Chronic Auditory Hallucinations in Schizophrenic Patients: MR Analysis of the Coincidence between Functional and Morphologic Abnormalities

Martí‐Bonmatí, Luis; Lull, Juan José; García-Martí, Gracián; Aguilar, Eduardo J.; Moratal-Pérez, David; Poyatos, C.; Robles, Montserrat; Sanjuán, Julio

doi:10.1148/radiol.2442060727

Cited by 50 publications

(23 citation statements)

References 40 publications

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“…The learned features of the DNN weights between the input layer and the first hidden layer are in agreement with previous studies and include findings such as aberrant FC patterns between the thalamus and the cerebellum (Çetin et al, 2014; Collin et al, 2011; Magnotta et al, 2008) and between the frontal and the temporal areas, possibly due to auditory hallucinations (Martí-Bonmatí et al, 2007). The reduced FC level from SZ group between the precuneus/PCC and the striatum may indicate altered striatal dopaminergic functions (Dandash et al, 2013; Tu et al, 2012).…”

Section: Discussionsupporting

confidence: 88%

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

et al. 2016

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Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns.

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Section: Discussionsupporting

confidence: 88%

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

et al. 2016

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“…Because the majority of bipolar studies comprised patients who had an illness duration of over 5 years, and were medicated, the search extended to allow matching using the keywords: schizophrenia, chronic schizophrenia, psychosis, medicated, medication, MRI; found 54 schizophrenia studies (Wright et al, 1999; Hulshoff Pol et al, 2001; Paillere-Martinot et al, 2001; Sigmundsson et al, 2001; Wilke et al, 2001; Ananth et al, 2002; Job et al, 2002; Kubicki et al, 2002; Shapleske et al, 2002; Suzuki et al, 2002; Marcelis et al, 2003; Salgado-Pineda et al, 2003, 2004; Ha et al, 2004; Moorhead et al, 2004; Antonova et al, 2005; Giuliani et al, 2005; Honea et al, 2005; Jayakumar et al, 2005; McDonald et al, 2005; Neckelmann et al, 2006; Ohnishi et al, 2006; Whitford et al, 2006; Bassitt et al, 2007; Chua et al, 2007; Douaud et al, 2007; Kasparek et al, 2007, 2010; Kawasaki et al, 2007; Marti-Bonmati et al, 2007; Prasad et al, 2007; Schaufelberger et al, 2007; Tregellas et al, 2007; Yamada et al, 2007; Cooke et al, 2008; Garcia-Marti et al, 2008; Hirao et al, 2008; Koutsouleris et al, 2008; Meda et al, 2008; Meisenzahl et al, 2008; Venkatasubramanian et al, 2008; Wolf et al, 2008; Yoshihara et al, 2008; Benedetti et al, 2009; Euler et al, 2009; Herold et al, 2009; Lui et al, 2009a,b; Segall et al, 2009; Tomelleri et al, 2009; Witthaus et al, 2009; Xu et al, 2009). Of these studies, Antonova et al (2005), Cooke et al (2008), Farrow et al (2005), Giuliani et al (2005), and Janssen et al (2008) included patients with schizoaffective disorder.…”

Section: Methodsmentioning

confidence: 99%

Are Bipolar Disorder and Schizophrenia Neuroanatomically Distinct? An Anatomical Likelihood Meta-analysis

Cheung

Leung

et al. 2010

Front. Hum. Neurosci.

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Objective: There is renewed debate on whether modern diagnostic classification should adopt a dichotomous or dimensional approach to schizophrenia and bipolar disorder. This study synthesizes data from voxel-based studies of schizophrenia and bipolar disorder to estimate the extent to which these conditions have a common neuroanatomical phenotype. Methods: A post-hoc meta-analytic estimation of the extent to which bipolar disorder, schizophrenia, or both conditions contribute to brain gray matter differences compared to controls was achieved using a novel application of the conventional anatomical likelihood estimation (ALE) method. 19 schizophrenia studies (651 patients and 693 controls) were matched as closely as possible to 19 bipolar studies (540 patients and 745 controls). Result: Substantial overlaps in the regions affected by schizophrenia and bipolar disorder included regions in prefrontal cortex, thalamus, left caudate, left medial temporal lobe, and right insula. Bipolar disorder and schizophrenia jointly contributed to clusters in the right hemisphere, but schizophrenia was almost exclusively associated with additional gray matter deficits (left insula and amygdala) in the left hemisphere. Limitation: The current meta-analytic method has a number of constraints. Importantly, only studies identifying differences between controls and patient groups could be included in this analysis. Conclusion: Bipolar disorder shares many of the same brain regions as schizophrenia. However, relative to neurotypical controls, lower gray matter volume in schizophrenia is more extensive and includes the amygdala. This fresh application of ALE accommodates multiple studies in a relatively unbiased comparison. Common biological mechanisms may explain the neuroanatomical overlap between these major disorders, but explaining why brain differences are more extensive in schizophrenia remains challenging.

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“…The superior temporal gyrus has also been linked to verbal hallucinations. In schizophrenia patients with marked auditory hallucinations, gray matter volume loss was observed in the STG bilaterally [Garcia-Marti et al, 2008;O'Daly et al, 2007], where gray matter volume correlated with the severity of hallucinations [Neckelmann et al, 2006;O'Daly et al, 2007] and where gray matter loss overlapped with functional abnormalities [Marti-Bonmati et al, 2007]. The STG is also involved in thought disorders and conceptual disorganization [Subotnik et al, 2003], semantic processing abnormalities [Nestor et al, 1993], verbal fluency Clusters of gray matter changes from the z-map derived from VBM analysis-thresholded at Z ¼ 3.…”

Section: Figurementioning

confidence: 97%

Source‐based morphometry of gray matter volume in men with first‐episode schizophrenia

Kašpárek

Mareček

Schwarz

et al. 2009

Human Brain Mapping

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Chronic Auditory Hallucinations in Schizophrenic Patients: MR Analysis of the Coincidence between Functional and Morphologic Abnormalities

Abstract: The middle and superior temporal and the cingular gyri are closely related to the abnormal neural network involved in the auditory emotional dysfunction seen in schizophrenic patients.

Cited by 50 publications

References 40 publications

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

Are Bipolar Disorder and Schizophrenia Neuroanatomically Distinct? An Anatomical Likelihood Meta-analysis

Source‐based morphometry of gray matter volume in men with first‐episode schizophrenia

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