fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review

Abstract

Background: Antipsychotic drugs are the first-choice therapy for psychotic episodes, but antipsychotic treatment response (AP-R) is unpredictable and only becomes clear after weeks of therapy. A biomarker for AP-R is currently unavailable. We reviewed the evidence for the hypothesis that functional magnetic resonance imaging functional connectivity (fMRI-FC) is a predictor of AP-R or could serve as a biomarker for AP-R in psychosis.

Method: A systematic review of longitudinal fMRI studies examining the predictive performance and relationship between FC and AP-R was performed following PRISMA guidelines. Technical and clinical aspects were critically assessed for the retrieved studies. We addressed three questions: Q1) is baseline fMRI-FC related to subsequent AP-R; Q2) is AP-R related to a change in fMRI-FC; and Q3) can baseline fMRI-FC predict subsequent AP-R?

Results: In total, 28 articles were included. Most studies were of good quality. fMRI-FC analysis pipelines included seed-based-, independent component- / canonical correlation analysis, network-based statistics, and graph-theoretical approaches. We found high heterogeneity in methodological approaches and results. For Q1 (N = 17) and Q2 (N = 18), the most consistent evidence was found for FC between the striatum and ventral attention network as a potential biomarker of AP-R. For Q3 (N = 9) accuracy's varied form 50 till 93%, and prediction models were based on FC between various brain regions.

Conclusion: The current fMRI-FC literature on AP-R is hampered by heterogeneity of methodological approaches. Methodological uniformity and further improvement of the reliability and validity of fMRI connectivity analysis is needed before fMRI-FC analysis can have a place in clinical applications of antipsychotic treatment.

Keywords: Antipsychotic response; Functional connectivity; Prediction; Psychosis; fMRI.

Fig. 1

Research questions Overview research questions. Q1: Baseline FC related to AP-R, Q2: FC change over time related to AP-R, Q3: FC predicting AP-R, by using machine learning techniques.

Fig. 2

Overview ROB assessment score per study A: Clinical parameters, B: fMRI methodological parameters. On the X-axis the first author and year of the different studies. On the y-axis the total points given to the studies are shown. We considered the study Nelson et al. (Nelson et al., 2022) (nelson a and b) as one study for the ROB tool.

Fig. 3

Overview of significant baseline FC + connections related to AP-R (Q1), indicating that higher baseline FC for these connections was associated with AP-R. The regions of each significant connection were divided in RSN, shown in corresponding color. If connections existed of more than two brain localizations, they were summarized into clusters If it was not possible to categorize regions of a significant connection in the RSN framework, it was assigned to the NA group. *Connection with itself. For an overview of the clusters see supplementary table S7. Abbreviations: DMN: default mode network, FN: frontoparietal network, LN: limbic network, NA: not applicable, SN: sensorimotor network, VAN: ventral attention network, VN: visual network, ACC: anterior cingulate cortex, l: left, PCC: posterior cingulate cortex, r: right, VTA: ventral tegmental area.

Fig. 4

Overview of significant baseline FC- connections related to AP-R, indicating that lower baseline FC for these connections was associated with AP-R. Each significant connection is divided in RSN, shown in corresponding color. If connections existed of more than two brain localizations, they were summarized into clusters. If it was not possible to categorize regions of a significant connection in the RSN framework, it was assigned to the NA group. For an overview of the clusters see supplementary table S7. *Connection with itself Abbreviations: DMN: default mode network, FN: frontoparietal network, LN: limbic network, NA: Not applicable, SN: sensorimotor network, VAN: ventral attention network, VN: visual network, ACC: anterior cingulate cortex, l: left, PCC: posterior cingulate cortex, r: right, VTA: ventral tegmental area.

Fig. 5

Overview of longitudinal FC increase changed connections related to AP-R. Each significant connection is divided in RSN, shown in corresponding color. If it was not possible to categorize regions of a significant connection in the RSN framework, it was assigned to the NA group. *Connection with itselfAbbreviations: DMN: default mode network, FN: frontoparietal network, LN: limbic network, NA: Not applicable, SN: sensorimotor network, VAN: ventral attention network, VN: visual network, DAN: dorsal attention network, ACC: anterior cingulate cortex, l: left, PCC: posterior cingulate cortex, r: right, VTA: ventral tegmental area.

Fig. 6

Overview of longitudinal FC decrease changed connections related to AP-R. Each significant connection is divided in RSN, shown in corresponding color. If it was not possible to categorize regions of a significant connection in the RSN framework, it was assigned to the NA group. *Connection with itselfAbbreviations: DMN: default mode network, FN: frontoparietal network, LN: limbic network, NA: Not applicable, SN: sensorimotor network, VAN: ventral attention network, VN: visual network, DAN: dorsal attention network, ACC: anterior cingulate cortex, l: left, PCC: posterior cingulate cortex, r: right, VTA: ventral tegmental area.