 et al., 2013;Sugranyes et al., 2011). Detrimental symptoms of schizophrenic patients have been attributed to altered anatomical and functional brain indices (Lawrie et al., 2001;Mathew et al., 2014;McDonald et al., 2005;Strasser et al., 2005;Tarcijonas and Sarpal, 2019). With regards to the differences in functionality, patients with schizophrenia have been shown to exhibit deficits in executive control and learning processes, coupled with increased and decreased activity in the prefrontal and anterior cingulate regions (e.g., (Glahn et al., 2005;Minzenberg et al., 2009)). Several caveats arise when investigating the brain correlates of medicated schizophrenic patients in an empirical setting. Firstly, episode and chronic developmental stages of schizophrenia have been shown to contribute to volumetric decreases within the cingulate, insular, prefrontal and temporal cortices, and the cerebellum. While untreated first-episode psychosis patients display thinning of prefrontal and temporal cortices in a functional connectivity study (Zhang et al., 2015), more extensive changes are displayed in schizophrenic patients who are exposed to long-term usage of antipsychotics Van Haren et al., 2011), supposedly by facilitating neuroplastic changes that develop new neural connections (Angelucci et al., 2000). In summary, the chronological stage of schizophrenia at which the brain is assessed may have a significant influence on the obtained results. A second caveat is medication usage, which may significantly contribute to brain activity in schizophrenic patients (Arsalidou et al., 2020). In a review article, it was suggested that while some brain regions become normalized from chronic medication usage, other regions may become denormalized (Abbott et al., 2013). In addition, two independent meta-analyses revealed overlapping regions when comparing neural volume before and after medication use (Leung et al., 2011) and regions associated with antipsychotic treatment (Bergé et al., 2014). However, as indicated by the former study, changes in grey matter across studies may be caused by a variety of reasons such as neuroleptic treatment, chronicity, and duration of illness (Leung et al., 2011).
To the best of our knowledge, a functional meta-analysis on medicated versus unmedicated schizophrenia patients has not been conducted. While multiple empirical studies have attempted to establish regions afflicted by unmedicated schizophrenia patients (Bergé et al., 2014;de la Fuente-Sandoval et al., 2010;Harrison et al., 2006;Torres et al., 2013), few have compared medicated with unmedicated groups directly (Van Snellenberg et al., 2016) or compared groups prescribed with typical versus atypical antipsychotic medication (Abbott et al., 2011;Kumari et al., 2015;2007;Tran et al., 1997). Due to the lack of articles directly comparing brain activity between medication and unmedicated individuals, it is challenging to assert functional activity to task-related processes, especially when medication has been shown to influence brain activation (C. Abbott et al., 2013;Van Snellenberg et al., 2016). Therefore, the meta-analysis approach may be beneficial to examine regions most likely to be affected by antipsychotic medication by comparing medicated and unmedicated schizophrenia patients across studies irrespective of task type. Our main goal is to perform separate meta-analyses on medicated and unmedicated schizophrenia patient groups to determine whether medication alters functional activity across studies. We further aim to isolate studies that examined specific types of medication taken by patients in the medication group (i.e. typical, atypical, anti-depressant), irrespective of studies that recruited patients prescribed with multiple medications (Dichter et al., 2010;Kumari et al., 2009;Song et al., 2017;Zedkova et al., 2006). As a supplementary analysis, we may also categorize studies into first-episode psychosis and chronic schizophrenia to determine regions associated with the stage of illness. Finally, we aim to explore illness duration and task type from each of the patient samples reporting relevant clusters from the main meta-analyses (medicated vs. unmedicated). By determining whether the clusters provided by medicated and unmedicated are specific to illness duration and task type we may shed light on any systematic patterns of activity across time from illness onset as well as systematic patterns of activity yielded any one task type. By adopting the perspective that drug-naïve schizophrenic patients serve as a baseline, brain activity map revealed by compiling and comparing medicated and unmedicated groups will demonstrate regions most likely to be affected by medication.
