In this review, we explore the biomarkers of different psychiatric disorders, such as major depressive disorder, generalized anxiety disorder, schizophrenia, and bipolar disorder. Moreover, we show the interplay between genetic and environmental factors. Novel techniques such as genome-wide association studies (GWASs) have identified numerous risk loci and single-nucleotide polymorphisms (SNPs) implicated in these conditions, contributing to a better understanding of their mechanisms. Moreover, the impact of genetic variations on drug metabolisms, particularly through cytochrome P450 (CYP450) enzymes, highlights the importance of pharmacogenomics in optimizing psychiatric treatment. This review also explores the role of neurotransmitter regulation, immune system interactions, and metabolic pathways in psychiatric disorders. As the technology advances, integrating genetic markers into clinical practice will be crucial in advancing precision psychiatry, improving diagnostic accuracy and therapeutic interventions for individual patients.

After more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have struggled to provide psychiatry with reliable brain-imaging metrics. However, the emergence of computational psychiatry has paved a new path not only for understanding the psychopathology of mental illness but also to provide practical tools for clinical practice in terms of computational metrics, specifically computational phenotypes. However, these phenotypes still lack sufficient test-retest reliability. In this review, we describe recent works revealing that mind and brain-related computational phenotypes show structural (not random) variation over time, longitudinal changes. Furthermore, we show that these findings suggest that understanding the causes of these changes will improve the construct validity of the phenotypes with an ensuing increase in test-retest reliability. We propose that the active inference framework offers a general-purpose approach for causally understanding these longitudinal changes by incorporating brain imaging as observations within partially observable Markov decision processes.

As the histories of psychiatry, neurology, and neuroimaging interweave through time, psychiatry has only recently started to recognize the need to embrace neuroimaging like its sibling specialty. While imaging in psychiatric research is well accepted, there is current clinical utility as well. Standards for psychiatry residency and board certification in the USA and abroad have carved out a place for neuroimaging, but the implementation is variable and sparse in the USA. The few publications that describe neuroimaging teaching to psychiatrists have barriers to widespread adoption, and no comprehensive curricular solution has been developed. In this context, we describe some of the barriers and propose solutions to shape the future of neuroimaging education for psychiatrists.

Negative symptoms in the context of psychosis are still poorly understood and diagnosed, which impairs the treatment efficacy of current therapies and patient's integration in society. In this study, we aimed to test hypothesis-based and exploratory associations of negative symptom domains, as defined by the Brief Negative Symptom Scale (BNSS), with hormonal and hematological variables, and, complementarily, with standard psychological/cognitive and psychopathological measures. Fifty-one male patients diagnosed with a psychotic disorder underwent a structured interview and blood collection. Standard Spearmen bivariate correlations were used for data analysis. We obtained evidence of hypothesis-based associations between specific negative symptoms and oxytocin, thyroid stimulating hormone levels and neutrophil-to-lymphocyte ratio; as well as novel and hypothesis-free associations with erythrocyte and lymphocyte count, mean corpuscular volume and red cell distribution width. Complementarily, we also obtained some validation of previous associations of negative symptoms with illness resolution, cognitive symptom severity and social performance, and a novel association with anger contagion. We hope our results can generate new hypotheses in psychosis research. Our work suggests further avenues in research on erythrocytic, inflammatory, thyroid and oxytocin-related markers and abnormalities in psychosis, especially in regards to specific negative symptoms, towards more precise and comprehensive etiological, diagnostic and therapeutic models.

Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.

This educational review article aims to discuss growing evidence from PET studies in the diagnosis and treatment of depression. PET has been used in depression to explore the neurotransmitters involved, the alterations in neuroreceptors, non-neuroreceptor targets (e.g., microglia and astrocytes), the severity and duration of the disease, the pharmacodynamics of various antidepressants, and neurobiological mechanisms of non-pharmacological therapies like psychotherapy, electroconvulsive therapy, and deep brain stimulation therapy, by showing changes in brain metabolism and receptor and non-receptor targets. Studies have revealed alterations in neurotransmitter systems such as serotonin, dopamine, GABA, and glutamate, which are linked to the pathophysiology of depression. Overall, PET imaging has furthered the neurobiological understanding of depression. Despite these advancements, PET findings have not yet led to significant changes in evidence-based practices. Addressing the reasons behind inconsistencies in PET imaging results, conducting large sample size studies with a more standardized methodological approach, and investigating further the genetic and neurobiological aspects of depression may better leverage PET imaging in future studies.

