Archival ReportMultivariate Pattern Analysis of Functional Magnetic Resonance Imaging Data Reveals Deficits in Distributed Representations in Schizophrenia
Section snippets
Subjects
Nineteen individuals with schizophrenia (SZ) and 15 healthy control subjects (C) were studied. Demographic and clinical data are displayed in Table 1. Data from a subset of these subjects have been published in a separate study (18). Patients were clinically stable and outpatients. Diagnostic status was evaluated with the Structured Clinical Interview for DSM-IV-Text Revision (SCID-I) conducted by masters- or doctoral-level clinicians and confirmed by consensus conference. Symptoms were
Results
Both groups performed the task well, but control subjects showed significantly higher accuracy than patients, 93% versus 81% (t = −5.5, p < .001). For all categories, control subject performance was higher, and for all but the everyday objects, the differences were significant. Control subjects responded faster, but RT differences were not significant (MC= 565 msec, SDC= 96 msec, MSZ= 617 msec, SDSZ = 117 msec, p = .17). For faces, there was a nonsignificant difference in reaction time (t =
Discussion
The accuracy of a multivariate pattern classifier was high for healthy subjects and in close agreement with other studies (15, 25). Classification accuracy was significantly lower in subjects with schizophrenia. In both groups, there was an inverse correlation between classification accuracy and reaction time, and in the schizophrenia group, there was a positive correlation between classification accuracy and behavioral accuracy. Diminished voxelwise correlation in activity across runs in
References (29)
Working memory as an emergent property of the mind and brain
Neuroscience
(2006)- et al.
Beyond mind-reading: Multi-voxel pattern analysis of fMRI data
Trends Cogn Sci
(2006) - et al.
Predicting the stream of consciousness from activity in human visual cortex
Curr Biol
(2005) - et al.
Preserved function of the fusiform face area in schizophrenia as revealed by fMRI
Psychiatry Res
(2006) - et al.
Category-specific modulation of inferior temporal activity during working memory encoding and maintenance
Brain Res Cogn Brain Res
(2004) - et al.
The variability of human, BOLD hemodynamic responses
Neuroimage
(1998) - et al.
A trial-based experimental design for fMRI
Neuroimage
(1997) - et al.
Using event-related fMRI to assess delay-period activity during performance of spatial and nonspatial working memory tasks
Brain Res Brain Res Protoc
(2000) - et al.
Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: Is there a ”face” area?
Neuroimage
(2004) - et al.
An integrative theory of prefrontal cortex function
Annu Rev Neurosci
(2001)
Selective deficits in prefrontal cortex function in medication-naive patients with schizophrenia
Arch Gen Psychiatry
Physiologic dysfunction of dorsolateral prefrontal cortex in schizophreniaII. Role of neuroleptic treatment, attention, and mental effort
Arch Gen Psychiatry
Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited
Cereb Cortex
Impaired recruitment of the hippocampus during conscious recollection in schizophrenia
Nat Neurosci
Cited by (37)
Multi feature fusion network for schizophrenia classification and abnormal brain network recognition
2024, Brain Research BulletinGender differences in attentive bias during social information processing in schizophrenia: An eye-tracking study
2021, Asian Journal of PsychiatryNeural Representation in Visual Word Form Area during Word Reading
2021, NeuroscienceCitation Excerpt :Finally, among studies investigating whether the VWFA represents case information, most of them analyzed their neuroimaging data by performing univariate activation analysis, in which each voxel is treated independently and multi-voxel pattern information is missed (Haxby, 2012; Haynes, 2015). The multi-voxel pattern information should be taken into account because the neural pattern information has been repeatedly found to be associated with information processing (Norman et al., 2006; Haxby, 2012; Haynes, 2015; Heilbron et al., 2020), and because there is evidence that the neural pattern information differs across different conditions even in brain regions showing common activations in several domains such as language and cognitive control (Yoon et al., 2008; Raizada et al., 2010; Xu et al., 2017; Carlos et al., 2019). In addition, multivariate methods (e.g., multivariate pattern analysis, MVPA) are able to examine stimulus-independent representation by training the classifier to distinguish one type of stimuli (e.g., lowercase words and consonant strings) and testing the classifier’s ability in predicting another type of stimuli (e.g., uppercase words and consonant strings) which own the same abstract representation as the stimuli used for training (Wurm et al., 2016; Van de Putte et al., 2017; Heilbron et al., 2020).
Insular functional alterations in emotional processing of schizophrenia patients revealed by Multivariate Pattern Analysis fMRI
2020, Journal of Psychiatric Research