Neuroimaging in Schizophrenia

Keshavan, Matcheri S.; Collin, Guusje; Guimond, Synthia; Kelly, Sinéad; Prasad, Konasale; Lizano, Paulo

doi:10.1016/j.nic.2019.09.007

Cited by 103 publications

(46 citation statements)

References 90 publications

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“…Our research is in line with the growing interest in predictive modeling using neuroimaging and machine learning methods (Janssen et al, 2018;Keshavan et al, 2020). Most machine learning studies have focused on differential diagnosis, identifying brain signatures that discriminate patients from healthy controls, and further established objective signs of disease pathology (Lai, 2019).…”

Section: Discussionsupporting

confidence: 61%

Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea

Xie

et al. 2020

Front. Neurosci.

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Primary dysmenorrhea (PDM) is a common complaint in women throughout the menstrual years. Acupuncture has been shown to be effective in dysmenorrhea; however, there are large interindividual differences in patients' responses to acupuncture treatment. Fifty-four patients with PDM were recruited and randomized into real or sham acupuncture treatment groups (over the course of three menstrual cycles). Pain-related functional connectivity (FC) matrices were constructed at baseline and post-treatment period. The different neural mechanisms altered by real and sham acupuncture were detected with multivariate analysis of variance. Multivariate pattern analysis (MVPA) based on a machine learning approach was used to explore whether the different FC patterns predicted the acupuncture treatment response in the PDM patients. The results showed that real but not sham acupuncture significantly relieved pain severity in PDM patients. Real and sham acupuncture displayed differences in FC alterations between the descending pain modulatory system (DPMS) and sensorimotor network (SMN), the salience network (SN) and SMN, and the SN and default mode network (DMN). Furthermore, MVPA found that these FC patterns at baseline could predict the acupuncture treatment response in PDM patients. The present study verified differentially altered brain mechanisms underlying real and sham acupuncture in PDM patients and supported the use of neuroimaging biomarkers for individual-based precise acupuncture treatment in patients with PDM.

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Section: Discussionsupporting

confidence: 61%

Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea

Xie

et al. 2020

Front. Neurosci.

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“…Although they are not yet sufficiently specific to be of diagnostic value, substantive evidence of structural, functional, and neurochemical brain alterations in SZ corroborates both the neurodevelopmental and neurodegenerative models of this disorder. 37 A comprehensive meta-analysis of more than 18,000 people found that those with SZ had lower brain volumes than controls, both globally (total volumes) and in several specific cortical and subcortical structures. Curiously, the findings were similar regardless of antipsychotic use.…”

Section: Neuroimaging Insightsmentioning

confidence: 99%

Heterogeneous trajectories in schizophrenia: insights from neurodevelopment and neuroprogression models

Reckziegel

Czepielewski

Hasse-Sousa

et al. 2022

Braz. J. Psychiatry

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The notion that schizophrenia is a neuroprogressive disorder is based on clinical perception of cumulative impairments over time and is supported by neuroimaging and biomarker research. Nevertheless, increasing evidence has indicated that schizophrenia first emerges as a neurodevelopmental disorder that could follow various pathways, some of them neuroprogressive. The objective of this review is to revisit basic research on cognitive processes and neuroimaging findings in a search for candidate keys to the intricate connections between neurodevelopment and neuroprogression in schizophrenia. In the complete panorama, schizophrenia is a neurodevelopmental disorder, possibly associated with an additional burden over the course of the disease through pathologically accelerated aging, and cognitive heterogeneity may explain the different trajectories of each patient.

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“…Schizophrenia is a complex disorder characterized by neuroanatomical ( 23 , 24 ), genetic ( 25 , 26 ), psychophysiological ( 27 , 28 ) and functional ( 29 , 30 ) impairments. Among these deficits, cognitive decline is a core feature of the disorder as evidenced by neuropsychological ( 31 , 32 ) as well as functional neuroimaging ( 33 , 34 ) studies.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Functioning and Schizotypy: A Four-Years Study

2021

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Although there is ample evidence from cross-sectional studies indicating cognitive deficits in high schizotypal individuals that resemble the cognitive profile of schizophrenia-spectrum patients, there is still lack of evidence by longitudinal/follow-up studies. The present study included assessments of schizotypal traits and a wide range of cognitive functions at two time points (baseline and 4-years assessments) in order to examine (a) their stability over time, (b) the predictive value of baseline schizotypy on cognition at follow-up and (c) differences in cognition between the two time points in high negative schizotypal and control individuals. Only high negative schizotypal individuals were compared with controls due to the limited number of participants falling in the other schizotypal groups at follow-up. Seventy participants (mean age: 36.17; 70% females) were assessed at baseline and follow-up. Schizotypal traits were evaluated with the Schizotypal Personality Questionnaire. We found that schizotypal traits decreased over time, except in a sub-group of participants (“schizotypy congruent”) that includes individuals who consistently meet normative criteria of inclusion in either a schizotypal or control group. In these individuals, negative schizotypy and aspects of cognitive-perceptual and disorganized schizotypy remained stable. The stability of cognitive functioning also varied over time: response inhibition, aspects of cued attention switching, set-shifting and phonemic/semantic verbal fluency improved at follow-up. High negative schizotypy at baseline predicted poorer response inhibition and semantic switching at follow-up while high disorganized schizotypy predicted poorer semantic processing and complex processing speed/set-shifting. The between-group analyses revealed that response inhibition, set-shifting and complex processing speed/set-shifting were poorer in negative schizotypals compared with controls at both time points, while maintaining set and semantic switching were poorer only at follow-up. Taken together, the findings show differential stability of the schizotypal traits over time and indicate that different aspects of schizotypy predict a different pattern of neuropsychological task performance during a 4-years time window. These results are of significant use in the formulation of targeted early-intervention strategies for high-risk populations.

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Neuroimaging in Schizophrenia

Cited by 103 publications

References 90 publications

Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea

Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea

Heterogeneous trajectories in schizophrenia: insights from neurodevelopment and neuroprogression models

Cognitive Functioning and Schizotypy: A Four-Years Study

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