Schizophrenia, a multifaceted mental disorder characterized by disturbances in thought, perception, and emotion, has been extensively investigated through resting-state fMRI, uncovering changes in spontaneous brain activity among those affected. However, a bibliometric examination regarding publication trends in resting-state fMRI studies related to schizophrenia is lacking. This study obtained relevant publications from the Web of Science Core Collection spanning the period from 1998 to 2022. Data extracted from these publications included information on countries/regions, institutions, authors, journals, and keywords. The collected data underwent analysis and visualization using VOSviewer software. The primary analyses included examination of international and institutional collaborations, authorship patterns, co-citation analyses of authors and journals, as well as exploration of keyword co-occurrence and temporal trend networks. A total of 859 publications were retrieved, indicating an overall growth trend from 1998 to 2022. China and the United States emerged as the leading contributors in both publication outputs and citations, with Central South University and the University of New Mexico being identified as the most productive institutions. Vince D. Calhoun had the highest number of publications and citation counts, while Karl J. Friston was recognized as the most influential author based on co-citations. Key journals such as Neuroimage, Schizophrenia Research, Schizophrenia Bulletin, and Biological Psychiatry played pivotal roles in advancing this field. Recent popular keywords included support vector machine, antipsychotic medication, transcranial magnetic stimulation, and related terms. This study systematically synthesizes the historical development, current status, and future trends in resting-state fMRI research in schizophrenia, offering valuable insights for future research directions.

Attention-deficit/hyperactivity disorder (ADHD) is often accompanied by executive function deficits and functional alterations in sensory integration. The present study aimed to investigate the relationship between ADHD core symptoms, executive function, and sensory integration in children with ADHD. A total of 228 children with ADHD were recruited for our study. The Sensory Organization Test (SOT) and Child Sensory Integration Scale (CSIS) evaluated the sensory integration ability from lab-based and scaled-based perspectives, respectively. Three core components of executive functions (inhibition, working memory, and set-shifting) were assessed using both lab-based tests and the relevant factors from the behavior rating inventory of executive function (BRIEF). Partial correlation analysis was performed to explore the correlation of sensory integration with EF and ADHD core symptoms. Based on the observed significant correlation, bootstrap analyses were further conducted to explore the potential mediating effect of EF on the relationship between sensory integration and ADHD core symptoms. ADHD symptoms and EF were significantly correlated with CSIS scores; no factors were significantly correlated with SOT performance. In detail, the vestibular-balance score was negatively correlated with both inattention and hyperactivity/impulsivity symptoms, while the hyper-sensory and proprioception scores were negatively correlated with only inattention symptoms. For the scaled-based EF, vestibular-balance was negatively correlated with inhibition and working memory, and the hyper-sensory score was negatively correlated with shift factor. No correlation was found for the lab-based EF tests. The subsequent mediation analysis found that inhibition partially mediated the relationship between vestibular balance and hyperactivity/impulsivity symptoms. Working memory completely mediated the relationship between vestibular-balance, hyper-sensory, proprioception, and inattention symptoms. These results were well validated in an independent sample. Our present findings demonstrated that the functional alteration in basic sensory integration might be associated with impairments of executive functions and then lead to the behavioral expression of ADHD. The present findings might provide a new perspective to understand the occurrence of ADHD symptoms and potential precise intervention methods.

Glioma patients have varying degrees of psychiatric symptoms, which severely affect the quality of life of patients and their families. The present study investigated the correlation between preoperative psychiatric symptoms and local cerebral perfusion parameters of in glioma patients.

The depression, anxiety, and cognitive impairment (CI) scores of 39 patients were assessed separately, and all of the patients underwent a preoperative perfusion computed tomography scan.