The approach to use meta-analysis across task-types may inevitably invite the study heterogeneity problem, which is central to understanding meta-analytic results. Contributors to this heterogeneity may include task paradigms, type of medication, and patient profiles, which may not be discernible from published results. Heterogeneity observed across studies could not be investigated by subgroup analyses or metaregression due to the limited number of available studies. In the end, precise answers to broad meta-analytic questions about medication effects may be difficult to achieve. However, meta-analyses addressing broadly framed questions may yield heterogeneous studies across tasktypes (Chein and Schneider, 2005;Li et al., 2015;Han and Ma, 2014;Wang et al., 2017). For example, to study age-related changes of fMRIderived activation, a recent meta-analysis compiled 114 fMRI studies on healthy aging across different types of tasks, including memory encoding, memory retrieval, and executive control. For this overall healthy aging meta-analysis, the researchers found that older adults showed decreased activation in the visual network and increased activation mainly in the frontoparietal and default-mode network. These findings provide novel evidence to support the view that declining sensory processing may underline cognitive aging which is associated with the frontoparietal compensation process and reduced suppression of the default-mode network. This study suggests that across task-types approach can be used to delineate age-related changes in activation across cognitive domains. A similar approach has successfully been used to study the overall effects of culture and oxytocin manipulation (Han and Ma, 2014) (Wang et al., 2017). Even for the same cognitive domain, there could be profound differences in experimental paradigms (Schurz et al., 2014) (Molenberghs et al., 2016). Even for the very specific task paradigms, e.g. strategic games, the task structures are also highly diverse, ranging from stag-hunt game, beauty contest game, to patent race game. Nevertheless, a consistent pattern emerges and the ALE results revealed consistent activation in the medial prefrontal cortex and bilateral temporoparietal junction across all theory of mind tasks and broader task parameters (Molenberghs et al., 2016;Schurz et al., 2014).
Here, we examined schizophrenia and medication related brain differences across different task domains. By compiling and performing these analyses we aim to determine: 1) neural activity most sensitive to schizophrenia if left untreated; 2) neural activity most likely influenced by medication usage; 3) neural activity most likely affected by atypical antipsychotic medication; 4) activity that may succumb to alterations as a result of chronic medication usage versus first-episode patients (refer to Supplementary Materials); 5) whether these clusters systematically derive from patients with high and low illness duration; and 6) whether these clusters systematically derive from any given task type. Since antipsychotic medication typically treats psychotic (positive) symptoms (Kane and Correll, 2010;Millan et al., 2012;Remington et al., 2016), which derive from parietal regions of the cerebral cortex (Konopaske et al., 2007), we hypothesize greater concordant activation across studies in the parietal cortex for the unmedicated group compared to the medicated group.

Literature search
Eligible articles were identified by searching in the Web of Science database (http://www.webofknowledge.com) on 18th June 2019. This was performed by combining a total of 14 searches continuously using the key terms: "fMRI" AND "schizophrenia", and varying key terms corresponding to each search: "unmedicated", "treatment-resistant", "anti-psychotic", "antipsychotic", "never medicated", "neuroleptic", "olanzapine", "risperidone", "Clozapine", "Aripiprazole", "Brexpiprazole", "Quetiapine", "Chlorpromazine", and "Haloperidol". These specific key terms were selected based on reiterations of searches that yielded the maximum number of articles with a minimum number of duplicates. After removing duplicates, a total of 448 articles were screened.

Data inclusion/exclusion criteria
The identified articles were deemed eligible if they: (i) included adult patients (no children or adolescents) were diagnosed using DSM or ICD criteria, as well as patient groups reported as schizophrenia, schizoaffective disorder, schizophreniform disorder, or or first episode psychosis; (ii) employed functional magnetic resonance imaging (fMRI) as the imaging modality; (iii) reported a within-subject contrast between an experimental task condition and a control condition, i.e. excluding resting-state fMRI contrast; (iv) applied whole-brain analyses (no region of interest [ROI] analyses were included); (v) reported neural activations in a standardized stereotaxic space (Talairach or Montreal Neurological Institute, MNI); and (vi) reported in English. Talairach coordinates were converted to the MNI coordinates using Brett's algorithm as implemented on the GingerALE software (http://brainmap. org/ale/). Fig. 1 displays a flowchart of how the screening procedure was conducted.
The final dataset included 20 articles for the unmedicated group and 37 articles for the medicated group. Further meta-analyses included 17 articles involving first-episode psychosis patients and 46 articles examining chronic