Abnormal Resting State fMRI Activity Predicts Processing Speed Deficits in First-Episode Psychosis

Abstract

Little is known regarding the neuropsychological significance of resting state functional magnetic resonance imaging (rs-fMRI) activity early in the course of psychosis. Moreover, no studies have used different approaches for analysis of rs-fMRI activity and examined gray matter thickness in the same cohort. In this study, 41 patients experiencing a first-episode of psychosis (including N ¼ 17 who were antipsychotic drug-naive at the time of scanning) and 41 individually age-and sex-matched healthy volunteers completed rs-fMRI and structural MRI exams and neuropsychological assessments. We computed correlation matrices for 266 regions-of-interest across the brain to assess global connectivity. In addition, independent component analysis (ICA) was used to assess group differences in the expression of rs-fMRI activity within 20 predefined publicly available templates. Patients demonstrated lower overall rs-fMRI global connectivity compared with healthy volunteers without associated group differences in gray matter thickness assessed within the same regions-of-interest used in this analysis. Similarly, ICA revealed worse rs-fMRI expression scores across all 20 networks in patients compared with healthy volunteers, with posthoc analyses revealing significant (po0.05; corrected) abnormalities within the caudate nucleus and planum temporale. Worse processing speed correlated significantly with overall lower global connectivity using the region-of-interest approach and lower expression scores within the planum temporale using ICA. Our findings implicate dysfunction in rs-fMRI activity in first-episode psychosis prior to extensive antipsychotic treatment using different analytic approaches (in the absence of concomitant gray matter structural differences) that predict processing speed.

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

“…Though valuable information would be evident using larger ROIs, such methods do not exclude the possibility that some more focused abnormalities could be missed, as reported (Argyelan et al, 2015). By contrast, we adopted several advanced feature selection approaches, including ReliefF and CFS, to mine from tens of thousands of brain voxels efficiently, and the comparisons in Table 3 did show advantages of the proposed method.…”

Increased gyrification in schizophrenia and non affective first episode of psychosis

“…Specifically, Satterthwaite et al (2016) showed altered frontal cortex in youth with psychosis spectrum disorders. Moreover, similarly to schizophrenia, altered structural (Luck et al, 2011;Price et al, 2008) and functional (Alonso-Solís et al, 2012;Argyelan et al, 2015;Fornito et al, 2013;Schmidt et al, 2013) PFC connectivity has also been reported for FEP. Therefore, despite the presence of inconsistencies in the available literature, our study suggests that altered GI and, consequently, disrupted PFC connectivity represents an indirect developmental marker for psychosis, overcoming the categorical diagnosis of schizophrenia (White and Gottesman, 2012), in line with a dimensional approach as per the Research Domain Criteria approach (Cuthbert and Insel, 2010).…”

Cortical sensorimotor alterations classify clinical phenotype and putative genotype of spasmodic dysphonia

“…Algorithmic classifiers have been successfully applied in several neurodegenerative disorders using structural [22,23] and functional [24][25][26] magnetic resonance imaging (fMRI) measures. Machine learning and fMRI voxel-wise multivariate classification were implemented as powerful tools for decoding neural representations of thoughts or physical objects at a particular time-point [27][28][29], with the LDA being validated in both normal [30][31][32][33] and disordered [34][35][36][37] states.…”