This study found that: (1) The incidence of preoperative symptoms of depression, anxiety, and CI was 46.15%, 48.72%, and 25.64%, respectively. (2) Cerebral blood volume (CBV) (lesion-sided [LS] occipital lobe white matter [WM] and parietal lobe WM and normal-sided temporal lobe WM), permeability surface (PS) (LS temporal lobe gray matter [GM] and parietal lobe WM) in the depression group were significantly decreased (p < .05). (3) CBV (LS occipital lobe WM), cerebral blood flow (LS parietal lobe GM, centrum ovale and frontal lobe WM and normal-sided frontal lobe WM, temporal lobe WM and parietal lobe WM), and mean transition time (MTT) (normal-sided frontal lobe WM and temporal lobe WM) in the anxiety group were significantly increased (p < .05). (4) CBV (LS temporal lobe GM), MTT (LS anterior limb of internal capsule), and PS (LS thalamus) in the CI group were significantly increased (p < .05).

This study showed that glioma patients had different levels of psychological distress in glioma patients before surgery, which may be related to the changes in brain perfusion caused by the tumor.

Aberrant patterns of cognition, perception, and behavior seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between spatial and temporal resolutions inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography, often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illnesses such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications that seek to drive a mechanistic understanding of psychopathology and the realization of preclinical translation.

The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term "explainability" in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability. These concepts are easier to articulate, to "ground" in our understanding of how algorithms and models operate and are used more consistently in the literature. We describe the TIFU (Transparency and Interpretability For Understandability) framework and examine how this applies to the landscape of AI/ML in mental health research. We argue that the need for understandablity is heightened in psychiatry because data describing the syndromes, outcomes, disorders and signs/symptoms possess probabilistic relationships to each other-as do the tentative aetiologies and multifactorial social- and psychological-determinants of disorders. If we develop and deploy AI/ML models, ensuring human understandability of the inputs, processes and outputs of these models is essential to develop trustworthy systems fit for deployment.

Brain perfusion single photon emission computed tomography (SPECT) scans were initially developed in 1970's. A key radiopharmaceutical, hexamethylpropyleneamine oxime (HMPAO), was originally approved in 1988, but was unstable. As a result, the quality of SPECT images varied greatly based on technique until 1993, when a method of stabilizing HMPAO was developed. In addition, most SPECT perfusion studies pre-1996 were performed on single-head gamma cameras. In 1996, the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology (TTASAAN) issued a report regarding the use of SPECT in the evaluation of neurological disorders. Although the TTASAAN report was published in January 1996, it was approved for publication in October 1994. Consequently, the reported brain SPECT studies relied upon to derive the conclusions of the TTASAAN report largely pre-date the introduction of stabilized HMPAO. While only 12% of the studies on traumatic brain injury (TBI) in the TTASAAN report utilized stable tracers and multi-head cameras, 69 subsequent studies with more than 23,000 subjects describe the utility of perfusion SPECT scans in the evaluation of TBI. Similarly, dementia SPECT imaging has improved. Modern SPECT utilizing multi-headed gamma cameras and quantitative analysis has a sensitivity of 86% and a specificity of 89% for the diagnosis of mild to moderate Alzheimer's disease-comparable to fluorodeoxyglucose positron emission tomography. Advances also have occurred in seizure neuroimaging. Lastly, developments in SPECT imaging of neurotoxicity and neuropsychiatric disorders have been striking. At the 25-year anniversary of the publication of the TTASAAN report, it is time to re-examine the utility of perfusion SPECT brain imaging. Herein, we review studies cited by the TTASAAN report vs. current brain SPECT imaging research literature for the major indications addressed in the report, as well as for emerging indications. In Part II, we elaborate technical aspects of SPECT neuroimaging and discuss scan interpretation for the clinician.

Psychiatry remains the only medical specialty where diagnoses are still based on clinical syndromes rather than measurable biological abnormalities. As imaging technology and analytical methods evolve, it is becoming clear that subtle but measurable radiological characteristics exist and can be used to experimentally classify psychiatric disorders, predict response to treatment and, hopefully, develop new, more effective therapies. This review highlights advances in neuroimaging modalities that are now allowing assessment of brain structure, connectivity and neural network function, describes technical aspects of the most promising methods, and summarizes observations made in some frequent psychiatric disorders.

