, the present study highlights that the opossum amygdaloid complex shares similarities with that of other species, thus supporting the idea that the organization of the amygdala is part of a basic plan conserved through mammalian evolution.


Subject(s)
Amygdala/anatomy & histology , Histocytochemistry/methods , Opossums/anatomy & histology , Acetylcholinesterase/metabolism , Amygdala/metabolism , Animals , Myelin Sheath/metabolism , NADPH Dehydrogenase/metabolism , Staining and Labeling/methods
SELECTION OF CITATIONS
SEARCH DETAIL