Haleh Falakshahi
Georgia Institute of Technology
ModularityPsychiatryMental healthDefault mode networkDiffusion MRIGraphical modelPattern recognitionMood disordersNosologySchizophreniaFractional anisotropy
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Publications 5
#1Hooman Rokham (Georgia Institute of Technology)
#2Godfrey D. Pearlson (Yale University)H-Index: 110
Last. Vince Calhoun (GSU: Georgia State University)
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Background: Mental health diagnostic approaches are seeking to identify biological markers to work alongside advanced machine learning approaches. It is difficult to identify a biological marker of disease when the traditional diagnostic labels themselves are not necessarily valid. Methods: We worked with T1 structural magnetic resonance imaging data collected from individuals with mood and psychosis disorders from over 1400 individuals comprising healthy controls, psychosis patients and their u...
#2Haleh FalakshahiH-Index: 1
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Psychotic disorders such as schizophrenia and bipolar disorder are difficult to classify because they share overlapping symptoms. Deriving biomarkers of illness using structural MRI dataset are essential because they may lead to improved diagnosis. Previous studies typically predict the diagnosis labels using supervised classifiers that rely on truly labeled dataset. Mislabeled subjects may increase the complexity of the predictive model and may impact its performance. In this work, we address t...
1 CitationsSource
Objective: Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ). Methods: We start with estimat...
1 CitationsSource