In the community, there is a need to more objectively evaluate the response of common chronic psychiatric disorders to treatment. Brain single photon emission computed tomography (SPECT) indirectly measures cerebral functional activity by uptake of a radiotracer, which follows regional cerebral blood flow. Brain 3D Thresholded SPECT scans are thresholded three dimensional images derived from brain SPECT data. A retrospective community study of longitudinal (before and after treatment) brain 3D Thresholded SPECT scans of 73 patients with all-cause psychiatric disorders (most frequent diagnostic clusters: attention-deficit hyperactivity disorder, post-mild traumatic brain injury, affective disorders, psychotic disorders, post-viral chronic syndromes), shows these baseline SPECT scans predict improvement (non-worsening to large improvement) in clinical functioning with a sensitivity of 94% (95% confidence interval 86-98%) and a specificity of 67% (95% confidence interval 21-94%). In contrast, contemporaneous analysis by the same radiologist of conventional 2D reading of the same before and after treatment brain SPECT scan data of the same 73 patients, predicted improvement (non-worsening to large improvement) in clinical functioning with a sensitivity of only 26% (95% confidence interval 17-37%) although with a specificity of 100% (95% confidence interval 44-100%). These data suggest 3D Thresholded SPECT scans can provide the clinician with a more objective measure for verifying improvement in psychiatric disorders seen in the community, consistent with prior studies of SPECT as a measure of neurobiological change. Furthermore, these data suggest 3D Thresholded SPECT scans may have clinical application in guiding treatment and potentially improving outcomes.

The difficulties of evaluating patients with complex neuropsychiatric conditions and prescribing appropriate treatments are well known. Imaging complements clinical assessments and allows a clinician to narrow the differential diagnosis by facilitating accurate and efficient evaluation. This is particularly relevant to neuropsychiatric conditions that are often diagnosed using a trial-and error process of exclusion. Single Photon Emission Computed Tomography (SPECT) is a functional brain imaging procedure that allows practitioners to measure the functional changes of gray matter structures based on regional cerebral blood flow (rCBF). The accurate diagnosis and treatment selection in psychiatry is challenging due to complex cases and frequent comorbidities. However, such complex neuropsychiatric conditions are increasingly benefitting from new treatment approaches, in addition to established medications. Among these are combination transcranial magnetic stimulation with ketamine infusions (CTK), hyperbaric oxygen therapy (HBOT) and perispinal administration of etanercept (PSE). This article provides readers with six case study examples that demonstrate how brain SPECT imaging can be used, both as a diagnostic tool, and as a potential biomarker for monitoring and evaluating novel treatments for patients with complex neuropsychiatric conditions. Six patients were assessed in our clinic and baseline brain SPECT imagesTourettes and a long history of alcohol were visually compared with SPECT images collected after periods of treatment with CTK or HBOT followed by PSE. This retrospective review demonstrates the clinical utility of these novel treatments and describes how SPECT imaging can complement standard diagnostic assessments. A novel display technique for SPECT images is described and we argue that SPECT imaging can be used for monitoring biomarker for clinical change.

Bipolar disorder is a significant mental illness affecting over 4 million people in North America and approximately 46 million worldwide. While the onset of bipolar disorder is typically in late adolescence and early adulthood, the correct diagnosis can be delayed for several years. This delay can result in inappropriate pharmaceutical interventions, loss of career or productivity, suicide, family hardship, and unnecessary expense. Moreover, prolonged untreated or inappropriately treated bipolar disorder may cause damage to the brain. Early diagnosis is a critical need to circumvent the damage, suffering, and expense caused by the current delay. Brain perfusion single photon emission computed tomography (SPECT) neuroimaging reveals visual correlates of brain function. Herein, a family cohort all with bipolar disorder is described and their symptoms correlated with findings on the individual SPECT brain scans. The family consisted of two parents and three children (one female). The scans were interpreted by a panel of experts. Then a post hoc region-of-interest (ROI) analysis was conducted on SPECT data normalized to the cerebellum maximum with comparison to similarly normalized data from a normative sample. These findings support two distinct patterns of SPECT perfusion scan changes that can be found in individuals with bipolar disorder. In addition, these findings indicate that SPECT scan findings may be predictive of individual risk for progressing to symptomatic bipolar disorder. While preliminary, the findings in this cohort support the need for larger, diverse cohort studies of bipolar and control subjects to assess the predictive value of these particular SPECT perfusion findings in bipolar disorder.

Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder with characteristics such as lack of concentration, excessive fidgeting, outbursts of emotions, lack of patience, difficulty in organizing tasks, increased forgetfulness, and interrupting conversation, and it is affecting millions of people worldwide. There is, until now, not a gold standard test using which an ADHD expert can differentiate between an individual with ADHD and a healthy subject, making accurate diagnosis of ADHD a challenging task. We are proposing a Knowledge Distillation-based approach to search for discriminating features between the ADHD and healthy subjects. Learned embeddings from a large neural network, trained on the functional connectivity features, were fed to one hidden layer Autoencoder for reproduction of the embeddings using the same connectivity features. Finally, a forward feature selection algorithm was used to select a combination of most discriminating features between the ADHD and the Healthy Controls. We achieved promising classification results for each of the five individual sites. A combined accuracy of 81% in KKI, 60% Peking, 56% in NYU, 64% NI, and 56% OHSU and individual site wise accuracy of 72% in KKI, 60% Peking, 73% in NYU, 70% NI, and 71% OHSU were obtained using our extracted features. Our results also outperformed state-of-the-art methods in literature which validates the efficacy of our proposed approach.

Traditional therapeutic methods in psychiatry, such as psychopharmacology and psychotherapy help many people suffering from mental disorders, but in the long-term prove to be effective in a relatively small proportion of those affected. Therapeutically, resistant forms of mental disorders such as schizophrenia, major depressive disorder, and bipolar disorder lead to persistent distress and dysfunction in personal, social, and professional aspects. In an effort to address these problems, the translational approach in neuroscience has initiated the inclusion of novel or modified unconventional diagnostic and therapeutic techniques with promising results. For instance, neuroimaging data sets from multiple modalities provide insight into the nature of pathophysiological mechanisms such as disruptions of connectivity, integration, and segregation of neural networks, focusing on the treatment of mental disorders through instrumental biomedical methods such as electro-convulsive therapy (ECT), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS) and deep brain stimulation (DBS). These methodologies have yielded promising results that have yet to be understood and improved to enhance the prognosis of the severe and persistent psychotic and affective disorders. The current review is focused on the translational approach in the management of schizophrenia and mood disorders, as well as the adaptation of new transdisciplinary diagnostic tools such as neuroimaging with concurrently administered psychopathological questionnaires and integration of the results into the therapeutic framework using various advanced instrumental biomedical tools such as ECT, TMS, tDCS and DBS.

Depressive symptoms are reported by 20% of the population and are related to altered functional integrity of large-scale brain networks. The link between moment-to-moment brain function and depressive symptomatology, and the implications of these relationships for clinical and community populations alike, remain understudied. The present study examined relationships between functional brain dynamics and subclinical-to-mild depressive symptomatology in a large community sample of adults with and without psychiatric diagnoses. This study used data made available through the Enhanced Nathan Kline Institute-Rockland Sample; 445 participants between 18 and 65 years of age completed a 10-min resting-state functional MRI scan. Coactivation pattern analysis was used to examine the dimensional relationship between depressive symptoms and whole-brain states. Elevated levels of depressive symptoms were associated with increased frequency and dwell time of the default mode network, a brain network associated with self-referential thought, evaluative judgment, and social cognition. Furthermore, increased depressive symptom severity was associated with less frequent occurrences of a hybrid brain network implicated in cognitive control and goal-directed behavior, which may impair the inhibition of negative thinking patterns in depressed individuals. These findings demonstrate how temporally dynamic techniques offer novel insights into time-varying neural processes underlying subclinical and clinically meaningful depressive symptomatology.

Neurotherapies for diagnostics and treatment-such as electroencephalography (EEG) neurofeedback, single-photon emission computerized tomography (SPECT) imaging for neuropsychiatric evaluation, and off-label/experimental uses of brain stimulation-are continuously being offered to the public outside mainstream healthcare settings. Because these neurotherapies share many key features of complementary and alternative medicine (CAM) techniques-and meet the definition of CAM as set out in Kaptchuk and Eisenberg-here we refer to them as "alternative neurotherapies." By explicitly linking these alternative neurotherapy practices under a common conceptual framework, this paper draws attention to, and critically considers, the cross-cutting ethical and legal issues related to the provision of these services. The first section of this paper provides an updated empirical overview of uses of SPECT neuropsychiatric evaluations, EEG neurofeedback, and experimental/off-label forms of brain stimulation. Next, drawing on CAM bioethics scholarship, we highlight the pertinent ethical issues in the alternative neurotherapy context, including the truthful representation of evidence base, marketing to vulnerable populations, potential harms, provider competency, and conflicts of interest. Finally, we consider the principal legal issues at stake for the provision of alternative neurotherapies in the U.S., namely those related to licensing and scope-of-practice considerations. We conclude with recommendations for future research in this domain.

Resting-state functional magnetic resonance imaging (R-fMRI) studies of the neural correlates of medication treatment in attention-deficit/hyperactivity disorder (ADHD) have not been systematically reviewed. Our objective was to systematically identify, assess and summarize within-subject R-fMRI studies of pharmacological-induced changes in patients with ADHD. We critically appraised strengths and limitations, and provide recommendations for future research.

Systematic review of published original reports in English meeting criteria in pediatric and adult patients with ADHD up to July 1, 2020. A thorough search preceded selection of studies matching prespecified criteria. Strengths and limitations of selected studies, regarding design and reporting, were identified based on current best practices.

We identified and reviewed 9 studies (5 pediatric and 4 adult studies). Sample sizes were small-medium (16-38 patients), and included few female participants. Medications were methylphenidate, amphetamines, and atomoxetine. Wide heterogeneity was observed in designs, analyses and results, which could not be combined quantitatively. Qualitatively, the multiplicity of brain regions and networks identified, some of which correlated with clinical improvements, do not support a coherent mechanistic hypothesis of medication effects. Overall, reports did not meet current standards to ensure reproducibility.

In this emerging field, the few studies using R-fMRI to analyze the neural correlates of medications in patients with ADHD suggest a potential modulatory effect of stimulants and atomoxetine on several intrinsic brain activity metrics. However, methodological heterogeneity and reporting issues need to be addressed in future research to validate findings which may contribute to clinical care. Such a goal is not yet at hand.

Over the last three decades, the brain's functional and structural imaging has become more prevalent in psychiatric research and clinical application. A substantial amount of psychiatric research is based on neuroimaging studies that aim to illuminate neural mechanisms underlying psychiatric disorders. Single-photon emission computed tomography (SPECT) is one of those developing brain imaging techniques among various neuroimaging technologies. Compared to PET, SPECT imaging is easy, less expensive, and practical for radioligand use. Current technologies increased the spatial accuracy of SPECT findings by combining the functional SPECT images with CT images. The radioligands bind to receptors such as 5-hydroxytryptamine 2A, and dopamine transporters can help us comprehend neural mechanisms of psychiatric disorders based on neurochemicals. This mini-review focuses on the SPECT-based neuroimaging approach to psychiatric disorders such as schizophrenia and major depressive disorder (MDD). Research-based SPECT findings of psychiatric disorders indicate that there are notable changes in biochemical components in certain disorders. Even though many studies support that SPECT can be used in psychiatric clinical practice, we still only use subjective diagnostic criteria such as the Diagnostic Statistical Manual of Mental Disorders (DSM-5). Glimpsing into the brain's biochemical world via SPECT in psychiatric disorders provides more information about the pathophysiology and future implication of neuroimaging techniques.